JP7497119B2 - Positioning device - Google Patents

Description

This invention relates to a positioning device, and in particular to a technology for receiving ranging signals transmitted from satellites of a global navigation satellite system and performing relative positioning using observation data from an observation station and observation data from a reference station.

The Global Navigation Satellite System (GNSS), which includes GPS (Global Positioning System), is a system that calculates the position of a receiving point using radio waves (specifically, ranging signals) transmitted from satellites. A positioning device that uses the Global Positioning Satellite System to measure position and direction measures its own position and direction based on the signals transmitted from multiple satellites.

A known conventional technology for performing relative positioning using GNSS is a positioning system that has multiple positioning means that perform positioning using positioning information transmitted from multiple satellites in the same combination, and determines the distance between the multiple positioning means by taking the difference between the positioning results from the multiple positioning means (see Patent Document 1).

Japanese Patent Application Laid-Open No. 11-287850

When using a GNSS receiver to perform positioning, for example by applying a Kalman filter, it is necessary to construct a state update model. This state update model generally expresses the pre-estimated position as the previously determined position and the estimated velocity, generates and adds system noise and observation noise in the state model, and calculates the position by solving the positioning algorithm.

System noise is generally composed of parameters that model noise for position and speed. However, because it is necessary to provide noise that takes into account changes in the positioning environment, there is a problem in that positioning accuracy will decrease if parameters that correspond to changes in the positioning environment are not applied. In addition, when estimating the speed amount using a different process (for example, when using a different positioning model or an inertial navigation system), it is difficult to appropriately provide errors in the speed amount. For this reason, when estimating a highly accurate position such as centimeter-level positioning (for example, relative positioning), it is necessary to appropriately reflect changes in the positioning environment and the reliability of the model that estimates the speed amount in the positioning process.

The objective of this invention is to provide a positioning device that can estimate a highly accurate position by appropriately reflecting changes in the positioning environment and the reliability of the speed amount in the positioning process.

In order to solve the above problem, the invention described in claim 1 is a positioning device in which a reference station and a receiver receive ranging signals transmitted from multiple satellites and calculate the relative position of the receiver with respect to the reference station, and the positioning device has a first positioning unit that performs single point positioning applying the least squares method and single point positioning applying a Kalman filter using the ranging signals received by the receiver, and a second positioning unit that performs relative positioning using the ranging signals received by the receiver and the ranging signals received by the reference station, and is characterized in that the second positioning unit performs the relative positioning using at least one of the following output from the first positioning unit: the speed of the receiver calculated by the single point positioning applying the Kalman filter, the difference between the position of the receiver calculated by the single point positioning applying the least squares method and the position of the receiver calculated by the single point positioning applying the Kalman filter, and the distance between the position of the receiver calculated by the single point positioning applying the Kalman filter and the position of the satellite.

The invention described in claim 2 is characterized in that, in the positioning device described in claim 1, the first positioning unit includes a KF independent positioning unit that calculates the speed of the receiver by the independent positioning to which the Kalman filter is applied, and the second positioning unit includes a pre-estimated position update unit that updates the three-dimensional position of the receiver of the pre-estimated state quantity of the Kalman filter by adding the moving distance of the receiver calculated using the speed.

The invention described in claim 3 is characterized in that, in the positioning device described in claim 1 or 2, the first positioning unit includes a position difference calculation unit that calculates the difference between the position of the receiver calculated by the single point positioning to which the least squares method is applied and the position of the receiver calculated by the single point positioning to which the Kalman filter is applied, and the second positioning unit includes a system noise update unit that updates the system noise covariance matrix of the Kalman filter by adding the difference to the system noise covariance matrix of the Kalman filter when the absolute value of the difference is equal to or greater than a predetermined threshold value.

The invention described in claim 4 is characterized in that, in the positioning device described in claims 1 to 3, the first positioning unit includes a position difference calculation unit that calculates the difference between the position of the receiver calculated by the single point positioning to which the least squares method is applied and the position of the receiver calculated by the single point positioning to which the Kalman filter is applied, and the second positioning unit includes an observation noise update unit that updates the observation noise covariance matrix of the Kalman filter by multiplying the observation noise covariance matrix of the Kalman filter by a predetermined amplification coefficient when the absolute value of the difference is equal to or greater than a predetermined threshold.

The invention described in claim 5 is characterized in that, in the positioning device described in claims 1 to 3, the first positioning unit includes a pseudorange residual calculation unit that calculates the difference between a code pseudorange based on the position of the receiver and the position of the satellite calculated by the single positioning to which the Kalman filter is applied and a code observation based on the ranging signal for each of the carrier waves of the multiple satellites, and the second positioning unit includes an observation noise update unit that updates the observation noise covariance matrix of the Kalman filter by multiplying the observation noise covariance matrix of the Kalman filter by a predetermined amplification coefficient when the average value of the absolute value of the difference calculated for each of the carrier waves of the multiple satellites is equal to or greater than a predetermined threshold value.

The invention described in claim 6 is a positioning device according to claims 1 to 3, characterized in that the first positioning unit includes a position difference calculation unit that calculates a position difference between the position of the receiver calculated by the point-based positioning to which the least squares method is applied and the position of the receiver calculated by the point-based positioning to which the Kalman filter is applied, and a pseudorange residual calculation unit that calculates a distance difference between a code pseudorange based on the position of the receiver and the position of the satellite calculated by the point-based positioning to which the Kalman filter is applied and a code observable based on the ranging signal for each of the carrier waves of the multiple satellites, and the second positioning unit includes an observation noise update unit that updates the observation noise covariance matrix of the Kalman filter by multiplying the observation noise covariance matrix of the Kalman filter by a predetermined amplification coefficient when the absolute value of the position difference is equal to or greater than a predetermined threshold and the average value of the absolute values of the distance differences calculated for each of the carrier waves of the multiple satellites is equal to or greater than a predetermined threshold.

According to the invention described in claims 1 to 6, the second positioning unit performs relative positioning using information output from the first positioning unit that performs independent positioning, so that changes in the positioning environment and the reliability of the speed amount can be appropriately reflected in the positioning process, making it possible to estimate the positioning position with high accuracy.

1 is a functional block diagram showing a schematic configuration of a GNSS receiver including a positioning device according to an embodiment of the present invention. FIG. 2 is a flow diagram showing a processing procedure in the GNSS receiver of FIG. 1 . 3 is a flowchart showing a processing procedure in a prior estimated position updating unit and a system noise updating unit, and is a flowchart showing details of the processing in step S9 in the flowchart of FIG. 2. FIG. 3 is a flowchart showing a processing procedure in an observation noise updating unit, and is a flowchart showing details of the processing in step S10 in the flowchart of FIG. 2.

The present invention will now be described with reference to the illustrated embodiment. FIG. 1 is a functional block diagram showing a schematic configuration of a GNSS receiver 1 including a positioning device 8 according to an embodiment of the present invention. FIG. 2 is a flow diagram showing a processing procedure in the GNSS receiver 1 according to the embodiment. The processing procedure shown in the flow diagram of FIG. 2 is mainly the processing content executed by the positioning device 8 according to, for example, a program, and is repeatedly executed at a predetermined time interval (for example, one second intervals).

The GNSS receiver 1 is a mechanism for acquiring position information using the Global Navigation Satellite System (GNSS). The GNSS receiver 1 has a GNSS antenna 2, a GNSS receiving unit 3, a data communication antenna 4, a data receiving unit 5, a satellite information acquiring unit 6, a reference station information acquiring unit 7, and a positioning device 8. The GNSS receiver 1 calculates and outputs its own relative position with respect to other GNSS receivers.

The other GNSS receivers mentioned above are positioned as "reference stations" in the calculation of relative position (i.e., relative positioning). The reference stations are fixedly installed and their installation positions are determined in advance (i.e., their installation positions are known).

The GNSS antenna 2 receives radio waves (ranging signals; specifically, signals in which a PRN (pseudo-random noise) code and a navigation message are superimposed on a carrier wave (i.e., a carrier) of a specific frequency) transmitted from each of a number of satellites (e.g., GPS satellites) and transfers them to the GNSS receiver 3.

Here, the multiple satellites that make up the global positioning satellite system and transmit radio waves (ranging signals) received by the GNSS receiver 1 and the reference station are referred to as "multiple satellites Sx." When the multiple satellites Sx are to be distinguished from one another (in other words, when they are meant to be mutually different satellites), the subscript x is changed. Specifically, for example, "satellite Sj" and "satellite Sk" indicate that they are mutually different satellites.

The GNSS receiver 3 performs a predetermined process on the ranging signal transferred from the GNSS antenna 2 and outputs a signal in a format that can be used by the satellite information acquisition unit 6. For example, the GNSS receiver 3 converts the frequency of the ranging signal transferred from the GNSS antenna 2 to an intermediate frequency and performs analog-to-digital conversion to generate a digital signal, and performs demodulation processing on the digital signal to output the demodulated ranging signal (step S1).

The data communication antenna 4 receives data transmitted from the reference station and transfers it to the data receiving unit 5. Signals are sent and received between the GNSS receiver 1 and the reference station according to a predetermined format.

The data transmitted from the reference station includes radio waves (ranging signals; specifically, signals in which a PRN (pseudo-random noise) code and a navigation message are superimposed on a carrier wave (i.e., a carrier) of a predetermined frequency) transmitted from each of the multiple satellites Sx and received by the reference station via the GNSS antenna. The data transmitted from the reference station and received by the data communication antenna 4 of the GNSS receiver 1, including the ranging signals for each of the multiple satellites Sx received by the reference station, is called "reference station satellite information". The reference station satellite information is transmitted from the reference station at predetermined intervals and supplied to the GNSS receiver 1 (step S2).

The data receiving unit 5 performs a predetermined process on the data (i.e., reference station satellite information) transferred from the data communication antenna 4, and outputs a signal in a format that can be used by the reference station information acquiring unit 7.

The satellite information acquisition unit 6, the reference station information acquisition unit 7, and the positioning device 8 are configured in a computing unit that includes, for example, a CPU (short for Central Processing Unit), memory, and input/output ports, and the CPU executes the necessary processing by referencing the control programs and various data stored in the memory.

The satellite information acquisition unit 6 uses the ranging signal output from the GNSS receiving unit 3 at each predetermined period, and performs conventionally known arithmetic processing on the ranging signal as necessary to acquire, for each of the multiple satellites Sx, information about the ranging signal and the satellite that transmitted the ranging signal (referred to as "observation station observation data") (step S3).

For each of the multiple satellites Sx, the satellite information acquisition unit 6 acquires, as observation station observation data, for example, the phase of the ranging signal received via the GNSS antenna 2 (in other words, the carrier phase), the pseudo-range (called the "code pseudo-range") between the satellite that transmitted the ranging signal and the GNSS receiver 1 (particularly the GNSS antenna 2), and the three-dimensional position (x, y, z) of the satellite that transmitted the ranging signal.

The reference station information acquisition unit 7 uses information related to the ranging signal received by the reference station, which is included in the reference station satellite information output from the data receiving unit 5, at each predetermined period, and performs conventionally known arithmetic processing on the ranging signal as necessary to acquire information (referred to as "reference station observation data") related to the ranging signal received by the reference station and the satellite that transmitted the ranging signal for each of the multiple satellites Sx (step S4).

For each of the multiple satellites Sx from which the reference station receives radio waves (ranging signals), the reference station information acquisition unit 7 acquires, as reference station observation data, the phase of the ranging signal received by the reference station (in other words, the carrier phase), the pseudo-distance (called the "code pseudo-distance") between the satellite that transmitted the ranging signal and the reference station (particularly the GNSS antenna), and the three-dimensional position (x, y, z) of the satellite that transmitted the ranging signal.

The above observation station observation data and reference station observation data are synchronized for necessary items using GPS time, etc., as necessary.

In addition, some or all of the above reference station observation data may be calculated at the reference station, included in the reference station satellite information, and transmitted from the reference station to the GNSS receiver 1.

The positioning device 8 is a mechanism for calculating the relative position between the GNSS receiver 1 (in other words, the observation station) including the positioning device 8 and the reference station (i.e., the relative position of the observation station with respect to the reference station) based on ranging signals transmitted from multiple satellites Sx, and has a first positioning unit 81 and a second positioning unit 82.

The positioning device 8 according to this embodiment is a mechanism for the reference station and the GNSS receiver 1 to receive ranging signals transmitted from multiple satellites Sx and calculate the relative position of the GNSS receiver 1 with respect to the reference station, and includes a first positioning unit 81 that performs independent positioning applying the least squares method and independent positioning applying a Kalman filter using the ranging signals received by the GNSS receiver 1, and a second positioning unit 82 that performs relative positioning using the ranging signals received by the GNSS receiver 1 (specifically, observation station observation data) and the ranging signals received by the reference station (specifically, reference station observation data). and a second positioning unit 82 that performs relative positioning using the speed of the GNSS receiver 1 calculated by single point positioning applying a Kalman filter, the difference between the position of the GNSS receiver 1 calculated by single point positioning applying a least squares method and the position of the GNSS receiver 1 calculated by single point positioning applying a Kalman filter, and the distance between the position of the GNSS receiver 1 calculated by single point positioning applying a Kalman filter and the position of the satellite, which are output from the first positioning unit 81.

The first positioning unit 81 is a mechanism for performing independent positioning and generating information to be provided to the positioning process in the second positioning unit 82, and includes a least-squares positioning unit 811, a KF independent positioning unit 812, a position difference calculation unit 813, and a pseudorange residual calculation unit 814.

The least squares positioning unit 811 performs positioning processing (specifically, independent positioning) applying the least squares method using the radio waves (ranging signals) received by the GNSS receiver 1 (step S5).

Positioning processing using the least squares method (specifically, single point positioning) is a well-known technique, so a detailed explanation will be omitted here, but in outline, it is a process that calculates the three-dimensional position of the GNSS receiver 1 by solving a system of simultaneous equations based on the code pseudoranges between the multiple satellites Sx and the GNSS receiver 1 through iterative calculations in an approximate estimation manner, with the three-dimensional position of each satellite of the multiple satellites Sx being the known variables and the three-dimensional position and clock error of the GNSS receiver 1 being the unknown variables.

The least squares positioning unit 811 outputs the three-dimensional position of the GNSS receiver 1 (called the "least squares positioning position") calculated by a positioning process that applies the least squares method (specifically, single-unit positioning).

The KF independent positioning unit 812 performs positioning processing (specifically, independent positioning) by applying a Kalman filter using the radio waves (ranging signals) received by the GNSS receiver 1 (step S6).

Here, the positioning equation used in the process of step S6 is expressed as the following Equation 1 (note that the subscript i indicates independent positioning).
(Equation 1) yi = Hi xi + ε

In Equation 1, yi is an observation vector whose elements are the three-dimensional position of each satellite and the code pseudorange between each satellite and the GNSS receiver 1, Hi is a planning matrix, and furthermore, xi is an unknown vector whose elements are the three-dimensional position (unknown) and velocity (unknown) of the GNSS receiver 1. Also, ε is a noise term.

The KF independent positioning unit 812 finds the unknown vector x i in equation 1 (specifically, the elements of which are the three-dimensional position vector and three-dimensional velocity vector of the GNSS receiver 1) by applying a Kalman filter (see equation 2 below).

The variables in Equation 2 are as follows (note that "x^" indicates that there is a ^ directly above x):
n: Time x^(n|n-1): Estimated time at time n using information up to time (n-1)
Prior estimated state quantity x^(n|n): The state quantity at time n estimated using information up to time n
Posterior estimated state quantity y(n): Observation value F(n): State transition matrix M(n): Observation matrix K(n): Kalman gain P(n|n-1): Prior error covariance matrix P(n|n): Posterior error covariance matrix Rε(n): Observation noise covariance matrix Rδ(n): System noise covariance matrix I: Identity matrix T: Represents the transpose.

Regarding the relationship between Equation 1 and Equation 2, the observation value y(n) in Equation 2 corresponds to the observation vector yi in Equation 1, and the observation matrix M(n) corresponds to the design matrix Hi in Equation 1.

According to Equation 2, the posterior estimated state quantity x^(n|n) is calculated by multiplying the observation prediction error by the Kalman gain K(n) and adding the result to the pre-estimated state quantity x^(n|n-1). Specifically, the processing by the KF independent positioning unit 812 calculates the three-dimensional position and velocity of the GNSS receiver 1.

The KF independent positioning unit 812 outputs the three-dimensional position (referred to as the "KF independent positioning position") and speed (referred to as the "KF independent positioning speed") of the GNSS receiver 1 calculated by a positioning process (specifically, independent positioning) that applies a Kalman filter.

The position difference calculation unit 813 calculates and outputs the difference between the least squares positioning position output from the least squares positioning unit 811 and the KF-only positioning position output from the KF-only positioning unit 812 (in other words, the straight-line distance between the two three-dimensional positions) (step S7).

The pseudorange residual calculation unit 814 calculates a code pseudorange using the KF independent positioning position output from the KF independent positioning unit 812 and the three-dimensional position of the satellite included in the observation station observation data, and calculates and outputs the difference between the calculated code pseudorange and the code pseudorange (called the "code observable") between the satellite included in the observation station observation data and the GNSS receiver 1 (step S8).

The pseudorange residual calculation unit 814 calculates the code pseudorange for each of the multiple satellites Sx, and calculates and outputs the difference between the code pseudorange and the code observation amount included in the observation station observation data (i.e., pseudorange residual = code pseudorange - code observation amount). The pseudorange residual calculation unit 814 also calculates the difference between the code observation amount for the code pseudorange based on the L1 wave (i.e., a carrier wave with a frequency of 1575.42 MHz) transmitted from each satellite, and calculates the difference between the code observation amount for the code pseudorange based on the L2 wave (i.e., a carrier wave with a frequency of 1227.60 MHz).

The code pseudorange is calculated by the geometric distance between the satellite and the GNSS receiver 1 + the speed of light x (the clock error of the GNSS receiver 1 - the clock error of the satellite) + the ionospheric delay + the tropospheric delay + noise. The code observable is calculated by (the time of transmission from the satellite - the time of reception by the GNSS receiver 1) x the speed of light.

The second positioning unit 82 is a mechanism for performing relative positioning to calculate the three-dimensional position of the GNSS receiver 1, and includes a pre-estimated position update unit 821, a system noise update unit 822, an observation noise update unit 823, a real solution calculation unit 824, and an integer solution calculation unit 825.

First, we will explain the positioning equation used by the second positioning unit 82.

First, the phase of the ranging signal when the radio wave from the satellite Ss is received at the reference station (in other words, the carrier phase: carrier observation quantity) φb_s [cycle] is expressed by the following Equation 3.
(Equation 3) λsφb_s = ρb_s + λsNb_s + c(dtb - dT_s) - Db_s + Tb_s
Here,
λs: Wavelength of the satellite Ss carrier wave [m]
ρb_s: Geometric distance between the reference station and the satellite Ss [m]
Nb_s: Real-valued bias [cycle]
dtb: Clock error of the reference station [seconds]
dT_s: Satellite clock error [seconds]
Db_s: Ionospheric delay between the reference station and the satellite Ss [m]
Tb_s: Tropospheric delay between the reference station and the satellite Ss [m]
c: Carrier wave speed [m/sec]
Subscript b: stands for reference station.
The subscript s represents the satellite Ss.

Regarding the phase of the ranging signal expressed according to Equation 3, when performing relative positioning using observation station observation data and reference station observation data, the double difference (double phase difference) φrb_uk of the phase of the ranging signal for satellite Su (reference satellite) and satellite Sk at the GNSS receiver 1 (observation station) and the reference station is calculated as shown in Equation 4 below.
(Equation 4) λφrb_uk = ρrb_uk + λNrb_uk
Here,
λ: wavelength of the carrier waves (for example, L1 waves and L2 waves) of the satellites Su and Sk; ρrb_uk: double difference of geometric distances; Nrb_uk: double difference of real value biases; Subscript r: represents the GNSS receiver 1 (observation station).
Subscript b: denotes a reference station.
The subscript u represents the satellite Su (reference satellite).
The subscript k represents the satellite S k .

As can be seen from Equation 4, by calculating the double difference of the observational quantities, the clock errors of the GNSS receiver 1 (observation station) and the reference station, the satellite clock errors, the ionospheric delay, and the tropospheric delay superimposed on the observational data are offset.

In addition, when performing relative positioning, the double difference φrb_uk of the phase (in other words, the carrier phase) of the ranging signal for satellite Su (reference satellite) and satellite Sk at the GNSS receiver 1 (observation station) and the reference station can be calculated as shown in the following Equation 5.
(Equation 5) φrb_uk = [φr_u - φb_u] - {φr_k - φb_k}
Here,
φr_u: Phase of the ranging signal of satellite Su (reference satellite) received by GNSS receiver 1 φb_u: Phase of the ranging signal of satellite Su (reference satellite) received at the reference station φr_k: Phase of the ranging signal of satellite Sk received by GNSS receiver 1 φb_k: Phase of the ranging signal of satellite Sk received at the reference station Subscript r: Represents GNSS receiver 1 (observation station).
Subscript b: stands for reference station.
The subscript u represents the satellite Su (reference satellite).
The subscript k represents the satellite S k .

The double difference ρrb_uk of the geometric distance in Equation 4 is expressed as ρrb_uk = [ρr_u - ρb_u] - [ρr_k - ρb_k], and the geometric distance ρb_u from the reference station to satellite Su (reference satellite) and the geometric distance ρb_k from the reference station to satellite Sk are calculated from the three-dimensional positions of satellites Su and Sk calculated based on satellite orbit information (in other words, ephemeris) and the known three-dimensional position of the reference station. The geometric distance ρr_u from the GNSS receiver 1 to satellite Su (reference satellite) and the geometric distance ρr_k from the GNSS receiver 1 to satellite Sk are calculated from the three-dimensional positions of satellites Su and Sk calculated based on satellite orbit information and the approximate three-dimensional position of the GNSS receiver 1. Since the amount of difference in position from the approximate three-dimensional position of GNSS receiver 1 to the exact three-dimensional position of GNSS receiver 1 is unknown, it is found as an unknown quantity in the positioning equation, and once the unknown quantity is found, the exact three-dimensional position of GNSS receiver 1 can be found.

The double difference of real-valued biases Nrb_uk in Equation 4 is expressed as Nrb_uk = [Nr_u - Nb_u] - [Nr_k - Nb_k] using the real-valued bias Nr_u between GNSS receiver 1 and satellite Su, the real-valued bias Nb_u between the reference station and satellite Su, the real-valued bias Nr_k between GNSS receiver 1 and satellite Sk, and the real-valued bias Nb_k between the reference station and satellite Sk.

Then, the positioning equation (i.e., the positioning equation used in the second positioning unit 82) in which the phase double difference φrb_uk obtained according to Equation 5 is the observable is expressed as Equation 6 below (note that the subscript r indicates relative positioning).
(Equation 6) yr = Hr xr + ε

In Equation 6, yr is an observation vector whose elements are the double phase difference φrb_uk calculated according to Equation 5, Hr is a design matrix, and xr is an unknown vector whose elements are the three-dimensional position of the GNSS receiver 1 (unknown) and the double difference Nrb_uk (unknown) of the real-valued bias in Equation 4. Also, ε is a noise term.

The second positioning unit 82 obtains the unknown vector xr of Equation 6 (specifically, the elements of which are the three-dimensional position of the GNSS receiver 1 and the double difference Nrb_uk of the real-valued bias) by applying a Kalman filter (see Equation 2 above).

Regarding the relationship between Equation 6 and Equation 2, the observation value y(n) in Equation 2 corresponds to the observation vector yr in Equation 6, and the observation matrix M(n) corresponds to the design matrix Hr in Equation 6.

In the processing by the second positioning unit 82, a real-valued bias (in other words, a real solution of the integer-valued bias) is calculated, and the real-valued bias is converted to an integer to obtain an integer solution of the integer-valued bias.

The pre-estimated position update unit 821 updates the pre-estimated state quantity x^(n|n-1) of the Kalman filter (see Equation 2), and the system noise update unit 822 updates the system noise covariance matrix Rδ(n) of the Kalman filter (step S9). Figure 3 is a flow diagram showing the processing procedure in the pre-estimated position update unit 821 and the system noise update unit 822, and is a flow diagram showing the details of the processing in step S9 in the flow diagram of Figure 2.

The advance estimated position update unit 821 calculates the three-dimensional movement distance (called the "estimated movement distance") of the GNSS receiver 1 by multiplying the KF-only positioning speed (specifically, a three-dimensional speed vector) output from the KF-only positioning unit 812 by the elapsed time from the previous relative positioning process by the second positioning unit 82 to the current relative positioning process.

The advance estimated position update unit 821 then updates the three-dimensional position of the GNSS receiver 1 (i.e., the advance estimated position) included as an element in the advance estimated state quantity x^(n|n-1) of the Kalman filter (see Equation 2) by adding the estimated movement distance to the three-dimensional position of the GNSS receiver 1 obtained by the previous relative positioning process by the second positioning unit 82 (step S9-1). Note that in the initial stage when the previous relative positioning process by the second positioning unit 82 has not been performed, for example, the KF-only positioning position output from the KF-only positioning unit 812 may be used as the initial value of the three-dimensional position of the GNSS receiver 1.

In the positioning process (specifically, relative positioning) in the second positioning unit 82, the amount of calculation can be reduced by using the three-dimensional position of the GNSS receiver 1 updated using the KF independent positioning speed obtained by the positioning process (specifically, independent positioning) by the first positioning unit 81. In addition, by not estimating the speed in the positioning process in the second positioning unit 82, the amount of calculation for the relative positioning process, which is highly accurate but has a high load, can be reduced.

The system noise update unit 822 determines whether the absolute value of the difference between the least-squares positioning position and the KF-only positioning position output from the position difference calculation unit 813 (referred to as the "positioning position difference ΔPlk") is greater than or equal to the position difference threshold Tpd (step S9-2).

The position difference threshold Tpd is a threshold for determining whether the positioning environment at the current time is good or bad, which is related to the positioning of the GNSS receiver 1. The position difference threshold Tpd is not limited to a specific value, but is set to an appropriate value taking into consideration that the above-mentioned determination can be performed appropriately and the magnitude of error normally expected in single positioning. The position difference threshold Tpd may be set to any value within a range of about 3 to 10 m, for example, and may be set to a value of about 5 m in particular.

If the positioning position difference ΔPlk is equal to or greater than the position difference threshold Tpd (step S9-2: Yes), the system noise update unit 822 updates the system noise covariance matrix Rδ(n) by adding the positioning position difference ΔPlk to the system noise covariance matrix Rδ(n) of the Kalman filter (see Equation 2) (step S9-3).

Here, if the absolute value of the difference between the least squares positioning position and the KF-only positioning position (i.e., the positioning position difference ΔPlk) is large, it is considered that the positioning environment is poor and therefore the error in the KF-only positioning speed is also large. Therefore, as the speed error, the system noise covariance matrix Rδ(n) of the Kalman filter (see Equation 2) is amplified by an amount corresponding to the magnitude of the positioning position difference ΔPlk. This allows changes in the positioning environment and the reliability of the speed amount to be appropriately reflected in the positioning process. In particular, by focusing on the difference between positioning using the least squares method, which is an instantaneous estimation, and positioning using the Kalman filter, which is a sequential estimation, it is possible to accurately determine cases where the estimation accuracy is likely to decrease, and the determination result can be appropriately reflected in the positioning process.

When the positioning position difference ΔPlk is equal to or greater than the position difference threshold Tpd, the system noise update unit 822 updates the system noise covariance matrix Rδ(n) by adding the positioning position difference ΔPlk to the system noise covariance matrix Rδ(n) of the Kalman filter (see Equation 2), as shown in Equation 7 below.

The variables (scalar values) in Equation 7 are as follows:
_Rδx (n): System noise covariance value for the x-coordinate position in the ECEF coordinate system
(ECEF: Earth Centered, Earth Fixed)
_Rδy (n): System noise covariance value for the y coordinate position in the ECEF coordinate system _Rδz (n): System noise covariance value for the z coordinate position in the ECEF coordinate system _RδSk (n): System noise covariance value for satellite Sk (where k = 1, 2, 3, ...)
ΔPlk(n): absolute value of the difference between the least squares positioning position and the KF only positioning position by the processing of the first positioning unit (i.e., positioning position difference)
β: Coefficient (specifically, for example, 0.001, 0.0001, etc.)
n: time

Then, the system noise update unit 822 advances the relative positioning processing procedure to step S10.

On the other hand, if the positioning position difference ΔPlk is less than the position difference threshold Tpd (step S9-2: No), the system noise update unit 822 advances the relative positioning processing procedure to step S10.

The observation noise update unit 823 updates the observation noise covariance matrix Rε(n) of the Kalman filter (see Equation 2) (step S10). Figure 4 is a flow diagram showing the processing procedure in the observation noise update unit 823, and is a flow diagram showing the details of the processing of step S10 in the flow diagram of Figure 2.

The observation noise update unit 823 determines whether the average value of the absolute value of the difference between the code pseudorange and the code observation amount of the observation station observation data output from the pseudorange residual calculation unit 814 (called the "pseudorange residual average value ΔDavg") is greater than or equal to the distance difference threshold Tdd (step S10-1).

Specifically, the observation noise update unit 823 determines whether the average of the absolute value of the difference between the code observation amount for the code pseudorange based on the L1 wave and the absolute value of the difference between the code observation amount for the code pseudorange based on the L2 wave for each of the multiple satellites Sx (i.e., the average value for all carrier waves of all satellites) is equal to or greater than the distance difference threshold Tdd as the pseudorange residual average value ΔDavg.

The distance difference threshold Tdd is a threshold for determining whether the positioning environment at the current time is good or bad, which is related to the positioning of the GNSS receiver 1. The distance difference threshold Tdd is not limited to a specific value, but is set to an appropriate value taking into consideration that the above-mentioned judgment can be performed appropriately and the magnitude of error normally expected in single positioning. The distance difference threshold Tdd may be set to any value within the range of about 1 to 5 m, for example, and may be set to a value of about 2 m in particular.

If the pseudorange residual average value ΔDavg is equal to or greater than the distance difference threshold Tdd (step S10-1: Yes), the observation noise update unit 823 then determines whether the absolute value of the difference between the least-squares positioning position and the KF-only positioning position (i.e., the positioning position difference ΔPlk) output from the position difference calculation unit 813 is equal to or greater than the position difference threshold Tpd (step S10-2).

Note that in the processing of step S10-2, a value different from the position difference threshold Tpd in the processing of step S9-2 described above may be used as the position difference threshold.

If the positioning position difference ΔPlk is equal to or greater than the position difference threshold Tpd (step S10-2: Yes), the observation noise update unit 823 sets the observation noise amplification coefficient to a value based on the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk (step S10-3).

Here, if the average absolute value of the difference between the code pseudorange and the code observation (i.e., the pseudorange residual average value ΔDavg) is large, it is considered that the positioning environment is poor, so the observation noise covariance matrix Rε(n) of the Kalman filter (see formula 2) is amplified. This allows changes in the positioning environment to be appropriately reflected in the positioning process.

In addition, since a large absolute value of the difference between the least squares positioning position and the KF-only positioning position (i.e., the positioning position difference ΔPlk) is considered to indicate a poor positioning environment, the observation noise covariance matrix Rε(n) of the Kalman filter (see Equation 2) is amplified. This allows changes in the positioning environment to be appropriately reflected in the positioning process. In particular, by focusing on the difference between positioning using the least squares method, which is an instantaneous estimation, and positioning using the Kalman filter, which is a sequential estimation, it is possible to accurately determine cases in which the estimation accuracy is likely to decrease, and the determination result can be appropriately reflected in the positioning process.

When the pseudorange residual average value ΔDavg is equal to or greater than the distance difference threshold Tdd and the positioning position difference ΔPlk is equal to or greater than the position difference threshold Tpd, the observation noise update unit 823 specifically sets the value of the observation noise amplification coefficient to the sum of the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk (i.e., the observation noise amplification coefficient α = pseudorange residual average value ΔDavg + positioning position difference ΔPlk; however, the observation noise amplification coefficient is 1.0 or greater).

Then, the observation noise update unit 823 advances the relative positioning processing procedure to step S10-5.

On the other hand, if the pseudorange residual average value ΔDavg is less than the distance difference threshold Tdd (step S10-1: No), or if the positioning position difference ΔPlk is less than the position difference threshold Tpd (step S10-2: No), the observation noise update unit 823 sets the observation noise amplification coefficient to 1.0 (step S10-4). Then, the observation noise update unit 823 advances the relative positioning processing procedure to step S10-5.

The observation noise update unit 823 then updates the observation noise covariance matrix Rε(n) of the Kalman filter (see Equation 2) by multiplying the observation noise amplification coefficient by the observation noise covariance matrix Rε(n) (step S10-5).

The observation noise update unit 823 performs the process of step S10-5 for each of all satellites used in the relative positioning process in the second positioning unit 82, and for each of the L1 waves and L2 waves transmitted from each satellite. In other words, the observation noise update unit 823 multiplies the observation noise elements related to the L1 waves and the L2 waves of each satellite, which are included in the observation noise covariance matrix Rε(n) of the Kalman filter (see Equation 2), by the observation noise amplification coefficient.

Specifically, the observation noise update unit 823 updates the observation noise covariance matrix Rε(n) by multiplying the observation noise amplification coefficient by the observation noise variance value of the Kalman filter (see Equation 2) as shown in the following Equation 8.

The variables (scalar values) in Equation 8 are as follows:
R _u1 (n): Observation noise variance of the ranging signal of the L1 wave of the satellite Su (reference satellite) R _u2 (n): Observation noise variance of the ranging signal of the L2 wave of the satellite Su (reference satellite) R _j1 (n): Observation noise variance of the ranging signal of the L1 wave of the satellite Sj (other than the reference satellite) R _j2 (n): Observation noise variance of the ranging signal of the L2 wave of the satellite Sj (other than the reference satellite) α(n): Observation noise amplification coefficient expressed as (ΔDavg + ΔPlk) n: Time where j = 1, 2, 3, ..., m

Here, in the above formula 8, the observation noise variance values R _j1 (n) and R _j2 (n) of the satellites Sj other than the reference satellite are multiplied by the observation noise amplification coefficient α(n). However, the process of multiplying the observation noise amplification coefficient α(n) is not limited to the process for satellites other than the reference satellite. For example, it may be multiplied by the observation noise variance value R _u1 (n) of the ranging signal of the L1 wave of the reference satellite Su or the observation noise variance value R _u2 (n) of the ranging signal of the L2 wave of the reference satellite Su.

In addition, in the above formula 8, only the diagonal elements of the observation noise covariance matrix Rε(n) are multiplied by the observation noise amplification coefficient α(n), but the elements multiplied by the observation noise amplification coefficient α(n) are not limited to the diagonal elements, and elements other than the diagonal elements may also be multiplied by the observation noise amplification coefficient α(n).

Furthermore, in the above formula 8, the observation noise covariance matrix Rε(n) is multiplied directly by the sum of the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk as the observation noise amplification coefficient α(n). However, the value by which the observation noise covariance matrix Rε(n) is multiplied (i.e., the observation noise amplification coefficient α(n)) is not limited to the sum of the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk itself, and may be, for example, a value that changes depending on the magnitude of the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk (in other words, a value based on the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk). Specifically, for example, it may be a value calculated by a function that uses the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk as variables, or a value that is predetermined for each range of the sum of the pseudorange residual average value ΔDavg and the positioning position difference ΔPlk.

Then, the observation noise update unit 823 advances the relative positioning processing procedure to step S11.

The real solution calculation unit 824 performs relative positioning using a Kalman filter (see Equation 2), and calculates a real-valued bias (in other words, a real solution of an integer-valued bias) by solving the positioning equation (see Equation 6) using a Kalman filter in which the system noise covariance matrix Rδ(n) and the observation noise covariance matrix Rε(n) have been updated in the processing of steps S9 and S10 (step S11).

The integer solution calculation unit 825 then performs relative positioning using the Kalman filter (see Equation 2), converting the real-valued bias calculated by the processing of step S11 into an integer (in other words, finding the integer solution of the integer bias based on the real solution of the integer bias) (step S12).

The integer solution calculation unit 825, for example, finds the integer bias (integer solution; i.e., wave number) with the smallest error for the real value bias (real solution) calculated by the processing of step S11 as a first candidate, and finds the integer bias (integer solution; i.e., wave number) with the next smallest error as a second candidate. Although the method for identifying an integer solution with a small error is not limited to a specific method, for example, the integer least squares method can be used, and in particular, the LAMBDA method can be used, which narrows the search space for integer solutions by decorrelating the integer bias and identifying a solution.

The integer solution calculation unit 825 then determines whether or not to fix the integer solution identified above. Specifically, it determines the reliability of the integer solution identified above, and if a highly reliable integer solution is obtained, it fixes the integer solution. The fixed integer solution is then used to perform relative positioning. The three-dimensional position of the GNSS receiver 1 calculated using the integer bias (in other words, the integer solution of the integer bias, i.e., the wave number) is called the "fixed solution."

The method for determining the reliability of the integer solution is not limited to a specific method, but for example, a ratio test may be used. The ratio, which is an index used in the ratio test, is the ratio of the distance between the real value bias and the second candidate for the integer value bias to the distance (called the "norm") between the real value bias (in other words, the real solution of the integer value bias) and the first candidate for the integer value bias (in other words, the integer solution of the integer value bias). Generally, the higher the ratio, the higher the reliability of the first candidate for the integer value bias. Therefore, it is conceivable to set an appropriate threshold value, and when the ratio is greater than the threshold value, determine that the reliability of the integer value bias is high and adopt the first candidate for the integer value bias.

According to the positioning device 8 as described above, the second positioning unit 82 performs relative positioning using information output from the first positioning unit 81 that performs independent positioning, so that changes in the positioning environment and the reliability of the speed amount can be appropriately reflected in the positioning process, making it possible to estimate the positioning position with high accuracy.

Although the embodiment of the present invention has been described above, the specific configuration is not limited to the above embodiment, and even if there are design changes within the scope of the invention that do not deviate from the gist of the invention, they are included in the invention.

For example, in the above embodiment, the following processes A) to D) are performed, however, performing all of the following processes A) to D) is not an essential configuration of the present invention, and at least one of the following processes A) to D) may be performed.
(a) The KF independent positioning speed, which is the speed of the GNSS receiver 1, is calculated by independent positioning using the Kalman filter (step S6), and the three-dimensional position of the GNSS receiver 1, which is the pre-estimated state quantity of the Kalman filter, is updated by adding the estimated movement distance, which is the movement distance of the GNSS receiver 1 calculated using the KF independent positioning speed (step S9-1).
a) The difference between the least-squares positioning position and the KF-only positioning position is calculated (step S7), and if the absolute value of the difference, that is, the positioning position difference ΔPlk, is greater than or equal to the position difference threshold Tpd (step S9-2: Yes), the system noise covariance matrix Rδ(n) of the Kalman filter is updated by adding the positioning position difference ΔPlk to the system noise covariance matrix Rδ(n) of the Kalman filter (step S9-3).
c) The difference between the least-squares positioning position and the KF-only positioning position is calculated (step S7), and if the absolute value of the difference, that is, the positioning position difference ΔPlk, is greater than or equal to the position difference threshold Tpd (step S10-2: Yes), the observation noise covariance matrix Rε(n) of the Kalman filter is amplified (step S10-3).
d) The difference between the code pseudorange and the code observation calculated from the KF independent positioning position and the three-dimensional position of the satellite is calculated (step S8). If the pseudorange residual average value ΔDavg, which is the average value of the absolute values of the difference, is equal to or greater than the distance difference threshold value Tdd (step S10-1: Yes), the observation noise covariance matrix Rε(n) of the Kalman filter is amplified (step S10-3).

The main point of this invention is that at least one of the above processes A) to D) is performed, and the specific configuration of the GNSS receiver 1 and the positioning device 8 is not limited to the example shown in FIG. 1, and the processing content other than the main point is not limited to the processing content in the above embodiment.

This invention can be applied, for example, to the fields of in-vehicle navigation systems, vehicle control, and automated driving systems.

REFERENCE SIGNS LIST 1 GNSS receiver 2 GNSS antenna 3 GNSS receiving unit 4 Data communication antenna 5 Data receiving unit 6 Satellite information acquisition unit 7 Reference station information acquisition unit 8 Positioning device 81 First positioning unit 811 Least squares positioning unit 812 KF independent positioning unit 813 Position difference calculation unit 814 Pseudorange residual calculation unit 82 Second positioning unit 821 Advance estimated position update unit 822 System noise update unit 823 Observation noise update unit 824 Real solution calculation unit 825 Integer solution calculation unit

Claims (6)

A positioning device in which a reference station and a receiver receive ranging signals transmitted from a plurality of satellites and calculate a relative position of the receiver with respect to the reference station,
a first positioning unit that performs point positioning using a least squares method and point positioning using a Kalman filter using the ranging signal received by the receiver;
a second positioning unit that performs relative positioning using the ranging signal received by the receiver and the ranging signal received by the reference station;
the second positioning unit performs the relative positioning using at least one of the following output from the first positioning unit: a velocity of the receiver calculated by the point positioning to which the Kalman filter is applied, a difference between a position of the receiver calculated by the point positioning to which the least squares method is applied and a position of the receiver calculated by the point positioning to which the Kalman filter is applied, and a distance between the position of the receiver calculated by the point positioning to which the Kalman filter is applied and a position of the satellite.
A positioning device characterized by: the first positioning unit includes a KF point positioning unit that calculates a velocity of the receiver by the point positioning to which the Kalman filter is applied,
the second positioning unit includes a pre-estimated position update unit that updates a three-dimensional position of the receiver in a pre-estimated state quantity of a Kalman filter by adding a moving distance of the receiver calculated using the speed;
2. The positioning device according to claim 1 . the first positioning unit includes a position difference calculation unit that calculates a difference between a position of the receiver calculated by the point positioning to which the least squares method is applied and a position of the receiver calculated by the point positioning to which the Kalman filter is applied,
the second positioning unit includes a system noise update unit that updates a system noise covariance matrix of a Kalman filter by adding the difference to the system noise covariance matrix when an absolute value of the difference is equal to or greater than a predetermined threshold value;
3. The positioning device according to claim 1 or 2. the first positioning unit includes a position difference calculation unit that calculates a difference between a position of the receiver calculated by the point positioning to which the least squares method is applied and a position of the receiver calculated by the point positioning to which the Kalman filter is applied,
the second positioning unit includes an observation noise updating unit that updates an observation noise covariance matrix of a Kalman filter by multiplying the observation noise covariance matrix by a predetermined amplification coefficient when an absolute value of the difference is equal to or greater than a predetermined threshold value;
4. The positioning device according to claim 1, wherein the positioning device is a positioning device for detecting a position of the object. the first positioning unit includes a pseudorange residual calculation unit that calculates a difference between a code pseudorange based on the position of the receiver and the positions of the satellites calculated by the point positioning to which the Kalman filter is applied and a code observation based on the ranging signal for each carrier wave of the plurality of satellites;
the second positioning unit includes an observation noise update unit that updates an observation noise covariance matrix of a Kalman filter by multiplying the observation noise covariance matrix by a predetermined amplification coefficient when an average value of the absolute values of the differences calculated for each carrier wave of each of the plurality of satellites is equal to or greater than a predetermined threshold value;
4. The positioning device according to claim 1, wherein the positioning device is a positioning device for detecting a position of the object. The first positioning unit,
a position difference calculation unit that calculates a position difference between a position of the receiver calculated by the point positioning to which the least squares method is applied and a position of the receiver calculated by the point positioning to which the Kalman filter is applied;
a pseudorange residual calculation unit that calculates a range difference between a code pseudorange based on the position of the receiver and the positions of the satellites calculated by the point positioning to which the Kalman filter is applied and a code observation based on the ranging signal for each carrier wave of the plurality of satellites,
The second positioning unit,
The absolute value of the position difference is equal to or greater than a predetermined threshold value, and
When the average value of the absolute values of the distance differences calculated for each carrier wave of each of the plurality of satellites is equal to or greater than a predetermined threshold value,
an observation noise update unit that updates the observation noise covariance matrix of the Kalman filter by multiplying the observation noise covariance matrix by a predetermined amplification coefficient;
4. The positioning device according to claim 1, wherein the positioning device is a positioning device for detecting a position of the object.

Images