JP7688356B2 - OBJECT DETECTION DEVICE, OBJECT DETECTION METHOD, AND OBJECT DETECTION SYSTEM - Google Patents

Description

This disclosure relates to an object detection device, an object detection method, and an object detection system, and in particular to an object detection device, an object detection method, and an object detection system that are suitable for detecting an object that does not carry a special device, based on propagation channel information measured by a wireless terminal station.

In the field of wireless communications, efforts are being made to improve communication speeds using a variety of technologies, such as beamforming, which controls the directionality of radio waves, in order to stably accommodate the ever-increasing amount of wireless traffic.

On the other hand, services are being considered that use signal strength information of wireless signals (e.g., RSS (Received Signal Strength)) to detect wireless terminal stations within a communication area and provide location information. In such services, for example, a wireless terminal station measures the RSS of beacon signals transmitted from multiple wireless base stations, and the location of the wireless terminal station is calculated from the multiple measured RSSs. This method is widely used as a positioning system in indoor environments where it is difficult to use GPS (Global Positioning System).

As disclosed in the following non-patent document 1, a device-free detection method is also known. With the device-free detection method, it is possible to detect objects that do not hold a special device such as an antenna, such as a person.

The device-free detection method is used in an environment where fixed wireless base stations and wireless terminal stations exist within a detection area. The wireless terminal stations measure propagation channel information (CSI) based on the radio signals transmitted from the wireless base station. The CSI reflects the presence or absence of an object in the detection area between the wireless base station and the wireless terminal stations, or the state of the object. Then, object detection is performed based on the fluctuation characteristics that appear in the CSI.

“WiFi Sensing with Channel State Information: A Survey”, YONGSEN MA, GANG ZHOU, and SHUANGQUAN WANG, Computer Science Department, College of William & Mary, USA, ACM Computing Surveys, Vol. 52, No. 3, Article 46. Publication date: June 2019.

However, device-free detection methods are implemented based on small fluctuations that occur in the propagation environment. For this reason, it is more difficult to ensure detection accuracy than when detecting an object holding a device such as an antenna. In addition, the CSI estimated by the wireless terminal station may contain momentary errors due to noise, etc. For this reason, conventional object detection methods based on CSI at a single time also have the issue of reduced detection accuracy due to momentary errors.

The present disclosure has been made with focus on the above-mentioned problems, and has as its first objective to provide an object detection device that can always detect device-free objects within a detection area with high accuracy by suppressing the effects of momentary errors caused by noise, etc.
A second object of the present disclosure is to provide an object detection method for always detecting device-free objects within a detection area with high accuracy by suppressing the influence of momentary errors caused by noise, etc.
Furthermore, a third objective of the present disclosure is to provide an object detection system that can always detect device-free objects within a detection area with high accuracy by suppressing the effects of momentary errors caused by noise, etc.

In order to achieve the above object, a first aspect of the present disclosure is an object detection device that detects an object in a detection area between a wireless base station and a wireless terminal station, comprising:
A process of receiving a feedback signal that carries information related to propagation channel information estimated for each subcarrier of a communication wave and is transmitted by the wireless terminal station;
extracting information relating to the propagation channel information from the feedback signal;
A process of combining information related to the propagation channel information in a time series to generate time series propagation channel information;
performing the object detection based on the time series propagation channel information;
Preferably, the system is configured to execute:

A second aspect of the present disclosure is an object detection method for detecting an object in a detection area between a wireless base station and a wireless terminal station, comprising:
a step of transmitting a radio signal to a radio terminal station by the radio base station on each of a plurality of subcarriers included in a frequency band used for communication;
the wireless terminal estimating propagation channel information for each of the subcarriers;
The wireless terminal station transmits a feedback signal carrying information related to the propagation channel information;
extracting information related to said propagation channel profile from said feedback signal;
combining the information related to the propagation channel information in a time series to generate time series propagation channel information;
performing the object detection based on the time series propagation channel information;
It is preferable that the present invention includes the following:

A third aspect of the present disclosure is an object detection system,
a wireless base station that transmits wireless signals on a plurality of subcarriers included in a frequency band used for communication;
a wireless terminal station that receives the wireless signal, estimates propagation channel information for each of the subcarriers, and transmits a feedback signal carrying information related to the propagation channel information;
an object detection device that detects an object in a detection area between the wireless base station and the wireless terminal station,
The object detection device includes:
receiving the feedback signal;
extracting information relating to the propagation channel information from the feedback signal;
A process of combining information related to the propagation channel information in a time series to generate time series propagation channel information;
and performing the object detection process based on the time-series propagation channel information.

According to the present disclosure, it is possible to suppress the effects of momentary errors caused by noise, etc., and to consistently detect device-free objects within the detection area with high accuracy.

FIG. 1 is a diagram for explaining a configuration of an object detection system according to a first embodiment of the present disclosure. FIG. 2 is a diagram for explaining an example of a detection area targeted by the first embodiment of the present disclosure. 1 is a diagram for explaining an overview of propagation channel information acquired by a wireless terminal station in the first embodiment of the present disclosure. FIG. FIG. 1 is a diagram illustrating an example of frequency characteristics appearing in CSI. 1 is a diagram showing how the influence of fluctuations in a transmission path caused by the movement of an object appears in the frequency characteristics of CSI over time. FIG. 1 is a diagram illustrating an example of an impulse response obtained by performing an inverse Fourier transform on the frequency characteristics appearing in the CSI. This figure compares the detection rate when based on a single-time CSI and when based on a combination of CSI appearing in a time series. 1 is a flowchart for explaining the flow of an object detection process performed in the first embodiment of the present disclosure. FIG. 1 is a diagram for explaining a method for reducing CSI-related information in the frequency domain or the time domain. 13 is a flowchart for explaining the flow of an object detection process performed in a second embodiment of the present disclosure. FIG. 11 is a diagram for explaining the effect of information reduction performed in the second embodiment of the present disclosure. 13 is a flowchart for explaining the flow of an object detection process performed in a third embodiment of the present disclosure. 11 is a diagram for explaining a technique for using an object's moving direction as a limiting condition when the moving direction is known. FIG.

Embodiment 1.
[Configuration of First Embodiment]
1 is a block diagram showing a configuration of an object detection system according to a first embodiment of the present disclosure. The system according to the present embodiment includes a wireless base station 10. The wireless base station 10 includes M antennas 12-1 to 12-M. Hereinafter, when there is no need to distinguish between the antennas 12-1 to 12-M, the subscripts of the reference symbols will be omitted and the antennas will be referred to as "antennas 12."

The wireless base station 10 transmits wireless signals belonging to a pre-assigned frequency band from each of the antennas 12. More specifically, this frequency band includes multiple subcarriers, and the antennas 12 transmit wireless signals for each of these subcarriers.

The system of this embodiment includes a wireless terminal station 20. The wireless terminal station 20 includes N antennas 22-1 to 22-N. Each of the antennas 22-1 to 22-N receives wireless signals transmitted on multiple subcarriers from each of the antennas 12-1 to 12-M. Hereinafter, when there is no need to distinguish between the antennas 22-1 to 22-N, the subscripts of the reference symbols will be omitted and the antennas will be referred to as "antennas 22."

Figure 2 is a diagram for explaining the environment in which the antennas 12 of the wireless base station 10 and the antennas 22 of the wireless terminal station 20 are placed. This shows an example in which the wireless base station 10 has four antennas 12 and the wireless terminal station 20 has one antenna 22.

In this embodiment, both antenna 12 and antenna 22 are fixedly disposed. A detection area is set between antenna 12 and antenna 22. FIG. 2 shows an example in which the detection area is divided into eight areas (1) to (8). This embodiment discloses a system that detects whether an object that does not hold a device that emits a wireless signal, such as a person, is present within the detection area, and further, in which area and at what density the object is present within the detection area.

Referring again to FIG. 1, radio units 24-1 to 24-N are connected to antennas 22-1 to 22-N of wireless terminal station 20, respectively. Hereinafter, when there is no need to distinguish between radio units 24-1 to 24-N, the subscripts of the reference numbers will be omitted and they will be referred to as "radio unit 24."

The radio unit 24 converts the radio signal received by the antenna 22 into a format that allows estimation of propagation channel information (CSI). The signal converted by the radio unit 24 is provided to the CSI estimation unit 26. Here, CSI refers to amplitude information, phase information, and information on relative values between antennas for each subcarrier of OFDM (Orthogonal Frequency Division Multiplexing) on the propagation path.

M types of radio signals emitted from antennas 12-1 to 12-M of radio base station 10 arrive at antenna 22-1 of radio terminal station 20. The same applies to the other antennas 22-2 to 22-N. As radio units 24-1 to 24-N each receive M types of radio signals, M×N types of signals are provided to CSI estimation unit 26. Each of these M×N types of signals contains a frequency element for each subcarrier.

Figure 3 is a diagram for explaining an overview of the CSI estimated by the CSI estimation unit 26. Figure 3 shows how an M x N CSI matrix is defined for each subcarrier, and how these are repeatedly defined for each sampling time. As shown in Figure 3, the CSI estimation unit 26 estimates the CSI for each sampling time in the form of an M x N matrix arranged in the frequency direction.

As shown in FIG. 1, the CSI estimated by the CSI estimation unit 26 is provided to the conversion unit 28. The conversion unit 28 converts the M×N CSI matrix into a right singular matrix by singular value decomposition according to standardized techniques for wireless communication. The converted right singular matrix is provided to the compression unit 30.

The compression unit 30 compresses the information of the right singular matrix using a known method defined in standardization technology. The compressed information is converted into a radio signal by the radio unit 32 and transmitted as a feedback signal from the antennas 22-1 to 22-N.

The system of this embodiment includes an object detection device 40 in addition to a wireless base station 10 and a wireless terminal station 20. The CSI feedback signal emitted from the wireless terminal station 20 is received by the wireless base station 10 and also by an antenna 42 of the object detection device 40. The signal received by the antenna 42 is provided to an extraction unit 44.

The extraction unit 44 extracts compressed CSI information from the signal provided by the antenna 42. The extracted compressed information is provided from the extraction unit 44 to the decompression unit 46. The decompression unit 46 reconstructs the right singular matrix from the extracted information using a decompression method defined by standardization. This reconstructs the right singular matrix for each subcarrier, that is, the right singular matrix arranged in the frequency direction.

Figure 4 shows the result of extracting specific elements of the right singular matrix and arranging them in the frequency direction. Each element of the right singular matrix generated for each subcarrier can be expressed as a frequency characteristic as shown in Figure 4.

The information on the right singular matrix restored by the decompression unit 46 is provided to the information combination unit 48 and the storage unit 50, as shown in FIG. 1. The storage unit 50 stores the information on the right singular matrix provided by the decompression unit 46 in chronological order. The information combination unit 48 reads past information from the storage unit and combines the latest information with the past information to generate a time-series CSI that includes information on the CSI at several sampling times.

Figure 5 shows how the effect of fluctuations in the transmission path caused by an object moving within the detection area appears in the frequency characteristics of the CSI over time. The three-dimensional waveform shown in the bottom part of Figure 5 shows the frequency characteristics of the CSI obtained at each of three consecutive sampling times. The effect of an object present within the detection area appears in the CSI. Then, as shown in the top part of Figure 5, when the object moves within the detection area, the effect appears in the CSI over time.

When object detection is performed by focusing on CSI at a single time (hereinafter referred to as "instantaneous CSI"), if momentary errors occur due to the effects of noise, etc., the effects of those errors are directly reflected in the object detection results. In contrast, if multiple CSI arranged in a time series (hereinafter referred to as "time-series CSI") are combined and the object detection results are derived from the combined results, the effects of momentary errors can be suppressed and the accuracy of the detection results can be improved, similar to observing a spectrogram. In order to obtain the benefits of such improved accuracy, the information combination unit 48 generates time-series CSI using information from the memory unit 50.

The time-series CSI generated by the information combination unit 48 is provided to the determination unit 52, as shown in FIG. 1. The determination unit 52 first applies an inverse Fourier transform to the frequency characteristics appearing in the time-series CSI. This converts the information in the frequency domain into an impulse response in the time domain.

Figure 6 shows an example of an impulse response obtained by applying an inverse Fourier transform to the frequency characteristics shown in Figure 4 above. In the determination unit 52, an inverse Fourier transform is applied to the frequency characteristics appearing in each of the instantaneous CSIs included in the time-series CSI. As a result, information in which multiple impulse responses such as those shown in Figure 6 are concatenated (hereinafter referred to as a "time-series impulse response") is generated.

Then, the determination unit 52 performs object detection processing using a known method based on the time-series impulse response generated in this manner. More specifically, the determination unit 52 applies machine learning to the time-series impulse response to perform object detection using a known method.

Figure 7 compares the simulation results of object detection based on instantaneous CSI with the simulation results of object detection based on time-series CSI. In the example shown in Figure 7, a higher average detection rate is obtained by using time-series CSI compared to using instantaneous CSI. Time-series CSI can reduce the effects of momentary errors superimposed on CSI due to noise, etc., and can improve the accuracy of object detection as described above.

[Processing flow in object detection device]
Fig. 8 is a flowchart for explaining the flow of processing executed by the object detection device 40 in this embodiment to realize the above functions. In this embodiment, the object detection device 40 may include a calculation processing unit and a memory that stores a program. The processing shown in Fig. 8 may be realized by the calculation processing unit executing the program stored in the memory.

In the routine shown in Figure 8, first, a CSI feedback signal emitted from the wireless terminal station 20 is received (step 100).

Next, the compressed CSI information is extracted from the received signal (step 102). This realizes the function of the extraction unit 44 shown in FIG. 1.

Next, the right singular matrix is restored from the compressed information by a default decompression process (step 104). This realizes the function of the decompression unit 46 shown in FIG. 1. The information on the decompressed right singular matrix is recorded in the memory constituting the storage unit 50.

Next, the object detection device 40 generates a time-series CSI including the right singular matrix newly reconstructed in the current process and a predetermined number of past right singular matrices read from the storage unit 50 (step 106). This realizes the function of the information combination unit 48 shown in FIG. 1.

Finally, object detection processing is performed based on the time-series CSI information obtained in step 106 (step 108). Specifically, first, an inverse Fourier transform is applied to the frequency characteristics appearing in the combined right singular matrix. Next, machine learning is applied to the combined impulse response generated as a result to perform object detection processing. This realizes the function of the determination unit 52 shown in FIG. 1.

As described above, the system of this embodiment performs object detection based on a time-series CSI obtained by combining multiple instantaneous CSIs. As a result, the system of this embodiment can suppress the effects of momentary errors caused by noise, etc., and can always achieve highly accurate object detection.

Embodiment 2.
[Configuration of the Second Embodiment]
Next, a second embodiment of the present disclosure will be described with reference to Fig. 9 and Fig. 10 as well as Fig. 1. The object detection system of the present embodiment can be realized by the configuration shown in Fig. 1, similar to the first embodiment. More specifically, the object detection system of the present embodiment can be realized by causing object detection device 40 to execute a routine shown in Fig. 10, which will be described later, instead of the routine shown in Fig. 8 above.

[Principle of Information Reduction]
Fig. 9 is a diagram for explaining a method for reducing the amount of information contained in CSI in the frequency domain or the time domain. More specifically, the upper left area of Fig. 9 shows the frequency characteristics appearing in the instantaneous CSI. The upper right area of Fig. 9 shows the impulse response in the time domain obtained by performing an inverse Fourier transform on the frequency characteristics.

CSI contains information that is meaningless or unimportant as a basis for object detection, such as repetitive information caused by delayed radio signals. When an inverse Fourier transform is applied to the frequency characteristics of CSI, such information appears in the impulse response as periodic waveforms called aliases.

The lower right area of Figure 9 shows the impulse response after information reduction (hereinafter referred to as the "reduced impulse response") obtained by applying a window function to remove aliases to the impulse response shown in the upper right area. From the viewpoint of object detection, the aliased part does not have a significant meaning. For this reason, there is no significant change in the accuracy of object detection whether the impulse response before information reduction is used as the basis or the reduced impulse response is used as the basis.

Therefore, by following the procedure below, it is possible to reduce the computational load of object detection without reducing the accuracy of object detection.
(1) An impulse response is obtained by performing an inverse Fourier transform on the frequency characteristics.
(2) Apply an appropriate window function to the impulse response to obtain a reduced impulse response.
(3) Object detection processing is performed based on the reduced impulse response.

In the frequency characteristics shown in the upper left region of Figure 9, both the dashed dots that make up the waveform and the circles spaced at regular intervals represent the CSI elements for each subcarrier. Here, the dashed dots represent data that is meaningless or unimportant from the perspective of object detection. On the other hand, the circles represent data that is important from the perspective of object detection.

The lower left area of Figure 9 shows the frequency characteristic (hereinafter referred to as the "reduced frequency characteristic") obtained by extracting only the important data, indicated by circles, from the frequency characteristic shown in the upper left area. By performing an inverse Fourier transform on the reduced frequency characteristic, a reduced impulse response containing only lean information can be obtained, as shown in the lower right area of Figure 9.

Therefore, by following the procedure below instead of the above procedures (1) to (3), it is possible to reduce the computational load of object detection without reducing the accuracy of object detection.
(A) Data is extracted at regular intervals from the frequency characteristics to obtain a reduced frequency characteristic.
(B) An inverse Fourier transform is applied to the reduced frequency characteristic to obtain a reduced impulse response.
(C) Object detection processing is performed based on the reduced impulse response.

In the above-described first embodiment, the decompression unit 46 provides the decompressed right singular matrix information as is to the information combination unit 48 and the storage unit 50. In contrast, in the system of this embodiment, the decompression unit 46 provides the information after the information reduction is performed by either of the above-described two methods to the information combination unit 48 and the storage unit 50. As a result, the information combination unit 48 provides the time-series CSI with reduced information volume to the determination unit 52. As a result, according to this embodiment, it is possible to reduce the calculation load of the determination unit 52 without reducing the detection accuracy compared to the case of the first embodiment.

[Processing flow in the second embodiment]
Fig. 10 is a flowchart of a routine executed by the object detection device 40 in this embodiment. In Fig. 10, steps similar to those shown in Fig. 8 are denoted by the same reference numerals, and their description will be omitted or simplified.

In the routine shown in FIG. 10, after the right singular matrix is restored in step 104, information reduction processing is performed (step 110). Specifically, a reduced impulse response corresponding to the frequency characteristics of the restored right singular matrix is generated by the above steps (1) and (2) or by the above steps (A) and (B). In this embodiment, the function of the decompression unit 46 shown in FIG. 1 is realized by the above steps 104 and 110.

Next, the newly generated reduced impulse response in this process is combined with a predetermined number of past reduced impulse responses read from the storage unit 50 to generate a time-series CSI (step 112). This realizes the function of the information combination unit 48 shown in FIG. 1.

Then, in step 108, object detection based on time-series CSI is performed in the same manner as in embodiment 1.

Figure 11 is a diagram for explaining the effect of information reduction performed in this embodiment. In the figure, the left bar and the center bar show the object detection rate based on instantaneous CSI and time-series CSI, as in the case shown in Figure 7. The right bar shows the simulation results of object detection performed based on time-series CSI with information reduction. However, here, it is assumed that the information combination conditions are set so that the data size of the time-series CSI is 16 times the data size of the instantaneous CSI. It is also assumed that the information reduction conditions are set so that the data size is 1/16.

The results shown in Figure 11 show that object detection using time-series CSI, which has a reduced amount of information, achieves a better average detection rate than when using instantaneous CSI, while using a similar data size as when using instantaneous CSI. Thus, according to the system of this embodiment, it is possible to achieve the same excellent detection accuracy as in embodiment 1, but with a significantly lighter computational load than in embodiment 1.

Embodiment 3.
Next, a third embodiment of the present disclosure will be described with reference to Fig. 12 together with Fig. 1. The object detection system of the present embodiment can be realized by the configuration shown in Fig. 1, similar to the cases of the first and second embodiments. More specifically, the object detection system of the present embodiment can be realized by causing the object detection device 40 to execute a routine shown in Fig. 12, which will be described later, instead of the routine shown in Fig. 8 or Fig. 10 above.

In the above-described first and second embodiments, it is assumed that the information combining conditions are experimentally determined in advance. The routine shown in FIG. 12 executed in this embodiment includes a process for selecting appropriate combining conditions. The object detection system of this embodiment is characterized in that the routine shown in FIG. 12 is used to provide the object detection device 40 with the function of selecting appropriate combining conditions.

Figure 12 shows a flowchart of a routine executed by the object detection device 40 in this embodiment. In Figure 12, steps that are the same as those shown in Figure 8 are given the same reference numerals, and their explanations are omitted or simplified.

In the routine shown in FIG. 12, in step 106, the right singular matrices are combined under appropriate combining conditions, as in the first embodiment. The combining conditions may, for example, include the number of instantaneous CSIs to be combined and the time interval between the instantaneous CSIs to be combined. Following this process, in step 108, object detection is performed based on the time-series CSI obtained by combining.

In the routine shown in FIG. 12, following the above process, it is determined whether the search for join conditions has ended (step 114). In this embodiment, candidates for join conditions are determined in advance. Here, it is determined whether the processes of steps 106 and 108 have been performed for all of the candidates. Then, if it is determined that these processes have not been performed for all of the candidates, the determination of the end of the search is denied.

If the determination of search termination is denied in step 114 above, the joining condition is changed to an unused one (step 116). Thereafter, the processing of steps 106 and 108 is carried out again using the changed joining condition.

On the other hand, if the search is terminated in step 114, the combination conditions that achieve the highest detection rate are then selected (step 118).

Finally, the combination conditions selected in the above process are determined as the conditions for generating time-series CSI (step 120).

The process described above makes it possible to appropriately select the combination conditions that achieve the highest detection rate among the combination conditions expected to be used by the object detection device 40. Such optimal combination conditions may change due to changes in the environment of the detection area. According to the system of this embodiment, by following such environmental changes and always using the optimal combination conditions, it is possible to always achieve highly accurate object detection.

[Modification of the third embodiment]
Incidentally, in the above-described third embodiment, the function of selecting the optimal join condition is combined with the function of the first embodiment. However, the present disclosure is not limited to this. The function of selecting the optimal join condition may be used in combination with the information reduction function described in the second embodiment.

In addition, in the above-mentioned third embodiment, the combination condition that achieves the maximum detection rate is adopted as the optimal combination condition, but the present disclosure is not limited to this. The object detection accuracy is easier to improve as the number of instantaneous CSI combinations increases, but the calculation load of object detection increases as the number of combinations increases, and the time required for processing increases. For this reason, when real-time performance is required for object detection, a time limit may be set and the condition that achieves the maximum detection rate among the conditions that satisfy the time condition may be set as the optimal condition.

Embodiment 4.
Next, a fourth embodiment of the present disclosure will be described with reference to Fig. 1 and Fig. 13. The object detection system of the present embodiment can be realized by the configuration shown in Fig. 1, similarly to the first embodiment.

In the above-mentioned first embodiment, the information combining conditions are set without considering how objects move in the detection area. If the detection area is, for example, a "passageway" for people to move, the "movement direction" of the person, who is the object to be detected, can be assumed to be the longitudinal direction of the passageway. In that case, the "speed of movement" can also be assumed to be a standard walking speed. Even if the detection area is, for example, a road, the "movement direction" and "movement speed" of a car can be specified and assumed.

The system of this embodiment is characterized in that when the "moving direction" of an object in the detection area can be estimated, or when both the "moving direction" and "moving speed" of an object can be estimated, the system attempts to combine instantaneous CSI using combination conditions set based on those assumptions.

Figure 13 is a diagram for explaining a method for setting information combination conditions based on an object's known direction of movement. More specifically, in the example shown in Figure 13, "positions" are defined in a grid pattern at 5 cm intervals within the detection area. Each of the positions defined in Figure 13 can be associated with any element of the M x N CSI matrix at any sampling time.

When a person to be detected is detected at the "reference position" shown with hatching in FIG. 13 and the direction of movement is assumed to be the up-down direction in FIG. 13, for example, instantaneous CSI corresponding to four positions above, below, left, and right of the "reference position" is combined to generate a time-series CSI for learning detection. This makes it possible to combine instantaneous CSI along the direction of movement, and to combine instantaneous CSI so as to cover errors in the left-right direction. As a result, the system of this embodiment can achieve even better detection accuracy than the first embodiment.

In addition, if the "movement speed" can be estimated, the "position" from which the instantaneous CSI is extracted may be determined by skipping a number of "positions" according to the movement speed in the vertical direction, i.e., in the movement direction, relative to the above-mentioned "reference position." This can further improve the accuracy of object detection.

[Modification of the fourth embodiment]
Incidentally, in the above-mentioned fourth embodiment, a technique for reflecting the expected "movement direction" or both the "movement speed" and the "movement speed" in the combination conditions is combined with the configuration of the first embodiment. However, the present disclosure is not limited to this. The technique for reflecting the "movement direction" or the like in the combination conditions may be combined with the configuration of the second or third embodiment.

10 Wireless base station 20 Wireless terminal station 40 Object detection device 48 Information combination unit 50 Storage unit 52 Determination unit

Claims (8)

An object detection device that detects an object in a detection area between a wireless base station and a wireless terminal station,
A process of receiving a feedback signal that carries information related to propagation channel information estimated for each subcarrier of a communication wave and is transmitted by the wireless terminal station;
extracting information relating to the propagation channel information from the feedback signal;
performing an inverse Fourier transform on the extracted frequency characteristics of the propagation channel information to obtain an impulse response in the time domain;
A process of applying a window function to the impulse response to obtain a reduced impulse response having a reduced amount of information;
combining the reduced impulse responses in a time series to generate time series propagation channel information;
performing the object detection based on the time series propagation channel information;
2. An object detection device configured to perform the steps of: A process of changing the window function and executing the object detection process for each window function;
A process of detecting an object detection rate for each window function;
A process of adopting a window function that achieves a maximum object detection rate as a window function for generating the reduced impulse response;
The object detection device of claim 1 , further configured to perform the following: An object detection device that detects an object in a detection area between a wireless base station and a wireless terminal station,
A process of receiving a feedback signal that carries information related to propagation channel information estimated for each subcarrier of a communication wave and is transmitted by the wireless terminal station;
extracting information relating to the propagation channel information from the feedback signal;
A process of obtaining a reduced frequency characteristic by extracting elements at regular frequency intervals from the extracted frequency characteristic of the propagation channel information;
performing an inverse Fourier transform on the reduced frequency characteristic to obtain a reduced impulse response in the time domain ;
combining the reduced impulse responses in a time series to generate time series propagation channel information;
performing the object detection based on the time series propagation channel information;
2. An object detection device configured to perform the steps of : A process of changing the constant frequency interval and executing the object detection process for each frequency interval;
A process of detecting an object detection rate for each frequency interval;
A process of adopting a frequency interval that achieves a maximum object detection rate as a frequency interval for obtaining the reduced frequency characteristic;
The object detection device according to claim 3 , further configured to perform the following: The object detection device according to any one of claims 1 to 4, wherein the process of generating the time-series propagation channel information includes a process of generating the time-series propagation channel information by combining information corresponding to a direction of movement of an object to be detected in the detection area, the information occurring in time series being assumed to be moving in the specific direction. 1. An object detection method for detecting an object in a detection area between a wireless base station and a wireless terminal station, comprising:
a step of transmitting a radio signal to a radio terminal station by the radio base station on each of a plurality of subcarriers included in a frequency band used for communication;
the wireless terminal estimating propagation channel information for each of the subcarriers;
The wireless terminal station transmits a feedback signal carrying information related to the propagation channel information;
extracting information related to said propagation channel profile from said feedback signal;
performing an inverse Fourier transform on the extracted frequency characteristics of the propagation channel information to obtain an impulse response in the time domain;
applying a window function to the impulse response to obtain a reduced impulse response having a reduced amount of information;
combining the reduced impulse responses in a time series to generate time series propagation channel information;
performing the object detection based on the time series propagation channel information;
The object detection method includes: 1. An object detection method for detecting an object in a detection area between a wireless base station and a wireless terminal station, comprising:
a step of transmitting a radio signal to a radio terminal station by the radio base station on each of a plurality of subcarriers included in a frequency band used for communication;
the wireless terminal estimating propagation channel information for each of the subcarriers;
The wireless terminal station transmits a feedback signal carrying information related to the propagation channel information;
extracting information related to said propagation channel profile from said feedback signal;
A step of obtaining a reduced frequency characteristic by extracting elements at regular frequency intervals from the extracted frequency characteristic of the propagation channel information;
performing an inverse Fourier transform on the reduced frequency characteristic to obtain a reduced impulse response in the time domain;
combining the reduced impulse responses in a time series to generate time series propagation channel information;
performing the object detection based on the time series propagation channel information;
The object detection method includes: a wireless base station that transmits wireless signals on a plurality of subcarriers included in a frequency band used for communication;
a wireless terminal station that receives the wireless signal, estimates propagation channel information for each of the subcarriers, and transmits a feedback signal carrying information related to the propagation channel information;
an object detection device that detects an object in a detection area between the wireless base station and the wireless terminal station,
The object detection device includes:
receiving the feedback signal;
extracting information relating to the propagation channel information from the feedback signal;
performing an inverse Fourier transform on the extracted frequency characteristics of the propagation channel information to obtain an impulse response in the time domain;
A process of applying a window function to the impulse response to obtain a reduced impulse response having a reduced amount of information;
combining the reduced impulse responses in a time series to generate time series propagation channel information;
and performing the object detection based on the time-series propagation channel information.

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