JP7467992B2 - Underwater object search support device, underwater object search support method and program thereof - Google Patents

Description

The present invention relates to an underwater object search support device, an underwater object search support method, and a program thereof, and in particular to an underwater object search support device, an underwater object search support method, and a program thereof that calculate the optimal sensor placement interval according to the conditions of the search area.

Sonar devices are used as underwater object search devices to search for objects underwater. These sonar devices can be broadly divided into passive sonar devices and active sonar devices. Passive sonar devices are devices that capture sound waves emitted by targets (such as submarines and underwater mines) that exist underwater (including on the surface) and calculate the presence and position (direction) of the target. Active sonar devices emit sound waves into the sea from a transmitter, detect the sound reflected from the target with a receiver, and measure the position, distance, and speed of the target based on the time difference between the sound emitted by the transmitter and the sound reflected by the target, or the detection direction of the reflected sound.

Active sonar devices also include multistatic sonar devices in which the transmitter and receiver are located at separate locations. In these multistatic sonar devices, receivers may be installed at multiple locations for one transmitter. In these multistatic sonar devices, in addition to the reflected sound from the target, the received signal also includes sound that is directly transmitted from the transmitter (e.g., direct waves).

Some multistatic sonar devices are equipped with multiple receivers and operate as a multistatic sonar system. We will explain this example in terms of a form that uses a sonobuoy. A sonobuoy is an anti-submarine acoustic search device dropped by an aircraft that receives underwater acoustic signals and transmits radio waves. A sonobuoy equipped with only a receiver is called a passive sonobuoy, and one equipped with a transmitter is called an active sonobuoy. In a multistatic sonar system that uses a sonobuoy, these two types are combined to form the system. Additional sonobuoys may be installed if a sonobuoy is swept away by the tide or if the target's range is narrowed.

Here, we will explain hydrographic forecasting, a technology required to support underwater object searches using sonar devices. Hydrographic forecasting involves predicting the detectable distance of a sonar device in a search area under defined conditions and methods based on the water temperature, background noise, and characteristics of the sonar device in that area. Because hydrographic forecasting is performed before the search is carried out, it is common to calculate the water temperature, wind speed, and ambient noise in the area where the sonar will be used using forecast values and statistical values. Technology related to hydrographic forecasting is disclosed in Patent Document 1.

The transmitter/receiver placement determination device described in Patent Document 1 is an underwater object search support device that searches for underwater objects by transmitting sound waves from each transmitter to a search area and receiving echo signals reflected by underwater objects in the search area with the receiver. The transmitter/receiver placement determination device detects and determines the placement of the multiple transmitters and receivers in a predetermined search area so that a predetermined coverage rate is achieved with a predetermined number of transmitters and receivers, and includes a placement calculation unit that measures actual environment measurements required for detection judgment, calculates coverage taking into account reverberation due to the placement based on the measured actual environment measurements, and determines the placement of the multiple transmitters and receivers using a predetermined optimization method based on the coverage calculation result. The placement calculation unit determines the placement of the multiple transmitters and receivers so that a value of a cost function including the cost of each transmitter, the number of the multiple transmitters, the cost of each receiver, and the number of the multiple receivers is minimized.

JP 2015-190914 A

As mentioned above, the searchable distance in a search area varies greatly depending on the water temperature and environmental noise in the search area, but there are often discrepancies with forecasts and statistics, and the estimate of the detectable distance can be significantly off. Therefore, the transmitter/receiver placement determination device described in Patent Document 1 measures the actual environment measurement values necessary for detection judgment, and calculates the coverage taking into account reverberation due to placement based on the measured actual environment measurement values.

However, the transmitter/receiver placement determination device described in Patent Document 1 does not disclose a specific method for calculating coverage that takes reverberation into account using real-world measurement values. And, for example, if only water temperature is used as the real-world measurement value, a problem occurs in which a sufficient detectable distance cannot be calculated.

One aspect of the underwater object search support device of the present invention includes a sonar parameter estimation unit that calculates, from an input signal generated by receiving an underwater acoustic signal emitted from a transmitter installed in a search sea area with a receiver, a propagation loss estimate that is an estimate of the propagation loss of the underwater acoustic signal in the search sea area, path difference information that indicates the amount of path difference between the actual measurement result and the forecast result of sound wave propagation, noise level actual measurement information including time series information of the input signal, and the standard deviation of the noise level of the input signal, and a forecast value calculated based on input data including at least oceanographic data, bottom topography data, and sensor specification information related to the search sea area. The system has a prediction unit that calculates the echo level value and masking level value of the underwater acoustic signal in the search area by replacing or correcting the values calculated by the sonar parameter estimation unit, a signal excess calculation unit that calculates a signal excess value based on the echo level value and the masking level value, a detection probability calculation unit that calculates detection distance characteristic information indicating the distance characteristic of the detection probability based on the signal excess value and the standard deviation of the noise level, and a sensor interval calculation unit that calculates the installation interval of sensors based on the detection distance characteristic information and the overlap rate of the search range specified by the operator.

One aspect of the underwater object search method in the underwater object search support device according to the present invention is an underwater object search support method in an underwater object search support device having a prediction unit that calculates an echo level value and a masking level value of an underwater acoustic signal based on input data including at least oceanographic data, bottom topography data, and sensor specification information related to a search area, a signal excess calculation unit that calculates a signal excess value based on the echo level value and the masking level value, a detection probability calculation unit that calculates a distance characteristic of a detection probability based on the signal excess value, and a sensor interval calculation unit that calculates an installation interval of sensors based on the distance characteristic of the detection probability and an overlap rate of a search range specified by an operator, and the underwater acoustic signal transmitted from a transmitter installed in the search area is received by a receiver, and an estimated value of the propagation loss of the underwater acoustic signal in the search area, path difference information indicating the amount of path difference between the actual measurement result and the prediction result of sound wave propagation, noise including time series information of the input signal are calculated. The system calculates the standard deviation of the noise level of the input signal and the actual measurement information of the propagation loss, calculates a reverberation level forecast value using a propagation loss estimate value that is an estimate of the propagation loss when calculating the echo level value and the masking level value in the prediction unit, calculates a reverberation level forecast value using the propagation loss estimate value that is an estimate of the propagation loss, calculates a reverberation level correction value by correcting the reverberation level forecast value using the path difference information, calculates a noise level forecast value that is a forecast value of the noise level based on the input data, replaces the noise level forecast value with a noise level estimate value that is an estimate of the noise level, selects the larger value of the reverberation level correction value and the noise level estimate value, and outputs the selected value as the masking level value, calculates the echo level value for the underwater acoustic signal in the search area based on the propagation loss estimate value and a source level value that indicates the output level of the transmitter, and calculates the distance characteristic of the detection probability in the detection probability calculation unit by adding the standard deviation of the noise level to the signal excess value.

One aspect of the underwater object search support program of the present invention is an underwater object search program that is executed on a computing device and calculates the installation interval of sensors based on input data including at least oceanographic data, bottom topography data, and sensor specification information related to a search area, and an input signal generated by receiving an underwater acoustic signal emitted from a transmitter installed in the search area with a receiver, and calculates from the input signal an estimated value of the propagation loss of the underwater acoustic signal in the search area, path difference information indicating the amount of path difference between the actual measurement result and the forecast result of sound wave propagation, actual noise level information including time series information of the input signal, and a standard deviation of the noise level of the input signal. The system performs a parameter estimation process, a forecast process to calculate echo level values and masking level values of the underwater acoustic signal in the search area by replacing or correcting the predicted values calculated based on the input data with the values calculated in the sonar parameter estimation process, a signal excess calculation process to calculate a signal excess value based on the echo level value and the masking level value, a detection probability calculation process to calculate the distance characteristic of the detection probability based on the signal excess value and the standard deviation of the noise level, and a sensor interval calculation process to calculate the installation interval of the sensors based on the distance characteristic of the detection probability and the overlap rate of the search area specified by the operator.

The underwater object search support device, underwater object search support method, and program thereof of the present invention can improve the accuracy of calculating the searchable distance.

1 is a block diagram of an underwater object search support device according to a first embodiment. 5 is a characteristic diagram of a propagation loss for explaining the processing of a propagation loss substitution processing unit according to the first embodiment; FIG. 5 is a characteristic diagram of reverberation for explaining the processing of the reverberation level determination unit according to the first embodiment; FIG. 1 is a diagram illustrating the difference between predicted and measured values of the propagation path of an underwater acoustic signal. FIG. 1 is a diagram illustrating a method for estimating a reverberation peak position caused by a path difference. FIG. 13 is a diagram for explaining a method of calculating a masking level at a target assumed speed designated by an operator. 1 is a graph illustrating the difference in signal excess value and detection probability characteristics due to standard deviation. 11 is a graph illustrating the relationship between the detectable distance of a sensor and an overlap rate.

For clarity of explanation, the following description and drawings have been omitted and simplified as appropriate. Furthermore, each element shown in the drawings as a functional block performing various processes can be configured in hardware with a CPU (Central Processing Unit), memory, and other circuits, and in software, can be realized by programs loaded into memory. Therefore, those skilled in the art will understand that these functional blocks can be realized in various forms using only hardware, only software, or a combination of both, and are not limited to any of these. Furthermore, in each drawing, the same elements are given the same reference numerals, and duplicate explanations are omitted as necessary.

The above-mentioned program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible recording media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R/Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of temporary computer-readable media. Examples of temporary computer-readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable media can supply the program to a computer via wired communication paths such as electric wires and optical fibers, or wireless communication paths.

First embodiment
Hereinafter, an embodiment of the present invention will be described with reference to the drawings. An underwater object search support device 1 according to the first embodiment performs a hydrographic forecast process to calculate a sensor interval distance indicating an optimal placement interval of sensors to be installed in a search sea area based on hydrographic data, bottom topography data, and sensor specification information stored in a database 10. Here, the hydrographic data includes information such as air temperature, humidity, air pressure, rainfall, solar radiation, seawater flow, water temperature, and salinity concentration (e.g., buoy information BT) obtained from a buoy installed in the search sea area. In addition, the sensor specification information includes information such as the frequency of the underwater acoustic signal used by the transmitter T and receiver R, and the depth at which the transmitter T and receiver R are installed.

The underwater object search support device 1 according to the first embodiment acquires an input signal Si from an underwater acoustic signal propagated in the search area using a transmitter T and a receiver R installed in the search area. The underwater object search support device 1 according to the first embodiment replaces or corrects the parameters used in calculating the sensor interval distance using a value (e.g., a sonar parameter estimate) calculated from the input signal Si. One of the features of the underwater object search support device 1 according to the first embodiment is that it uses a sonar parameter estimate calculated using the input signal Si in the above-mentioned underwater observation processing. Therefore, the underwater object search support device 1 according to the first embodiment will be described in detail below.

Figure 1 shows a block diagram of the underwater object search support device 1 according to the first embodiment. As shown in Figure 1, the underwater object search support device 1 according to the first embodiment has a database 10, a prediction unit 11, an input signal processing unit 12, a sonar parameter estimation unit 13, a signal excess calculation unit 14, a detection probability calculation unit 15, a sensor interval calculation unit 16, and a display unit 17. Also, Figure 1 shows a transmitter T and a receiver R as sensors used by the underwater object search support device 1. Note that while Figure 1 shows only one pair of transmitter T and receiver R, it is assumed that multiple transmitters T and receivers R or both are used, and the number of transmitters T and receivers R can be changed as appropriate.

The database 10, prediction unit 11, input signal processing unit 12, sonar parameter estimation unit 13, signal excess calculation unit 14, detection probability calculation unit 15, sensor interval calculation unit 16, and display unit 17 may be implemented as a single device or as separate distributed devices. In addition, the prediction unit 11, input signal processing unit 12, sonar parameter estimation unit 13, signal excess calculation unit 14, detection probability calculation unit 15, and sensor interval calculation unit 16 can be realized, for example, by a program executed on a computing device (e.g., a computer).

The prediction unit 11 calculates a forecast value based on input data Di, which includes at least oceanographic data, bottom topography data, and sensor specification information related to the search area. The prediction unit 11 then calculates an echo level value EL1 and a masking level value ML2 of the underwater acoustic signal in the search area by replacing or correcting the forecast value calculated by the sonar parameter estimation unit 13.

The input signal processing unit 12 converts the input signal generated by the receiver R receiving the underwater acoustic signal emitted from the transmitter T installed in the search area into an input signal Di that can be processed by a computer. The content of the signal processing in the input signal processing unit 12 differs depending on the transmission waveform of the sonar. If the underwater acoustic signal emitted by the transmitter T is an FM (Frequency Modulation) or so-called chirp signal, the propagation loss substitution processing unit 21 performs replica correlation processing on the input signal to calculate the reception level. If the underwater acoustic signal emitted by the transmitter T is a PCW (Pulsed Continuous Wave), the input signal processing unit 12 calculates the reception level for each frequency by frequency analysis processing such as FFT. This processing is always performed regardless of the presence or absence of oscillation from the transmitter T.

The sonar parameter estimation unit 13 calculates a propagation loss estimate TLe, reverberation level determination information RLe, path difference information RTd, actual noise level information NLe, and standard deviation SD from the input signal Si. The propagation loss estimate TLe is an estimate of the propagation loss of the underwater acoustic signal in the search area. The reverberation level determination information RLe is information indicating the period during which the signal level of the input signal Si propagates at the reverberation level of the underwater acoustic signal. The path difference information RTd is information indicating the amount of path difference between the actual measurement result and the forecast result of sound wave propagation. The actual noise level information NLe is information including time series information of the input signal. The standard deviation SD is the standard deviation of the noise level of the input signal Di (i.e., the underwater acoustic signal).

Then, the signal excess calculation unit 14 calculates a signal excess value SE1 based on the echo level value EL1 and the masking level value ML2. The detection probability calculation unit 15 calculates detection distance characteristic information SR indicating the distance characteristic of the detection probability based on the signal excess value SE1 and the standard deviation SD of the noise level. The sensor interval calculation unit 16 calculates the sensor installation interval based on the detection distance characteristic information SR and the overlap rate of the search range specified by the operator. The display unit 17 displays the sensor installation interval calculated by the sensor interval calculation unit 16. The display unit 17 also displays a user interface for operating the underwater object search support device 1.

Here, one of the features of the underwater object search support device 1 according to the first embodiment is the processing in the prediction unit 11 and the sonar parameter estimation unit 13. Therefore, the following describes the details of the prediction unit 11 and the sonar parameter estimation unit 13.

The prediction unit 11 has a propagation loss calculation unit 20, a propagation loss replacement processing unit 21, a noise level calculation unit 22, a noise level replacement processing unit 23, a reverberation level calculation unit 24, a reverberation level correction processing unit 25, a masking level calculation unit 26, a masking level replacement processing unit 27, and an echo level calculation unit 28. The sonar parameter estimation unit 13 has a propagation loss estimation unit 30, a reverberation level determination unit 31, a noise level measurement unit 32, a path difference calculation unit 33, and a standard deviation calculation unit 34. In the underwater object search support device 1 according to the first embodiment, calculations are performed by operating the processing blocks implemented in the prediction unit 11 and the sonar parameter estimation unit 13 in a cooperative manner.

The propagation loss calculation unit 20 reads the input data Di and calculates the propagation loss forecast value TL1. The propagation loss forecast value TL1 is calculated based on previously acquired information such as weather forecasts, oceanographic data acquired from fixed observation points (e.g., weather observation buoys), seabed topography data, and sensor specifications, and is a forecast value of the propagation loss of the underwater acoustic signal in the search area. The propagation loss estimation unit 30 subtracts the signal level of the input signal Si from the source level value indicating the strength at which the transmitter T outputs the underwater acoustic signal to output the propagation loss estimated value TLe. The propagation loss substitution processing unit 21 replaces the propagation loss forecast value TL1 with the propagation loss estimated value TLe and outputs the propagation loss substitution value TL2. In other words, the propagation loss substitution value TL2 can be considered to be the same as the propagation loss estimated value TLe.

Here, the calculation process of the propagation loss replacement value TL2 by the propagation loss calculation unit 20, the propagation loss estimation unit 30, and the propagation loss replacement processing unit 21 will be described. FIG. 2 shows a propagation loss characteristic diagram for explaining the processing of the propagation loss replacement processing unit according to the first embodiment. In the example shown in FIG. 2, three propagation loss estimate values TLe are calculated based on the input signals Si acquired from three sensors (sensors A to C in FIG. 2) at different distances from the receiver R. The propagation loss replacement processing unit 21 generates an estimated propagation loss characteristic curve based on the propagation loss estimate value TLe calculated by the propagation loss estimation unit 30. In FIG. 2, the estimated propagation loss characteristic curve is shown by a dashed line. Also, in FIG. 2, the forecast characteristic curve using the propagation loss forecast value TL1 calculated by the propagation loss calculation unit 20 is shown by a solid line. The propagation loss replacement processing unit 21 outputs the propagation loss replacement value TL2 by replacing the forecast characteristic curve with the estimated characteristic curve.

The noise level calculation unit 22 calculates a noise level forecast value NL1, which is a forecast value of the noise level, based on the input data Di. The noise level measurement unit 32 outputs noise level actual measurement information NLe, which is time series information of the input signal Si, based on the input signal Si. More specifically, the noise level measurement unit 32 calculates the noise level actual measurement information NLe by taking into account the specifications of the sonar device (DI (directivity gain of the receiver), reception frequency band, etc.) for the reception level of the input signal Si. The noise level substitution processing unit 23 replaces the noise level forecast value NL1 with the noise level actual measurement information NLe and outputs a noise level substitution value NL2. Note that this noise level actual measurement information NLe includes reverberation level determination information RLe, in which the signal level of the input signal Si indicates the reverberation level.

The reverberation level calculation unit 24 calculates a reverberation level forecast value RL1, which is a forecast value of the reverberation level of the underwater acoustic signal, based on a propagation loss estimate value TLe (or a propagation loss replacement value TL2) indicating an estimated value of the propagation loss. The reverberation level determination unit 31 determines the period during which the underwater acoustic signal propagates at a reverberation level as a reverberation area period based on the signal level of the input signal Si, and outputs reverberation level determination information RLe indicating the reverberation area period. This reverberation level determination information RLe is output to the noise level measurement unit 32 and the path difference calculation unit 33.

Figure 3 shows a reverberation characteristic diagram for explaining the processing of the reverberation level determination unit 31 according to the first embodiment. The processing of the reverberation level determination unit 31 will be explained with reference to Figure 3. As shown in Figure 3, the reverberation level determination unit 31 compares the signal level of the input signal Si after oscillation from the transmitter T with the noise level actual measurement information NLe measured before oscillation at every time, and if the difference between the two exceeds a preset threshold, it determines that the time is the reverberation region of the underwater acoustic signal.

Then, the path difference calculation unit 33 calculates path difference information RTd based on the reverberation level determination information RLe and the reverberation level forecast value RL1. Here, FIG. 4 shows a diagram explaining the difference between the forecast value and the actual measured value of the propagation path of the underwater acoustic signal, and FIG. 5 shows a diagram explaining a method of estimating the reverberation peak position caused by the path difference. Then, the calculation method of the path difference information in the path difference calculation unit 33 will be described in detail with reference to FIG. 4 and FIG. 5.

First, when using oceanographic data generated by forecasts and statistics, differences can occur between the actual sound propagation conditions and those predicted using oceanographic data. Figure 4 shows a case where a difference occurs between the forecast path, which is the sound propagation path calculated from forecast information, and the actual path, which is the sound propagation path derived from actual measurements. In this case, when a difference occurs in the propagation path, a difference occurs between the forecast reverberation peak included in the reverberation level forecast value and the actual reverberation peak obtained from the actual measurement results, as shown in the upper diagram of Figure 5. Figure 5 shows a case where the propagation path of the actual measurement value is longer than the forecast value. Therefore, in the example shown in Figure 5, the time to receive the reverberation peak is delayed by the path difference in the propagation path.

Here, we will explain the process of estimating the reverberation peak arrival time using sensors A to C shown in Figure 1. Specifically, we will explain the process of estimating the reverberation peak arrival time at the position of sensor C, assuming that sensors A and B are existing sensors. First, the path difference calculation unit 33 measures the reception levels of sensors A and B, and calculates the time when the reverberation level reaches its peak. Next, the path difference calculation unit 33 recognizes the reverberation arrival time calculated by prediction based on the reverberation level forecast value RL1, calculates the time difference between the actual reverberation peak and the forecast reverberation peak, and calculates the path difference ratio with respect to the distance. Then, the path difference calculation unit 33 outputs the calculated path difference ratio as path difference information RTd. This path difference information RTd is used by the reverberation level correction processing unit 25.

The reverberation level correction processing unit 25 corrects the reverberation level forecast value RL1 based on the path difference information RTd to calculate the reverberation level correction value RL2. More specifically, the reverberation level correction processing unit 25 multiplies the reverberation level arrival time of the sensor C's predicted reverberation level in the reverberation level forecast value RL1 by the path difference ratio to correct the peak time.

The reverberation level correction processing unit 25 may use methods other than the method using the updated propagation loss (e.g., the propagation loss replacement value TL2). For example, the reverberation level at any position may be calculated by interpolating data from the reception levels of existing sensors. In addition, when the target speed is known or the target speed is determined from the frequency analysis results (spectrogram, etc.), the reverberation level correction process may be a method of measuring the masking level value ML from the time series data of the frequency band matching the speed and estimating the reverberation level RL. This method can further improve the accuracy of the reverberation level by taking the speed into account.

Here, we will explain the method of estimating the reverberation level from the masking level value ML. Figure 6 shows a diagram explaining the method of calculating the masking level at the target assumed speed specified by the operator. As shown in Figure 6, in this method, a spectrum analysis is performed on the input signal Si. Then, the reverberation level RL can be estimated by measuring the masking level value ML from the time series data of the frequency band that matches the relative speed between the target object and the receiver R.

The masking level calculation unit 26 selects the larger of the noise level values calculated from the reverberation level correction value RL2 and the noise level replacement value NL2 (for example, the noise level actual measurement information NLe), and outputs the selected value as the masking level value (for example, the masking level calculation value ML1). Here, the masking level value ML1 differs depending on the following two cases. When noise is dominant as interference noise (noise-limited state), the masking level value ML = noise level NL - receiver directional gain DI. In other words, in this noise-limited state, the masking level value ML is the noise level actual measurement information NLe that has already taken into account the receiver directional gain DI from the noise level NL. On the other hand, when reverberation is dominant as interference noise (reverberation-limited state), the masking level value ML = reverberation level correction value RL2. In general, the reverberation level is dominant immediately after the transmitter T oscillates, and the noise level is dominant after a certain time has passed.

When the magnitude of the input signal Si indicated by the noise level measurement information NLe indicates a period during which direct waves are being received, the masking level replacement processing unit 27 replaces the output of the masking level calculation unit 26 with the noise level measurement information NLe and outputs the masking level value ML2. In other words, the masking level value ML2 is a value equivalent to the noise level measurement information NLe when direct waves are being measured, and is the masking level calculation value ML1 when the noise level measurement information NLe is not a period during which direct waves are being measured.

The echo level calculation unit 28 calculates an echo level value EL for the underwater acoustic signal in the search area based on the propagation loss estimate (e.g., the propagation loss replacement value TL2) and the source level value SL indicating the output level of the transmitter T. The echo level value EL1 is calculated as EL1 = SL - TLa - TLb + TS, where SL is the source level value, TS is the target strength that is the strength at which the reflection band reflects sound, TLa is the propagation loss on the outbound path, and TLb is the propagation loss on the return path.

Here, we will explain the calculation of the signal excess value in the signal excess calculation unit 14. In a multistatic sonar device, the signal excess calculation unit 14 calculates the signal excess value SE1 by calculating SE1 = EL1 - ML2 - DT, where SE1 is the signal excess value, ML2 is the masking level value, and DT is the detection threshold at which the operator can visually recognize reflected sound.

The underwater object search support device 1 according to the first embodiment calculates the detection distance characteristic information SR using the signal excess value SE1, and at this time, the detection distance characteristic information SR is calculated taking into account the standard deviation SD. Here, the operation of the standard deviation calculation unit 34 that calculates the standard deviation SD and the detection probability calculation unit 15 that calculates the detection distance characteristic information SR will be described.

First, the standard deviation calculation unit 34 has a peak exclusion determination unit 35. The standard deviation calculation unit 34 receives the time-series noise level actual measurement information NLe calculated by the noise level actual measurement unit 32, and calculates its standard deviation SD. Here, since the noise level actual measurement information NLe varies over time, in order to calculate an accurate detection probability, it is necessary to find the standard deviation SD and take it into account in the calculation of the detection probability.

Here, a graph illustrating the difference in the signal excess value and detection probability characteristics due to standard deviation is shown in FIG. 7. The effect of standard deviation on detection probability will be described with reference to FIG. 7. FIG. 7 shows the characteristics of the detectable probability P when the standard deviation of the noise level is σ=0 dB, 3 dB, and 6 dB. If received data from the reverberation-limited area is added when calculating the standard deviation SD, the standard deviation SD becomes extremely large, lowering the accuracy of the detectable probability P. Therefore, the standard deviation calculation unit 34 in the first embodiment is configured to improve the accuracy of the standard deviation SD by providing a peak exclusion determination unit 35 and excluding peaks in advance.

The processing of the peak exclusion determination unit 35 will be described. The peak exclusion determination unit 35 measures the difference in level between the noise level actual measurement information NLe corresponding to the reception level of the input signal Si for each time and the noise level forecast value for the search area, performs threshold processing, and excludes data for times when the difference is too large from the calculation targets. The standard deviation calculation unit 34 then calculates the standard deviation SD from the noise level actual measurement information NLe for the area determined to be excluded. The detection probability calculation unit 15 then uses this standard deviation SD to output the detection distance characteristic information SR.

Next, the sensor interval calculation unit 16 will be described. FIG. 8 shows a graph illustrating the relationship between the detectable distance and overlap rate of the sensors. The sensor interval calculation unit 16 calculates the sensor interval that results in the overlap rate designated by the operator using the detection distance characteristic information SR. Here, the overlap rate is a value indicating how much the distance between adjacent sensors overlap with the detectable distance as the denominator, and is calculated using formula (1). Here, the detectable distance is indicated by the detection distance characteristic information SR.
Overlap rate = 1 - ((sensor distance - detectable distance) / detectable distance) ... (1)

In the example shown in Figure 8, the upper diagram shows an example of a sensor arrangement where an overlap rate of 75% is specified, and the lower diagram shows an example of a sensor arrangement where an overlap rate of 12.5% is specified. Also, in the example shown in Figure 8, the detectable distance, which is the coverage range of one sensor, is set to 1.6 nautical miles (1.6 NM) based on the detection distance characteristic information SR calculated using the underwater object search support device 1. As shown in Figure 8, by changing the overlap rate, it is possible to set the inter-sensor distance to 2 NM or 3 NM. The sensor interval calculation unit 16 calculates this inter-sensor distance based on the overlap rate and the detection distance characteristic information SR.

From the above explanation, in the underwater object search support device 1 according to the first embodiment, the estimated value of the sonar parameters used in the prediction unit 11 is calculated from the characteristics of the underwater acoustic signal in the search area acquired using the transmitter T and receiver R in the search area where the sensor is laid. Then, in the underwater object search support device 1 according to the first embodiment, the predicted values used to calculate the echo level value EL1 and the masking level value ML2 calculated by the prediction unit 11 are replaced or corrected by the estimated values calculated by the sonar parameter estimation unit 13. By using the values calculated in this way to calculate the signal excess value SE1 and the detection distance characteristic information SR, the underwater object search support device 1 can reflect the propagation characteristics of the underwater acoustic signal in the search area in the calculation results, thereby improving the accuracy of the sensor's detectable distance.

In addition, in the underwater object search support device 1 according to the first embodiment, the sonar parameters used in the underwater object search support device 1 are simply replaced with values obtained by actual measurement, so that it is possible to prevent the calculation time using the prediction unit 11 from becoming long.

The present invention is not limited to the above embodiment, and can be modified as appropriate without departing from the spirit and scope of the invention.

REFERENCE SIGNS LIST 1 Underwater object search support device 10 Database 11 Prediction unit 12 Input signal processing unit 13 Sonar parameter estimation unit 14 Signal excess calculation unit 15 Detection probability calculation unit 16 Sensor interval calculation unit 17 Display unit 20 Propagation loss calculation unit 21 Propagation loss replacement processing unit 22 Noise level calculation unit 23 Noise level replacement processing unit 24 Reverberation level calculation unit 25 Reverberation level correction processing unit 26 Masking level calculation unit 27 Masking level replacement processing unit 28 Echo level calculation unit 30 Propagation loss estimation unit 31 Reverberation level determination unit 32 Noise level actual measurement unit 33 Path difference calculation unit 34 Standard deviation calculation unit 35 Peak exclusion determination unit Di Input data Si Input signal TL1 Propagation loss forecast value TL2 Propagation loss replacement value RL1 Reverberation level forecast value RL2 Reverberation level correction value NL1 Noise level forecast value NL2 Noise level replacement value TLe Propagation loss estimate value RLe Reverberation level judgment information RTd Path difference information NLe Noise level actual measurement information SD Standard deviation EL1 Echo level value ML1 Masking level calculation value ML2 Masking level value SE1 Signal excess value SR Detection distance characteristic information AS Estimated target speed information SL Transmitting wave level information T Transmitter R Receiver

Claims (8)

a sonar parameter estimator that calculates a propagation loss estimate, which is an estimate of the propagation loss of the underwater acoustic signal in the search area , by subtracting a signal level of an input signal generated by receiving an underwater acoustic signal with a receiver from a source level value indicating the strength of an underwater acoustic signal output by a transmitter installed in the search area, and that calculates path difference information indicating the amount of path difference between the actual measurement result and the forecast result of sound wave propagation, actual noise level information including time series information of the input signal, and standard deviation of the noise level of the input signal;
a prediction unit which outputs a value obtained by replacing a propagation loss forecast value calculated based on input data including at least oceanographic data, bottom topography data, and sensor specification information related to the search sea area with the propagation loss estimated value as a masking level value, outputs the noise level measurement information as a masking level value when the magnitude of the input signal indicated by the noise level measurement information indicates a period in which a direct wave is being received, and outputs an echo level value related to the underwater acoustic signal in the search sea area ;
a signal excess calculation unit that calculates a signal excess value based on the echo level value and the masking level value;
a detection probability calculation unit that calculates detection distance characteristic information indicating a distance characteristic of a detection probability based on the signal excess value and the standard deviation of the noise level;
a sensor interval calculation unit that calculates an installation interval of the sensors based on the detection distance characteristic information and a search range overlap rate that indicates the extent to which the distance between adjacent sensors overlaps with the detectable distance as a denominator ;
An underwater object search support device having: The prediction unit is
a reverberation level calculation unit that calculates a reverberation level forecast value that is a forecast value of a reverberation level of the underwater acoustic signal based on a propagation loss estimate value indicating the estimated value of the propagation loss;
a reverberation level correction processing unit that corrects the reverberation level forecast value based on path difference information indicating an estimated value of the path difference to calculate a reverberation level corrected value;
a noise level calculation unit that calculates a noise level forecast value that is a forecast value of the noise level based on the input data;
a noise level replacement processing unit that replaces the noise level forecast value with the noise level actual measurement information;
a masking level calculation unit that selects the larger value of the reverberation level correction value or the noise level actual measurement information, and outputs the selected value as the masking level value;
an echo level calculation unit that calculates the echo level value related to the underwater acoustic signal in the search area based on the propagation loss estimation value and a source level value indicating an output level of the transmitter;
2. The underwater object search support device according to claim 1, further comprising: The sonar parameter estimation unit
a propagation loss estimation unit that outputs the propagation loss estimation value by subtracting a signal level of the input signal from a source level value that indicates an intensity at which the underwater acoustic signal is output by the transmitter;
a reverberation level determination unit that determines a period during which the underwater acoustic signal propagates at a reverberation level as a reverberation area period based on a signal level of the input signal, and outputs reverberation level determination information indicating the reverberation area period;
a path difference calculation unit that calculates, as the path difference information , a path difference ratio to a distance calculated from a time difference between an actual reverberation peak determined from the reverberation level determination information and a predicted reverberation peak determined from the reverberation level predicted value; and
a noise level measurement unit that outputs noise level actual measurement information, which is time-series information of the input signal, based on the input signal;
a standard deviation calculation unit that calculates the standard deviation of the noise level of the input signal based on the noise level actual measurement information;
3. The underwater object search support device according to claim 2, further comprising: The prediction unit is
4. The underwater object search support device according to claim 3, further comprising a masking level substitution processing unit that substitutes the output of the masking level calculation unit with the noise level actual measurement information and outputs the masking level value when the magnitude of the input signal indicated by the noise level actual measurement information indicates a period during which direct waves are being received. The underwater object search support device according to claim 3, wherein the standard deviation calculation unit has a peak exclusion determination unit that excludes information on the reverberation region period from the noise level actual measurement information, and calculates the standard deviation based on the noise level actual measurement information excluding the reverberation region period. The prediction unit is
a propagation loss calculation unit that calculates a propagation loss forecast value that is a forecast value of the propagation loss of the underwater acoustic signal in the search area based on the input data;
6. The underwater object search support device according to claim 2, further comprising: a propagation loss replacement unit that replaces the propagation loss forecast value with the propagation loss estimated value. a prediction unit that calculates an echo level value and a masking level value of an underwater acoustic signal based on input data including at least oceanographic data, bottom topography data, and sensor specification information related to a search area;
a signal excess calculation unit that calculates a signal excess value based on the echo level value and the masking level value;
a detection probability calculation unit that calculates a distance characteristic of a detection probability based on the signal excess value;
a sensor interval calculation unit that calculates an installation interval of sensors based on a search range overlap rate indicating an overlap rate between adjacent sensors, using the distance characteristic of the detection probability and the detectable distance as a denominator, the sensor interval calculation unit comprising:
A propagation loss estimate is calculated by subtracting the signal level of an input signal generated by receiving an underwater acoustic signal by a receiver from a source level value indicating the strength at which an underwater acoustic signal is output by a transmitter installed in the search area, and also calculating path difference information indicating the amount of path difference between the actual measurement result and the forecast result of sound propagation, actual noise level information including time series information of the input signal, and standard deviation of the noise level of the input signal.
In the prediction unit,
calculating a reverberation level forecast value using the propagation loss estimate value when calculating the echo level value and the masking level value;
correcting the reverberation level forecast value using the path difference information to calculate a reverberation level corrected value;
Calculating a noise level forecast value, which is a forecast value of the noise level, based on the input data;
replacing the noise level forecast value with a noise level estimate value that is an estimate of the noise level;
selecting the larger of the reverberation level correction value and the noise level estimation value, and outputting the selected value as the masking level value;
calculating an echo level value for the underwater acoustic signal in the search area based on the propagation loss estimate value and a source level value indicating an output level of the transmitter;
In the detection probability calculation unit,
An underwater object search support method in an underwater object search support device, the method comprising the steps of: calculating the distance characteristic of the detection probability by adding the standard deviation of the noise level to the signal excess value. An underwater object search program that is executed on a computing device and calculates an installation interval of sensors based on input data including at least oceanographic data, bottom topography data, and sensor specification information related to a search area, and an input signal generated by receiving, by a receiver, an underwater acoustic signal emitted from a transmitter installed in the search area, the program comprising:
a sonar parameter estimation process for calculating a propagation loss estimate , which is an estimate of the propagation loss of the underwater acoustic signal in the search area, by subtracting the signal level of the input signal from a source level value indicating the intensity of outputting the underwater acoustic signal, and calculating path difference information indicating the amount of path difference between the actual measurement result and the forecast result of sound wave propagation, actual noise level information including time series information of the input signal, and a standard deviation of the noise level of the input signal;
a prediction process for outputting a value obtained by replacing a propagation loss forecast value calculated based on the input data with the propagation loss estimated value as a masking level value, outputting the noise level measurement information as a masking level value when the magnitude of the input signal indicated by the noise level measurement information indicates a period in which a direct wave is being received, and outputting an echo level value related to the underwater acoustic signal in the search area;
a signal excess calculation process for calculating a signal excess value based on the echo level value and the masking level value;
a detection probability calculation process for calculating a distance characteristic of a detection probability based on the signal excess value and the standard deviation of the noise level;
a sensor interval calculation process for calculating an installation interval of the sensors based on the distance characteristic of the detection probability and a search range overlap rate indicating the extent to which the distance between adjacent sensors overlap, with the detectable distance as a denominator;
An underwater object search support program.

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