JP2008275339A - Target detection device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To perform signal processing capable of detecting a target, even when SNR (Signal-to-Noise Ration) after coherent integration is extremely low. <P>SOLUTION: This target detection device is equipped with a receiving means 1 for receiving a reflected pulse from the target, and performing A/D conversion of reception data in each pulse hit; a signal reproduction means 2 for adding a noise series to a receiving video comprising a data series of the reception data after A/D conversion, and reproducing a signal; a coherent integration means 3 for performing FFT processing in the pulse hit direction to the receiving video after signal reproduction; and a threshold means 6 for comparing an integration result by the coherent integration means 3 with a prescribed threshold, and determining as target detection when exceeding the threshold. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a target detection apparatus, and more particularly, to a target detection apparatus for receiving data received by a radar or a sensor and detecting a target with weak received power.

  In the conventional target detection apparatus, for signal detection, in integration processing within 1 CPI (Coherent Pulse Interval), coherent integration (FFT: Fast Fourier Transform) processing is performed in the pulse hit direction, and in integration processing between CPIs Non-coherent integration (integration after amplitude detection) of a plurality of FFT results. In the non-coherent integration, the transition of the signal processing cell occurs depending on the target motion, and an integration path is searched in consideration of the movement amount. After searching for the integration path, the amount of movement of the signal processing cell is compensated during non-coherent integration, and the target signal can be extracted by non-coherent integration (see, for example, Non-Patent Document 1).

  The CPI means a time interval in which the target motion can be regarded as a constant velocity linear motion, and the phase change of the target signal can be regarded as linear in this section, and an integration process considering the phase can be performed. .

  In a conventional target detection apparatus, a search process for a target integration path is performed in the integration process between CPIs. That is, the transition of the signal processing cell between CPI (Coherent Pulse Interval) as shown in FIG. 3 is searched according to the evaluation function shown by the following formula (1) (hereinafter, the evaluation function is called track score). Note that coherent integration (FFT) is performed on received data within 1 CPI. On the other hand, the integration process between CPIs is non-coherent integration.

Here, A _k is a collection of signal processing cells in CPI k shown in FIG.
In Equation (1), the first term on the right side represents the track score increment from CPI k-1 to k, and the second term on the right side represents the track score from CPI k-1. Therefore, the track score up to CPI k is obtained by calculating the track score increment from each CPI to the next CPI and accumulating it.

  In the conventional target detection apparatus, the transition of the detection cell when each CPI reaches the next CPI is evaluated by Expression (2), and the transition that maximizes or minimizes Expression (2) is extracted. The integration path is searched by repeating this process up to CPI n shown in FIG.

  After the target integration path search, the target signal can be extracted by performing non-coherent integration processing according to the integration path.

M. F. Fernandez, A. Aridgides and D. Bray: “Detecting and Tracking Low-Observable Targets Using IR,” SPIE Vol. 1305 Signal and Data Processing of Small Targets, 1990

  As described above, the target integration path at the time of non-coherent integration is extracted by repeating the search of the integration path between the CPIs based on the track score by the configuration of the conventional target detection apparatus. However, when the SNR (Signal-to-Noise Ration) after coherent integration is extremely low, for example, when the SNR is 0 or less, it is extremely difficult to extract a target integration path. There was a problem of becoming. For this reason, integration processing capable of signal detection even when the signal-to-noise power ratio is extremely low is desired.

  The present invention has been made to solve such a problem, and is capable of performing target detection even when the signal-to-noise ratio (SNR) after coherent integration is extremely low. An object of the present invention is to obtain a target detection apparatus that performs the above.

  The present invention provides a receiving means for receiving a reflected pulse from a target and A / D converting received data for each pulse hit, and a received video that is a data series of the received data after A / D conversion. In addition, the signal reproduction means for reproducing the signal, the coherent integration means for performing FFT processing in the pulse hit direction on the received video after the signal reproduction, and the integration result of the coherent integration means are compared with a predetermined threshold value. And a threshold detecting means for determining as target detection when the threshold is exceeded.

  The present invention provides a receiving means for receiving a reflected pulse from a target and A / D converting received data for each pulse hit, and a received video that is a data series of the received data after A / D conversion. In addition, the signal reproduction means for reproducing the signal, the coherent integration means for performing FFT processing in the pulse hit direction on the received video after the signal reproduction, and the integration result of the coherent integration means are compared with a predetermined threshold value. In addition, since the target detection apparatus includes a threshold means for determining target detection when the threshold value is exceeded, the SNR (Signal-to-Noise Ratio) after coherent integration is extremely low. The signal processing that can detect the target can be performed.

Embodiment 1 FIG.
Hereinafter, the target detection apparatus according to the first embodiment of the present invention will be described with reference to the drawings.
FIG. 1 shows the configuration of the target detection apparatus according to the first embodiment. The target detection apparatus includes a reception unit 1, a signal reproduction unit 2, a coherent integration unit 3, and a threshold unit 6. Since this embodiment has a feature in the process of searching for the integral path, the description of the reception process including the sensor is omitted.

  In FIG. 1, a receiving means 1 receives a reflected pulse from a target from which a pulse signal emitted toward a target from a pulse generator (not shown) is reflected by the target and returns to each target. Received data is A / D converted. The signal reproduction means 2 adds a noise sequence for a plurality of pulse hits to a received video composed of a data sequence of received data after A / D conversion, and takes an average of the signals after the addition of noise. . The coherent integration unit 3 performs FFT (Fast Fourier Transform) processing in the pulse hit direction on the received video after the signal reproduction processing that is the output of the signal reproduction unit 2. Further, the threshold means 6 compares the integration result of the coherent integration means 3 with a preset threshold value, and performs a determination that the signal processing cell whose integration result exceeds the threshold is regarded as target detection.

  FIG. 2 is a block diagram showing the configuration of the signal reproduction means 2. FIG. 3 illustrates the flow of signal reproduction processing by the signal reproduction means 2. As shown in these drawings, first, the receiving means 1 outputs a received video after A / D conversion for each pulse hit. This received video is sampled at a constant sampling period, and the sampling number is set to L. The data for each sample is called a range bin, and the range bin includes amplitude data of received video I (real part) and Q (imaginary part) for each number. The signal reproduction means 2 collects received video for each pulse hit for 1 CPI, extracts data for the pulse hit for each range bin number, and rearranges the data series into M pulse hits for each range bin number. The signal reproduction means 2 generates N types of noise sequences corresponding to pulse hits, and adds and averages the data sequences of received video after rearrangement for each of the N types of noise sequences.

The operation will be described below.
The signal reproduction means 2 adds a plurality of noise sequences for pulse hits to the received video after A / D conversion to the signal, and takes the addition average of the signals after the noise addition.
The reception means 1 outputs the received video after A / D conversion for each pulse hit as shown in FIG. This received video is sampled at a constant sampling period, and the sampling number is set to L. The data for each sample is called a range bin, and the range bin includes amplitude data of received video I (real part) and Q (imaginary part) for each number.
In the signal reproduction means 2, first, received video for each pulse hit for 1 CPI is collected and stored in the memory 201 shown in FIG.
Next, the received video rearrangement unit 202 retrieves data corresponding to pulse hits for each range bin number from the memory 201, and rearranges the data series into M pulse hits for each range bin number. As a result, the data sequence of the received video is as shown in FIG.
Further, the noise generation unit 203 randomly generates N types of noise sequences corresponding to pulse hits. These N types of noise sequences are added to the data sequence of the received video after rearrangement, which is the output of the received video rearranging unit 202, by the noise adding unit 204 for each noise sequence. This addition processing is performed for each noise sequence as shown in FIG. 3C, and the addition result for each noise sequence is input to the addition averaging unit 205 in FIG. As a result, as shown in FIG. 3D, the signal after the averaging is taken out and output to the coherent integration means 3.

  The signal reproduction process is performed for each range bin number. At this time, a different noise sequence may be generated for each range bin number or may be shared.

  The coherent integration unit 3 inputs the received video after signal reproduction, which is the output of the signal reproduction unit 2, and performs FFT processing in the pulse hit direction.

  The above processing is performed for each range bin number. This process is applied to all range bins.

  As described above, in the present embodiment, the receiving means 1 that receives the reflected pulse from the target and A / D-converts the received data for each pulse hit, and the received video after the A / D conversion has a noise sequence. A signal reproduction means 2 for adding and reproducing the signal, a coherent integration means 3 for performing FFT processing in the pulse hit direction, and a threshold value means 6 for performing threshold processing on the integration result of the coherent integration means 3 and detecting a target are provided. Since a random noise sequence is added in the signal reproduction means 2 before processing, and coherent integration processing is performed after averaging, SNR (Signal-to-Noise Ration) after coherent integration is performed. Even when the value is extremely low, signal processing capable of target detection can be performed.

Embodiment 2. FIG.
FIG. 4 shows the configuration of the target detection apparatus according to the second embodiment. The target detection apparatus includes a receiving unit 1, a signal reproduction process 2, a coherent integration unit 3, an integration path search unit 4, a non-coherent integration unit 5, and a threshold processing unit 6. Since this embodiment has a feature in the process of searching for the integral path, the description of the reception process including the sensor is omitted.

  In FIG. 4, the receiving means 1 performs the same processing as in the first embodiment. Further, the signal reproduction means 2 adds a plurality of noise sequences corresponding to pulse hits to the received video after A / D conversion, and takes the average of the signals after the noise addition.

FIG. 3 shows the flow of processing in the signal reproduction means 2.
The reception means 1 outputs a received video after A / D conversion for each pulse hit.
This received video is sampled at a constant sampling period, and the sampling number is set to L. The data for each sample is called a range bin, and the range bin includes amplitude data of received video I (real part) and Q (imaginary part) for each number.
The signal reproduction means 2 collects received video for each pulse hit for 1 CPI, extracts data for the pulse hit for each range bin number, and rearranges the data series into M pulse hits for each range bin number.
The signal reproduction means 2 generates N types of noise sequences corresponding to pulse hits, and adds and averages the data sequences of received video after rearrangement for each of the N types of noise sequences.
The difference between the second embodiment and the first embodiment is that the number of noise sequences to be added is lowered. In order to prevent frequent false alarms from occurring as an adverse effect, the present embodiment employs a configuration in which false alarms are suppressed by non-coherent integration means.

In FIG. 4, the coherent integration unit 3 performs FFT processing in the pulse hit direction on the received video after the signal reproduction process that is the output of the signal reproduction unit 2.
FFT processing is performed in CPI units, and non-coherent integration is performed between CPIs. However, if the FFT results are integrated between the same signal processing cells, the signal processing cells may fluctuate as shown in FIG. If it occurs at, integration loss occurs. Therefore, the detection cell is searched by the integration path search means before the non-coherent integration process so that there is no integration loss.

  The integration path search means 4 receives the output of the coherent integration means 3 and searches for an integration path in consideration of fluctuations of the detection cell and target movement. That is, the integration path search means 4 obtains a plurality of transitions of the signal processing cell based on the integration result of the coherent integration means 3, and has the best evaluation value evaluated by a predetermined evaluation function from the plurality of transitions. Is extracted as a target integration path to be detected. Below, the internal structure of the integration path search means 4 is demonstrated.

In the integration path search means 4, 401 is an amplitude detector, which calculates an amplitude value for each signal processing cell after coherent integration. The result is stored in the first memory 402 for each CPI.
Reference numeral 403 denotes an integration path search unit, which sets an integration path in consideration of the possibility of transition between CPIs for each signal processing cell as shown in FIG. In FIG. 6, the vertical direction represents the signal processing cell and is the result of the FFT obtained by the coherent integration means 3. Here, the number of FFT points is set to M. The horizontal direction represents the CPI number. The integration path search unit 403 considers transitions between CPIs of all signal processing cells as shown in FIG.
An integration path evaluation unit 404 evaluates the integration search path set by the integration path search unit 403. The integration path is evaluated by the track score, but the highest signal processing cell of the track score is selected for the transition from the previous CPI to the signal processing cell in the current CPI. The selected cell is stored in the second memory 407 for each CPI and signal processing cell. The processing of the integration path evaluation unit 404 is performed for the number of CPIs to be non-coherently integrated.
Reference numeral 405 denotes an amplitude probability density function setting unit, which defines a probability density function for a target expected amplitude value to be used in the integration path evaluation unit 404 based on a plurality of expected amplitude values composed of K expected amplitude values.
A signal processing cell transition probability setting unit 406 sets a probability of transition from each signal processing cell to the next signal processing cell for use in the integration path evaluation unit 404.
A state quantity update unit 408 calculates a state variable vector of each signal processing cell based on the output from the integral evaluation unit 404 and inputs the state variable vector to the signal processing cell transition probability setting unit 406.
Reference numeral 409 denotes an integration path extraction unit that follows the CPI and the integration path for each signal processing cell stored in the second memory 407 in the reverse direction, and extracts the integration path at the time of non-coherent integration.

  The movement compensation type non-coherent integration means 5 inputs an integration path which is an output of the integration path search means 4, compensates the integration result of the coherent integration means 3 based on the integration path, and transmits the compensated integration result between the CPIs. Perform non-coherent integration with.

  The threshold means 6 receives the integration result that is the output of the movement compensation type non-coherent integration means 5 and performs threshold processing. That is, a determination is made that a signal processing cell in which the integration result of non-coherent integration exceeds a threshold value is regarded as target detection.

The operation will be described below.
The signal reproduction means 2 adds a plurality of noise sequences for pulse hits to the received video after A / D conversion to the signal, and takes the addition average of the signals after the noise addition.

FIG. 2 shows the configuration of the signal reproduction means 2. FIG. 3 shows the flow of processing in the signal reproduction means 2.
The reception means 1 outputs the received video after A / D conversion for each pulse hit as shown in FIG. This received video is sampled at a constant sampling period, and the sampling number is set to L. The data for each sample is called a range bin, and the range bin includes amplitude data of received video I (real part) and Q (imaginary part) for each number.

In the signal reproduction means 2, first, received video for each pulse hit for 1 CPI is collected and stored in the memory 201 shown in FIG.
Next, the received video rearrangement unit 202 extracts data corresponding to pulse hits for each range bin number, and rearranges the data series into M pulse hits for each range bin number. As a result, the data sequence of the received video is as shown in FIG.
Further, the noise generation unit 203 generates N types of noise sequences corresponding to pulse hits. N types of noise sequences are added to the data sequence of the received video after rearrangement, which is the output of the received video rearrangement unit 202, by the noise addition unit 204 for each noise sequence. This addition processing is performed for each noise sequence as shown in FIG. 3C, and the addition result for each noise sequence is added and averaged by the addition averaging unit 205 of FIG. As a result, as shown in FIG. 3D, the signal after the averaging is taken out and output to the coherent integration means 3.

  The signal reproduction process is performed for each range bin number. At this time, a different noise sequence may be generated for each range bin number or may be shared.

  The coherent integration unit 3 inputs the received video after signal reproduction, which is the output of the signal reproduction unit 2, and performs FFT processing in the pulse hit direction.

The integration path search means 4 inputs the output of the coherent integration means 3 and searches for the integration path of the target signal at the time of non-coherent integration in consideration of a plurality of transitions when considering the transition of the signal processing cell.
First, the amplitude detection unit 401 calculates the amplitude value A _k (j) from the coherent integration result V _k (j) for each signal processing cell as shown in the following equation (3). The result is stored in the first memory 402 for each CPI.
The integration path search unit 403 searches for an integration path considering movement between CPIs for each signal processing cell as shown in FIG. 6, and the integration path evaluation unit 404 evaluates the search path.
The track score shown in the following formulas (4) and (5) is used for evaluation of the search path.
Here, A _k is a collection of signal processing cells in CPI k shown in FIG.
In Equation (4), the first term on the right side represents the track score increment from CPI k-1 to k, and the second term on the right side represents the track score from CPI k-1. Therefore, the track score up to CPI k is obtained by calculating the track score increment from each CPI to the next CPI and accumulating it.
In the formula (5), the first term on the right-hand side target signal processing cell A _k-1 probability of transition to A _k, the second term on the right side of the received signal when the target is present in the theta _k amplitude value z _k The probability density (calculated from the target amplitude model) and the likelihood ratio of the probability density of the receiver noise amplitude value.

White circles in FIG. 6 indicate signal processing cells. The vertical direction means a signal processing cell number (FFT point number), and the horizontal direction means a CPI number.
For example, consider a transition from CPI number 1 to CPI number 2 and signal processing cell number 1 indicated by a thick white circle.
Three arrows are connected to the signal processing cell from the signal detection cell of CPI number 1. This arrow indicates that there is a possibility of transition from CPI number 1 to CPI number 2 and signal processing cell number 1 indicated by thick white circles.
The integration path evaluation unit 404 evaluates the transition represented by these three arrows using equations (4) and (5). As a result, the transition from the signal processing cell having the maximum (or minimum) track score is recorded in the second memory 407 as the transition from CPI number 1 to the CPI number 2 signal processing cell 1 as the best transition. . This processing is applied to all signal processing.
Here, the second term on the right side of Equation (5) represents the likelihood ratio with respect to the amplitude of the signal processing cell, and the larger this is, the higher the possibility of being the target.

In the conventional signal detection apparatus, a unique probability density function is defined based on the target expected amplitude value, but in this embodiment of the present invention, the expected amplitude value is unknown by the signal reproduction process.
Therefore, as shown in Equation (6), the probability density function p (z _k | A _k for the target amplitude expected value is based on a plurality of expected amplitude values including K expected amplitude values by the amplitude probability density function setting unit 405. ) Is defined. Further, the first term on the right side of the equation (5) represents the probability that the target shifts from the signal processing cell A _k−1 to A _k , but in this embodiment of the present invention, the equations (7) and (8) As shown, the transition probability P (A _k | A _k−1 ) is calculated by the transition probability setting unit 406 between signal processing cells.
In Equation (7), P ₀ is a set parameter, and L is the maximum number of signal processing cell transitions to the next CPI determined in consideration of the target motion.
As a method of setting L, for example, it is determined in consideration of the target acceleration as shown in Equation (8).
In Expression (8), a _max is a target maximum acceleration, and Δa is an acceleration resolution in signal processing.

The transition probability is set as follows for the second and subsequent CPI.
As a result of obtaining the transition from CPI number 1 to 2 for each signal processing cell, the state variable vector x _{k, j of} each signal processing cell can be derived by the state quantity update unit 408 as shown in Expression (9).
After obtaining the state variable vector x _{k, j} by the equation (9) _, the prediction process of the state variable vector x _{k + 1, l} ahead of ₁ CPI is performed by the equation (10).
As a result, the prediction transition A _{k + 1} (l) of the signal processing cell from the CPI number k to the CPI number (k + 1) is determined by the equation (12).
This result is returned to the transition probability setting unit 406 between signal processing cells, and the transition probability P (A _k | A _k−1 ) is defined as in Expression (14).

  When the processing is completed up to the CPI number n, the CPI number n and the signal processing cells 1 to M are referred to, and the signal processing cell whose value is a certain value or more is extracted. Here, when there are a plurality of signal processing cells extracted at the CPI number n, it means that there are a plurality of targets.

  The integration path extraction unit 409 refers to the transition information recorded in the second memory 407 with respect to the signal processing cell in which the track score is extracted as a certain value or more in the CPI number n, and the track score moves in the reverse direction of the integration path. The target integration path at the time of non-coherent integration is extracted.

The movement compensation type non-coherent integration means 5 inputs the integration path that is the output of the integration path search means 4 and performs non-coherent integration that compensates for the transition of the signal processing cell between the CPIs.
FIG. 7 shows a signal processing cell for each CPI number. The vertical direction means a signal processing cell number (FFT point number), and the horizontal direction means a CPI number. The arrow in the figure represents the integration path extracted by the integration path search means 4.
The movement compensation type non-coherent integration means 5 refers to the extracted integration path and shifts the signal processing cell extracted at the final CPI number to match the signal processing cell at each CPI number as shown in FIG.
This process is repeated for the target number. The target number corresponds to the number of signal processing cells extracted at the CPI number n in the integration path search means 3.

  The above processing is performed for each range bin number. Apply processing to all range bins.

  The integration result of the movement compensation type non-coherent integration unit 5 is input to the threshold unit 6 and subjected to threshold processing. That is, the input integration result is compared with a preset threshold value, and the threshold value unit 6 determines that the signal processing cell exceeding the threshold value is regarded as target detection.

  As described above, in the present embodiment, the receiving means 1 that receives the reflected pulse from the target and A / D-converts the received data for each pulse hit, and the received video after the A / D conversion has a noise sequence. In addition, the signal reproduction means 2 for reproducing the signal, the coherent integration means 3 for performing FFT processing in the pulse hit direction, and the coherent integration result are input, and when considering the transition of the signal processing cell, the target is taken into consideration An integration path search means 4 for searching the detection cells, and a movement compensation type non-coherent integration means 5 for integrating the coherent integration result after compensating the transition of the signal processing cell according to the integration path obtained by the integration path search means 4; In addition, it is difficult to detect a signal in the prior art because it includes a threshold processing unit 6 that performs threshold processing on the integration result of the movement compensation type non-coherent integration unit 5 and detects a target. Possible signal detected in the signal-to-noise power ratio Tsu. In addition, it is possible to reduce the processing load by setting the number of noise sequences required for signal reproduction processing to be low, and as a result, false alarms that are harmful are suppressed by movement compensation non-coherent integration processing. It is possible to accurately detect the target while preventing the occurrence of the problem.

Embodiment 3 FIG.
FIG. 9 shows the configuration of the target detection apparatus according to the third embodiment. Here, only the part which makes this invention the summary is demonstrated.
In FIG. 9, the integration path search means 7 receives the output of the coherent integration means 3 and searches for an integration path in consideration of fluctuations of the signal processing cell during non-coherent integration and the transition of the signal processing cell accompanying the target acceleration motion. .
Other configurations are the same as those in the second embodiment, and are denoted by the same reference numerals. In the following description, differences are mainly described, and description of the same operation is omitted.

In the integration path search means 7, reference numeral 701 denotes an amplitude detector, which calculates an amplitude value for each signal processing cell after coherent integration. The result is stored in the first memory 702 for each CPI.
Reference numeral 703 denotes an integration path search unit, which sets an integration path considering the possibility of transition between CPIs for each signal processing cell as shown in FIG.
In FIG. 6, the vertical direction represents the signal processing cell and is the result of the FFT obtained by the coherent integration means 3. Here, the number of FFT points is set to M. The horizontal direction represents the CPI number. The integration path evaluation unit considers transitions between CPIs of all signal processing cells as shown in FIG.

  Reference numeral 704 denotes an integral path evaluation unit that evaluates the integral search path set by the integral path search unit 703. The integration path is evaluated by the track score, but the signal processing cell at the top of the track score is selected for the transition from the previous CPI to the signal processing cell in the current CPI. The selected cell is stored in the second memory 707 for each CPI and signal processing cell. The processing of the integration path evaluation unit 704 is performed for the number of CPIs to be non-coherently integrated.

Reference numeral 705 denotes an amplitude probability density function setting unit that defines a probability density function for the amplitude of the signal processing cell to be used in the integration path evaluation unit 704 based on K expected amplitude values.
Reference numeral 706 denotes a signal processing cell transition probability setting unit, which sets a probability of transition from each signal processing cell to be used in the integration path evaluation unit 704 to the next signal processing cell.
A state quantity update unit 708 calculates a state variable vector of each signal processing cell based on an output from the integration path evaluation unit 704 and inputs it to the signal processing cell transition probability setting unit 706.

  Reference numeral 709 denotes an integration path extraction unit, which traces the integration path for each CPI and signal processing cell stored in the second memory 707 in the reverse direction, and extracts an integration path at the time of non-coherent integration.

  The operation will be described below.

The integration path search means 7 inputs the output of the coherent integration means 3 and searches for the integration path of the target signal during non-coherent integration. In the present embodiment, the target integration path is searched by evaluating the likelihood value of the amplitude value of the signal processing cell.
First, the amplitude detector 701 calculates the amplitude value A _k (j) from the coherent integration result V _k (j) for each signal processing cell as shown in the following equation (15). The result is stored in the first memory 702 for each CPI.
The integration path search unit 703 searches for an integration path considering movement between CPIs for each signal processing cell as shown in FIG. 6, and the integration path evaluation unit 704 evaluates the search path.
The track score shown in Equations (16) and (17) is used for evaluation of the search path.
Here, A _k is a collection of signal processing cells in CPI k shown in FIG.
In Expression (16), the first term on the right side represents the track score increment from CPI k-1 to k, and the second term on the right side represents the track score from CPI k-1. Therefore, the track score up to CPI k is obtained by calculating the track score increment from each CPI to the next CPI and accumulating it.
In Expression (17), the first term on the right side is the probability that the target will shift from the signal processing cell A _k−1 to A _k, and the second term on the right side is the probability of the amplitude value z _k of the received signal when the target exists at θ _k. It is the likelihood ratio of the density (calculated from the target amplitude model) and the probability density of the receiver noise amplitude value.

The circles in FIG. 6 indicate signal processing cells. The vertical direction means a signal processing cell number (FFT point number), and the horizontal direction means a CPI number.
For example, consider a transition from CPI number 1 to CPI number 2 and signal processing cell number 1 indicated by a thick white circle.
Three arrows are connected to the signal processing cell from the signal detection cell of CPI number 1. This arrow indicates that there is a possibility of transition from CPI number 1 to CPI number 2 and signal processing cell number 1 indicated by thick white circles.
The integration path evaluation unit 704 evaluates the transition represented by these three arrows using equations (16) and (17). As a result, the transition from the signal processing cell with the maximum (or minimum) track score is recorded in the second memory 707 as the transition from the CPI number 1 to the CPI number 2 signal processing cell 1 as the best transition. . This processing is applied to all signal processing.

Here, the second term on the right-hand side of Expression (17) represents the likelihood ratio with respect to the amplitude of the signal processing cell, and the larger this is, the higher the possibility of being the target.
In the conventional signal detection apparatus, a unique probability density function is defined based on the target expected amplitude value, but in this embodiment of the present invention, the expected amplitude value is unknown by the signal reproduction process.
Therefore, the probability density function p (z _k | A _k ) with respect to the amplitude of the signal processing cell is defined by the amplitude probability density function setting unit 705 based on K types of expected amplitude values as shown in Expression (18).
The first term on the right side of Equation (17) represents the probability that the target will transition from the signal processing cell A _k−1 to A _k , but is omitted because it is the same processing as in the first embodiment.

For the probability density function p (z _k | A _k ) defined by the amplitude probability density function setting unit 705 shown in Expression (18), the weight value εi is updated.
Probability density function update period A For each CPI, the amplitude value of the signal processing cell having the maximum track score value is referred to, and the probability density function p (z _k | A based on the K types of expected amplitude values shown in Expression (18) evaluate the likelihood of _k ).

The likelihood of the K kinds of probability density functions p (z _k | A _k ) is evaluated, and when the likelihood value is equal to or larger than the reference value B as shown in the equation (19), the weight for the probability density function is increased. That is, Δε is added to the weighting value εi.
On the other hand, when the likelihood value is less than the reference value B as in Expression (20), the weighting for the probability density function is reduced. That is, Δε is subtracted from the weighting value εi.
The value of Δε is a predetermined value set in advance, but may be determined as appropriate based on various conditions.

  By performing these processes for each ACPI process, the weighting for the probability density function is updated.

  As described above, in the present embodiment, as in the second embodiment, signal detection is possible even at a signal-to-noise power ratio that has been extremely difficult to detect in the past, and signal reproduction processing is also possible. By setting the required number of noise sequences low, the processing load can be reduced, and as a result, adverse false alarms are suppressed by the movement-compensated non-coherent integration process. The target can be detected. Further, in the present embodiment, the integration path evaluation unit 704 evaluates the likelihood of the amplitude value of the signal processing cell when evaluating the integration path, but the signal processing set by the amplitude probability density function setting unit 705 Since the weight of the expected value of the probability density function with respect to the cell amplitude is updated based on the result, the integration path can be more accurately evaluated, and an appropriate integration path search can be achieved.

Embodiment 4 FIG.
FIG. 10 shows the configuration of the target detection apparatus according to the fourth embodiment. Here, only the part which makes this invention the summary is demonstrated.
In FIG. 10, the integration path search means 8 receives the output of the coherent integration means 3 and searches for an integration path in consideration of the fluctuation of the signal processing cell during non-coherent integration and the transition of the signal processing cell accompanying the target acceleration motion. . In the present embodiment, the target integration path is searched by evaluating the likelihood value of the amplitude value of the signal processing cell.
Other configurations are the same as those in the second embodiment, and are denoted by the same reference numerals. In the following description, differences are mainly described, and description of the same operation is omitted.

In the integration path search means 8, reference numeral 801 denotes an amplitude detector, which calculates an amplitude value for each signal processing cell after coherent integration. The result is stored in the first memory 802 for each CPI.
Reference numeral 803 denotes an integration path search unit, which sets an integration path in consideration of the possibility of transition between CPIs for each signal processing cell as shown in FIG.
In FIG. 6, the vertical direction represents the signal processing cell, and is the result of the FFT obtained by the coherent integration means 3. Here, the number of FFT points is set to M. The horizontal direction represents the CPI number. The integration path evaluation unit considers transitions between CPIs of all signal processing cells as shown in FIG.
An integration path evaluation unit 804 evaluates the integration search path set by the integration path search unit 803. The integration path is evaluated by the track score, but the signal processing cell at the top of the track score is selected for the transition from the previous CPI to the signal processing cell in the current CPI.
The selected cell is stored in the second memory 807 for each CPI and signal processing cell.
The processing of the integration path evaluation unit is performed for the number of CPIs for non-coherent integration.

Reference numeral 805 denotes an amplitude probability density function setting unit that defines a probability density function with respect to the amplitude of the signal processing cell for use in the integration path evaluation unit 804 based on K expected amplitude values.
Reference numeral 806 denotes a signal processing cell transition probability setting unit that sets a probability of transition from each signal processing cell to the next signal processing cell for use in the integration path evaluation unit 804.
A state quantity update unit 808 calculates a state variable vector of each signal processing cell based on the output from the integration path evaluation unit 804 and inputs it to the signal processing cell transition probability setting unit 806.

  An integration path extraction unit 809 follows the CPI and the integration path for each signal processing cell stored in the second memory 807 in the reverse direction, and extracts the integration path at the time of non-coherent integration.

  The operation will be described below.

The integration path search means 8 inputs the output of the coherent integration means 3 and searches for the integration path of the target signal during non-coherent integration.
First, the amplitude detector 801 calculates the amplitude value A _k (j) from the coherent integration result V _k (j) for each signal processing cell as shown in Expression (21). The result is stored in the first memory 802 for each CPI.
The integration path search unit 803 searches for an integration path considering movement between CPIs for each signal processing cell as shown in FIG. 6, and the integration path evaluation unit 804 evaluates the search path.
The track score shown in Equations (22) and (23) is used for evaluation of the search path.
Here, A _k is a collection of signal processing cells in CPI k shown in FIG.

In Expression (22), the first term on the right side represents the track score increment from CPI k-1 to k, and the second term on the right side represents the track score from CPI k-1. Therefore, the track score up to CPI k is obtained by calculating the track score increment from each CPI to the next CPI and accumulating it.
The first term on the right side in equation (23), the target is from the signal processing cell A _k-1 probability of transition to A _k, the second term on the right side of the received signal when the target is present in the theta _k amplitude value z _k The probability density (calculated from the target amplitude model) and the likelihood ratio of the probability density of the receiver noise amplitude value.

White circles in FIG. 6 indicate signal processing cells. The vertical direction means a signal processing cell number (FFT point number), and the horizontal direction means a CPI number.
For example, consider a transition from CPI number 1 to CPI number 2 and signal processing cell number 1 indicated by a thick white circle.
Three arrows are connected to the signal processing cell from the signal detection cell of CPI number 1. This arrow indicates that there is a possibility of transition from CPI number 1 to CPI number 2 and signal processing cell number 1 indicated by a thick circle.

  The integration path evaluation unit 804 evaluates the transition represented by these three arrows using equations (22) and (23). As a result, the transition from the signal processing cell having the maximum (or minimum) track score is recorded in the second memory 807 as the transition result from CPI number 1 to the CPI number 2 signal processing cell 1 as the best transition. . This processing is applied to all signal processing.

Here, the second term on the right side of Expression (23) represents the likelihood ratio with respect to the amplitude of the signal processing cell, and the larger this is, the higher the possibility of being the target.
The probability density function setting unit 805 for amplitude defines a probability density function for the amplitude of the signal processing cell based on K types of expected amplitude values.
The first term on the right side of Equation (23) represents the probability that the target will transition from the signal processing cell A _k−1 to A _k , but is omitted because it is the same processing as in the first embodiment.

As for the probability density function defined by the amplitude probability density function setting unit 805 shown in Expression (24), the weight value εi is updated as in the second embodiment.
Probability density function update period A Reference is made to the amplitude value of the signal processing cell having the maximum track score value for each CPI, and the likelihood of the probability density function based on the K types of expected amplitude values shown in Expression (24) is evaluated. .
The likelihoods of the K types of probability density functions are evaluated, and when the likelihood value is equal to or greater than the reference value B as shown in Expression (25), the weighting for the probability density function is increased. That is, Δε is added to the weighting value εi.
On the other hand, when the likelihood value is less than the reference value B as shown in Expression (26), the weighting for the probability density function is reduced. That is, Δε is subtracted from the weighting value εi.
By performing these processes for each ACPI process, the weighting for the probability density function is updated.
If the weighting coefficient εi for a certain probability density function is less than the predetermined threshold C by these processes, the probability density function is rejected, and the weighting coefficient value is forcibly set to 0 as shown in Expression (27). And
Here, the value of Δε is appropriately determined in the same manner as in the third embodiment.

  As described above, in the present embodiment, as in the second embodiment, signal detection is possible even at a signal-to-noise power ratio that has been extremely difficult to detect in the past, and signal reproduction processing is also possible. By setting the required number of noise sequences low, the processing load can be reduced, and as a result, adverse false alarms are suppressed by the movement-compensated non-coherent integration process. The target can be detected. Further, in the present embodiment, the integration path evaluation unit 804 evaluates the likelihood of the amplitude value of the signal processing cell when evaluating the integration path, but the signal processing set by the amplitude probability density function setting unit 805 The weight of the expected value of the probability density function with respect to the cell amplitude is updated with the result, and the probability density function that is less than the predetermined threshold is rejected as a result of the update, so the integration path is evaluated more accurately. And an appropriate integration path search can be performed.

Embodiment 5. FIG.
FIG. 11 shows the configuration of the target detection apparatus according to the fourth embodiment. Here, only the part which makes this invention the summary is demonstrated.
In FIG. 11, the integration path search means 9 receives the output of the coherent integration means 3 and searches for an integration path taking into account the fluctuations of the signal processing cell during non-coherent integration and the transition of the signal processing cell accompanying the target acceleration motion. . In the present embodiment, the target integration path is searched by evaluating the likelihood value of the amplitude value of the signal processing cell.
Other configurations are the same as those in the second embodiment, and are denoted by the same reference numerals. In the following description, differences are mainly described, and description of the same operation is omitted.

In the integration path search means 9, reference numeral 901 denotes an amplitude detector, which calculates an amplitude value for each signal processing cell after coherent integration. The result is stored in the first memory 902 for each CPI.
An integration path search unit 903 sets an integration path in consideration of the possibility of transition between CPIs for each signal processing cell.
An integration path evaluation unit 904 evaluates the integration search path set by the integration path search unit 903. The integration path is evaluated by the track score, but the highest signal processing cell of the track score is selected for the transition from the previous CPI to the signal processing cell in the current CPI.
The selected cell is stored in the second memory 907 for each CPI and signal processing cell.
The processing of the integration path evaluation unit is performed for the number of CPIs for non-coherent integration.

Reference numeral 905 denotes an amplitude probability density function setting unit that defines a probability density function with respect to the amplitude of the signal processing cell to be used in the integration path evaluation unit 904 based on K expected amplitude values.
Reference numeral 906 denotes a transition probability setting unit between signal processing cells, which sets a probability of transition from each signal processing cell to be used in the integration path evaluation unit 904 to the next signal processing cell.
A state quantity update unit 908 calculates a state variable vector of each signal processing cell based on an output from the integration path evaluation unit 904 and inputs it to the signal processing cell transition probability setting unit 906.
Reference numeral 909 denotes an integration path extraction unit that follows the CPI and the integration path for each signal processing cell stored in the second memory 907 in the reverse direction, and extracts the integration path at the time of non-coherent integration.

  The operation will be described below.

The integration path search means 9 receives the output of the coherent integration means 3 and searches for the integration path of the target signal during non-coherent integration.
First, the amplitude detector 901 calculates the amplitude value A _k (j) as shown in Equation (28) from the coherent integration result V _k (j) for each signal processing cell. The result is stored in the first memory 902 for each CPI.
The integration path search unit 903 searches for an integration path considering movement between CPIs for each signal processing cell, and evaluates the search path.
The track score shown in Equations (29) and (30) is used for evaluation of the search path.
Here, A _k is a collection of signal processing cells at the CPI number k.
In Expression (29), the first term on the right side represents the track score increment from CPI k-1 to k, and the second term on the right side represents the track score from CPI k-1. Therefore, the track score up to CPI k is obtained by calculating the track score increment from each CPI to the next CPI and accumulating it.
In Expression (30), the first term on the right side is the probability that the target will shift from the signal processing cell A _k−1 to A _k, and the second term on the right side is the probability of the amplitude value z _k of the received signal when the target is at θ _k. It is the likelihood ratio of the density (calculated from the target amplitude model) and the probability density of the receiver noise amplitude value.

  The integration path evaluation unit 904 evaluates the integration path set by the integration path search unit 903 for each CPI using equations (29) and (30). As a result, the transition from the signal processing cell having the maximum (or minimum) track score is recorded in the second memory 907 as the transition result from the CPI number 1 to the CPI number 2 signal processing cell 1 as the best transition. . This process is applied to all signal processing cells.

Here, the second term on the right side of the equation (30) represents the likelihood ratio with respect to the amplitude of the signal processing cell, and the larger this is, the higher the possibility of being the target. The value is unknown.
Therefore, the probability density function setting unit 905 for amplitude defines a probability density function for the amplitude of the signal processing cell based on K types of expected amplitude values.
As a method for setting K types of expected amplitude values, K types of expected amplitude values are generated as shown in FIG. 12 according to normal random numbers in which the average of the expected amplitude values is M and the standard deviation is R.
For the standard deviation, a constant value σ is given.
The curve in FIG. 12 represents a normal distribution function having an average of M and a standard deviation of R. The amplitude probability density function setting unit 905 generates normal random numbers according to this distribution, and thereby K types of expected amplitude values. To decide.

With respect to the probability density function p (z _k | A _k ) defined by the amplitude probability density function setting unit 905 shown in Expression (31), an equal value is set among all functions as the initial value of the weighting value εi.
However, the probability density function p (z _k based on the K types of expected amplitude values represented by the equation (31) is referred to by referring to the amplitude value of the signal processing cell having the maximum track score value for each probability density function update period ACPI. Evaluate the likelihood of | A _k ).

A plurality of curves in FIG. 13 indicate normal distribution functions according to K kinds of expected amplitude values and standard deviation σ. Probability density function update cycle A For each CPI, the amplitude value of the signal processing cell having the maximum track score value is referenced, and the likelihood of the probability density function based on the K types of expected amplitude values shown in Expression (31) is evaluated. As a result, the likelihood shown in the middle part of FIG. 13 is obtained.
The likelihoods of the K types of probability density functions are evaluated, and when the likelihood is less than the reference value B, the expected value of the probability density function is integrated with the expected value of the probability density function having a high likelihood as shown in the lower part of FIG. Then, a normal random number is generated again to create an expected value.

  As described above, in the present embodiment, as in the second embodiment, signal detection is possible even at a signal-to-noise power ratio that has been extremely difficult to detect in the past, and signal reproduction processing is also possible. By setting the required number of noise sequences low, the processing load can be reduced, and as a result, adverse false alarms are suppressed by the movement-compensated non-coherent integration process. The target can be detected. Furthermore, in the present embodiment, the integration path evaluation unit 904 evaluates the likelihood of the amplitude value of the signal processing cell when evaluating the integration path, but the expected value of the probability density function with respect to the amplitude of the signal processing cell is calculated. Decided by normal random number, reject probability density function with low likelihood from likelihood ratio of probability density of best path, integrate probability density function with high likelihood, and generate probability density function with random number again As a result, the integration path can be evaluated more accurately, and an appropriate integration path search can be performed.

Embodiment 6 FIG.
FIG. 14 shows the configuration of the target detection apparatus according to the fifth embodiment. Here, only the part which makes this invention the summary is demonstrated.
In the sixth embodiment, signal detection results in a plurality of frames are obtained with different signal processing frames by repeating the processes of the first to fourth embodiments a plurality of times.
The object is to perform target positioning based on signal detection results in a plurality of frames.

  In FIG. 14, the correlator 10 determines whether or not the signal exceeding the threshold obtained by the threshold 6 is a signal from the same target as the previous frame.

  In FIG. 14, the positioning means 11 inputs the signal detection result determined as the same target by the correlation means 10, and uses the signal processing result of 6 frames or more to perform the target positioning based on the least square method. To do.

  Other configurations are the same as those in the first or second embodiment, and are denoted by the same reference numerals. In the following description, differences are mainly described, and description of the same operation is omitted.

  The operation will be described below.

  The correlation means 10 inputs the output of the threshold means 6 for each frame, determines whether the detection signal obtained for each signal processing frame is from the same target, and targets the detection signals for a plurality of frames. Give a number.

  As a correlation method, for example, for ambiguity with respect to a radio wave source, correlation processing may be performed by GNN (Global Nearest Neighbour). For this, “Multiple-Target Tracking with Radar Application” , Artech House, 1986, etc. are well-known techniques, and the explanation is omitted.

  The positioning means 11 calculates the target position and velocity vector in the Cartesian coordinate system by the non-linear least square method for the detection signals regarded as signals from the same target by the correlation means 10. This will be described below.

It is assumed that the target position and velocity vector in the north reference orthogonal coordinate system at the reference time t ₀ are unknowns, and the Doppler frequency at the signal processing frame time t is output as the detection signal. These relationships can be described as the following equations (32) to (39).

In Expression (32), t represents the signal processing frame time, and t ₀ represents the reference time.
Now, if the unknown of the target is defined as a six-dimensional vector of position and velocity in the north reference Cartesian coordinate system at the reference time t ₀ and constant velocity linear motion is assumed as the target motion model, the target at the signal processing frame time t is assumed. The position and speed of can be described by equations (34) to (39).

  Therefore, if the target detection signal obtained by the threshold means 6 at the signal processing frame time t is set as fd (t), the relationship can be described as in Expression (33).

Here, _assuming that (x _s , y _s , z _s , dot (x _s ), dot (y _s ), dot (z _s )) are known at the target position and velocity of receiving means 1, the unknown Are six of the target position and velocity vectors in the north reference Cartesian coordinates, so that if the detection signal of 6 frames or more is obtained, it is possible to obtain the target position and velocity vector which are unknown by the nonlinear least square method. is there. Since the non-linear least square method is a known method, description thereof is omitted.

  As described above, in the present embodiment, as in the second embodiment, signal detection is possible even at a signal-to-noise power ratio that has been extremely difficult to detect in the past, and signal reproduction processing is also possible. By setting the required number of noise sequences low, the processing load can be reduced, and as a result, adverse false alarms are suppressed by the movement-compensated non-coherent integration process. The target can be detected. Further, in the present embodiment, the threshold means 6 detects the Doppler frequency of the detection signal, and performs correlation processing by the GNN for the ambiguity with respect to the radio wave source, so that the detection signal from the threshold means 6 is detected. The correlation means 10 for determining whether or not the signals are from the same target, and the detection signal determined to be a signal from the same target by the correlation means 10 using 6 frames or more, and the target of the orthogonal coordinate system by the least square method Since the positioning means 11 for calculating the position and velocity vectors is further provided, the integration path can be evaluated more accurately, and an appropriate integration path search can be achieved.

It is the block diagram which showed the structure of the target detection apparatus which concerns on Embodiment 1 of this invention. It is a block diagram which shows the structure of the signal reproduction means in the target detection apparatus which concerns on Embodiment 1 of this invention. It is explanatory drawing which shows the flow of a process of the signal reproduction means in the target detection apparatus which concerns on Embodiment 1 of this invention. It is the block diagram which showed the structure of the target detection apparatus which concerns on Embodiment 2 of this invention. It is explanatory drawing which shows the transition of the target signal at the time of the non-coherent integration in the target detection apparatus which concerns on Embodiment 2 of this invention. It is explanatory drawing which shows the integral path search in the target detection apparatus which concerns on Embodiment 2 of this invention. It is explanatory drawing which shows the non-coherent integration in the target detection apparatus which concerns on Embodiment 2 of this invention. It is explanatory drawing which shows the movement compensation type non-coherent integration in the target detection apparatus which concerns on Embodiment 2 of this invention. It is the block diagram which showed the structure of the target detection apparatus which concerns on Embodiment 3 of this invention. It is the block diagram which showed the structure of the target detection apparatus which concerns on Embodiment 4 of this invention. It is the block diagram which showed the structure of the target detection apparatus which concerns on Embodiment 5 of this invention. It is explanatory drawing which shows the amplitude expected value production | generation method in the target detection apparatus which concerns on Embodiment 5 of this invention. It is explanatory drawing which shows the amplitude expected value update method in the target detection apparatus which concerns on Embodiment 5 of this invention. It is the block diagram which showed the structure of the target detection apparatus which concerns on Embodiment 6 of this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 Reception means, 2 Signal reproduction means, 3 Coherent integration means, 4 Integration path search means, 5 Movement compensation type non-coherent integration means, 6 Threshold means, 7 Integration path search means, 8 Integration path search means, 9 Integration path search means DESCRIPTION OF SYMBOLS 10 Correlation means, 11 Positioning means, 201 Memory, 202 Reception video rearrangement part, 203 Noise generation part, 204 Noise addition part, 205 Addition averaging part, 401 Amplitude detection part, 402 1st memory, 403 Integration path search part 404 integration path evaluation section, 405 amplitude probability density function setting section, 406 signal processing cell transition probability setting section, 407 second memory, 408 state quantity update section, 409 integration path extraction section, 701 amplitude detection section, 702 First memory, 703 integration path search unit, 704 integration path evaluation unit, 705 probability density function setting unit for amplitude, 706 signal processing Inter-cell transition probability setting unit, 707 second memory, 708 state quantity update unit, 709 integration path extraction unit, 801 amplitude detection unit, 802 first memory, 803 integration path search unit, 804 integration path evaluation unit, 805 amplitude Probability density function setting unit, 806 Signal processing inter-cell transition probability setting unit, 807 second memory, 808 state quantity update unit, 809 integration path extraction unit, 901 amplitude detection unit, 902 first memory, 903 integration path search 904 integration path evaluation section, 905 amplitude probability density function setting section, 906 signal processing cell transition probability setting section, 907 second memory, 908 state quantity update section, 909 integration path extraction section.

Claims (6)

Receiving means for receiving a reflected pulse from a target and A / D converting received data for each pulse hit;
A signal reproduction means for reproducing a signal by adding a noise sequence to the received video that is a data sequence of the received data after A / D conversion;
Coherent integration means for performing FFT processing in the pulse hit direction on the received video after signal reproduction;
A target detection apparatus comprising: threshold means for comparing the integration result of the coherent integration means with a predetermined threshold value and determining as target detection when the threshold value is exceeded. Receiving means for receiving a reflected pulse from a target and A / D converting received data for each pulse hit;
A signal reproduction means for reproducing a signal by adding a noise sequence to the received video that is a data sequence of the received data after A / D conversion;
Coherent integration means for performing FFT processing in the pulse hit direction on the received video after signal reproduction;
Obtaining a plurality of transitions of the signal processing cell based on the integration result of the coherent integration means, and integrating path search means for searching the target integration path from the plurality of transitions;
Based on the integration path obtained by the integration path search means, the movement compensation type non-coherent integration means for compensating the integration result of the coherent integration means and performing non-coherent integration on the compensated integration result;
A target detection apparatus comprising: threshold means for comparing the integration result of the movement compensation type non-coherent integration means with a predetermined threshold and determining as target detection when the threshold is exceeded. The integration path search means searches for the target integration path by evaluating the likelihood value of the amplitude value of the signal processing cell,
The target detection apparatus according to claim 2, wherein the weight of the expected value of the probability density function of the amplitude value of the signal processing cell is updated based on the result of the likelihood value. The integration path search means searches for the target integration path by evaluating the likelihood value of the amplitude value of the signal processing cell,
The weight of the expected value of the probability density function of the amplitude value of the signal processing cell is updated based on the result of the likelihood value,
The target detection apparatus according to claim 2, wherein the probability density function is rejected when the weight is less than a threshold as a result of updating the weight. The integration path search means searches for the target integration path by evaluating the likelihood value of the amplitude value of the signal processing cell,
The expected value of the probability density function of the amplitude value of the signal processing cell is determined by a normal random number,
3. The probability density function having a low likelihood is rejected from the likelihood ratio of the probability density of the best path, integrated with a probability density function having a high likelihood, and a probability density function using a random number is generated again. The target detection apparatus as described. Correlation means for receiving detection signals from the threshold means and determining whether the detection signals are from the same target;
And positioning means for calculating a position and velocity vector of the target in an orthogonal coordinate system by a least square method using a detection signal determined to be a signal from the same target by the correlation means. Item 6. The target detection device according to any one of Items 2 to 5.

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