JP2018205174A - Radar device and radar signal processing method thereof - Google Patents

Abstract

To observe target speed and distance with LPI property secured.SOLUTION: A radar device for transmitting/receiving pulse or continuous wave to create range-Doppler (RD) data from N times (N≥1) CPI data, is configured to: extract a cell of the range-doppler shaft exceeding a predetermined threshold for the RD data; analyze a cluster using the extracted cell to select a representative value of the cluster as a target candidate; extract target range-Doppler from the representative value; and measure at least one of a distance, a speed and an angle to output a target observation value.SELECTED DRAWING: Figure 1

Description

  The present embodiment relates to a radar apparatus and a radar signal processing method thereof.

  In the conventional radar system, when detecting a low RCS (Radar Cross Section) target, the CFAR (Constant False Alarm Rate) threshold is reduced to increase sensitivity. However, reducing the threshold increases false detection. In addition, when observing a low altitude target, false detection increases due to the effect of clutter. As described above, when the number of false detections increases, a large number of verification beams (dedicated verify beams) are required to discriminate whether or not the target is detected after detection. As a result, there is a problem that transmission / reception beam scheduling (beam management) for efficiently observing the target within a predetermined period of time increases, making it impossible to observe all the targets.

FM ranging, Yoshida, "Revised radar technology", IEICE, pp.274-275 (1996) MPRF range resolver method, Guy Morris, AIRBORNE PULSED DOPPLER RADAR 2nd edition, pp.264-270 (1996) Encoding radar, Yoshida, 'Revised radar technology', IEICE, pp.278-280 (1996) Code code (M-sequence) generation method, M.I.Skolnik, Introduction to radar systems, pp.429-430, McGRAW-HILL (1980) BPSK, QPSK, Nishimura, "Communication system design by digital signal processing", CQ Publishing Co., pp. 222-226 (2006) SS modulation, Marubayashi, 'Spread spectrum communication and its application', edited by IEICE, pp.1-18 (1998) CFAR processing, Yoshida, 'Revised radar technology', IEICE, pp.87-89 (1996) Least-squares estimation, Nakamizo, 'Signal analysis and system identification processing', Corona, pp.10-176 (1987) DBSCAN (Density-based Spatial Clustering of Applications with Noise) Sebastian Raschka, Python Machine Learning Programming, Impress, pp.319-323 (2016) Extended array (KR product array) Wing-Kin Ma, 'DOA Estimation of Quasi-Stationary Signals With Less Sensors Than Sources and Unkown Spatial Noise Covariance: A Khatri-Rao Subspace Approach', IEEE Trans. Signal Process., Vol.58, no.4, pp.2168-2180, April (2010) Pulse compression, Yoshida, 'Revised radar technology', IEICE, pp.278-280 (1996) Neural network, Okaya, 'Deep learning', Kodansha, pp.7-26 (2014) Convolutional Neural Network, Okaya, 'Deep Learning', Kodansha, pp.79-110 (2014) Recursive neural network, Okaya, 'Deep Learning', Kodansha, pp.111-130 (2014)

  As described above, in the conventional radar system, if the detection of CFAR threshold for high sensitivity or the number of false detections due to the influence of clutter in the case of target observation in the low sky increases, it is discriminated whether the target is detected after detection. For this reason, a large number of verification beams are required, and there are problems such as the restriction of beam management becomes large and all targets cannot be observed.

  The present embodiment has been made in view of the above problems, and an object thereof is to provide a radar apparatus capable of suppressing erroneous detection and extracting only a target with high accuracy and a radar signal processing method thereof.

  In order to solve the above-described problem, according to the present embodiment, in a radar device that transmits and receives pulses (or continuous waves), N times (N ≧ 1) CPI (Coherent Pulse Interval: Coherent Pulse Integration Period) data is used. Range-Doppler (RD) data is created, and a representative value of the target candidate cluster is selected by analyzing the cluster using cells of the range-Doppler axis exceeding the predetermined threshold for this RD data. The target range-Doppler is extracted from the target, and at least one of ranging, velocity measurement, and angle measurement is performed, and target observation values such as position coordinates and velocity are output.

  That is, in the radar apparatus according to the present embodiment, erroneous detection can be suppressed and only the target can be extracted by cluster analysis based on a difference in density distribution between the target and the false detection on the three-dimensional coordinate axis.

1 is a block diagram showing a schematic configuration of a radar apparatus according to a first embodiment. The figure which shows a mode that a target is extracted from several CPI cluster in 1st Embodiment. The figure for demonstrating FM ranging in 1st Embodiment. The figure for demonstrating MPRF ranging in 1st Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 2nd Embodiment. The figure for demonstrating pseudo high-resolution in 2nd Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 3rd Embodiment. The figure for demonstrating multi-cluster analysis processing in 3rd Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 4th Embodiment. The figure which shows a mode that the target range is set with an interpolation curve in 4th Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 5th Embodiment. The block diagram for demonstrating a DBSCAN system in 5th Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 6th Embodiment. The block diagram which shows the specific structure of a target extraction filter in 6th Embodiment. The figure for demonstrating a neural network system in 6th Embodiment. The figure for demonstrating the unit used for a neural network in 6th Embodiment. The figure which shows the example of the distribution state of the cluster contained in range-Doppler two-dimensional data in 6th Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 7th Embodiment. The figure for demonstrating the encoding process of ranging in 7th Embodiment. The figure for demonstrating the correlation process of ranging in 7th Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 8th Embodiment. The block diagram which shows schematic structure of the radar apparatus which concerns on 9th Embodiment. The figure for demonstrating an extended array process in 9th Embodiment. The figure which shows the observation belt of an extended array process in 9th Embodiment. The figure for demonstrating the monopulse beam formation by an extended array process in 9th Embodiment. The figure for demonstrating the monopulse angle measurement by an extended array process in 9th Embodiment.

  Hereinafter, embodiments will be described with reference to the drawings. In the description of each embodiment, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(First Embodiment)-Cluster Distribution As a radar device, in the case of pulse modulation or continuous wave, or as pulse modulation, LPRF having an ambiguity in speed according to the division of PRI (Pulse Repetition Interval). Low Pulse Repetition Frequency: MPRF (Medium Pulse Repetition Frequency) with ambiguity in distance and speed, HPRF (High Pulse Repetition Frequency: High pulse repetition) with ambiguity in distance In the case of frequency), the case of LPRF pulse modulation will be described here for the sake of simplicity.

  The radar apparatus according to the first embodiment will be described with reference to FIGS. 1 to 4. FIG. 1 is a block diagram showing the schematic configuration, FIG. 2 is a diagram showing how targets are extracted from a plurality of CPI clusters, FIG. 3 is a diagram for explaining FM ranging, and FIG. 4 is a diagram for explaining MPRF ranging. FIG.

  As shown in FIG. 1, the radar apparatus according to the present embodiment forms a monopulse antenna by a plurality of antenna elements, transmits a transmission signal through the antenna 1 that forms a transmission / reception beam of Σ and Δ, and the antenna 1. And a signal processor 3 for observing a target distance / velocity from the received signal obtained by the transceiver 2 and calculating and outputting a three-dimensional position coordinate.

  The transceiver 2 includes a transceiver 21 and a modulation controller 22. The transmission / reception unit 21 transmits a pulse modulated wave of a monopulse generated by the pulse modulation of LPRF of the modulation control unit 22 through the antenna 1, receives a reflected wave of the transmission output, and receives reception signals (Σ beams and Δ beams) (Hereinafter referred to as Σ signal and Δ signal).

  The signal processor 3 includes a Σ observation processing unit 31, a CFAR processing unit 32, a cluster analysis unit 33, a distance measurement / speed measurement unit 34, a Δ observation processing unit 35, a cell extraction unit 36, an angle measurement unit 37, and a three-dimensional position coordinate. An output unit 38 is provided.

The Σ observation processing unit 31 receives a Σ reception signal using a monopulse beam, performs signal processing such as pulse compression or FFT according to the transmission waveform using CPI data, and obtains range-Doppler (RD) data. .
The CFAR processing unit 32 detects a reflection point by setting a predetermined threshold by the CFAR using the RD data of the Σ signal based on the monopulse output of the antenna 1.

The cluster analysis unit 33 performs cluster analysis using a density difference that a cell density near a target having a large amplitude is high and is small near a false detection, and when the number of reflection points is not enough in one CPI process, An output after CFAR obtained by adding a plurality of CPI detection cells is used.
The distance measurement / speed measurement unit 34 extracts a target distance by converting the time axis into a distance axis for the detection cell obtained by the cluster analysis (distance measurement), and calculates the velocity from the target distance and the beat frequency. (Side speed).

  The Δ observation processing unit 35 receives a Δ reception signal by a monopulse beam, and performs signal processing such as pulse compression or FFT according to the transmission waveform using the CPI data, similarly to the Σ observation processing unit 31. Range-Doppler (RD) data is obtained.

  The cell extraction unit 36 uses the cluster analysis result of the cluster analysis unit 33 to extract the same cell as the detection cell of the Σ signal near the target having a large amplitude.

The angle measuring unit 37 performs AZ, EL for each extracted cell by performing monopulse angle measurement processing on the cell extracted by the cell extracting unit 36 and the target distance and speed obtained by the distance measuring / speed measuring unit 34. The angle of is output.
The three-dimensional position coordinate output unit 38 calculates and outputs a target three-dimensional position coordinate from the target distance and angle obtained through the angle measuring unit 37.

  In the above configuration, a CPI cluster detection method according to the present embodiment will be described below.

  First, a Σ received signal is input, and signal processing such as pulse compression and FFT according to the transmission waveform is performed using CPI data to obtain range-Doppler (RD) data (31). Here, using the RD data of the Σ signal from the monopulse output of the antenna 1, a predetermined threshold is set by the CFAR to detect the reflection point (32).

  At this time, in consideration of the case where the number of reflection points is not sufficient in one CPI processing, as shown in FIG. 2, output CPIgr1 to CPIgrN after CFAR obtained by adding the detection cells of CPI1 to CPIM a plurality of times are obtained. Use. As shown in FIG. 2, this detection cell has a high cell density in the vicinity of the target having a large amplitude and is small in the vicinity of the erroneous detection. Cluster analysis using this density difference is performed (33).

If the target cluster is extracted, the cell value in the cluster is output as it is, or the centroid value by the following formula (1) using the average value and amplitude of the three-dimensional coordinates in the cluster as the representative value is calculated as the cell value.

With this detection cell, in the case of LPRF, the distance can be measured using a range cell.

  If the target cluster is extracted by the above processing, the cell value in the cluster is output as it is, or the centroid value by the following formula using the average value and amplitude of the three-dimensional coordinates in the cluster as the representative value is output as the cell value To do. In the case of HPRF, the speed can be measured using a Doppler cell.

Further, as shown in FIGS. 3A to 3C, the beat frequency is observed by the reception process in which the received local signal is also swept using the FM ranging method (Non-patent Document 1) by the up sweep and the down sweep. The beat frequency cell is detected by the same cluster analysis using the output subjected to the FFT processing on the fast-time axis and the slow-time axis. From this detection result, the distance and speed can be calculated by the following equations. The beat frequency is given by

For up and down sweeps:

Therefore, the distance R and the speed V can be calculated by the following equations.

In the case of MPRF, a plurality of CPIs having different PRIs (pulse repetition periods) are transmitted and received, and cluster analysis is performed to extract a range cell and a Doppler cell. As a result, the range (MPRF ranging) is calculated by a cell having the least common multiple of a plurality of CPIs as shown in FIG. Similarly, as shown in FIG. 4B, Doppler (MPRF speed measurement) can be calculated by a cell having the least common multiple of multiple CPIs (Non-patent Document 2). Using this Doppler, the speed can be calculated by the following equation.

  Both are common processes in that a cluster analysis is performed using RD data obtained by performing signal processing (pulse compression, FFT) on CPI data to extract a range-Doppler cell.

  As described above, a predetermined threshold is set by the CFAR using the output of the Σ reception signal of the monopulse beam, the target is detected by cluster analysis, and the target distance is output by the detected range cell.

  Further, the same signal processing is performed for the Δpulse reception signal of the monopulse beam, the same cell as the detection cell of the Σ reception signal is extracted, and the angles of AZ and EL are calculated by monopulse angle measurement processing. Three-dimensional position coordinates are calculated from the distance and angle thus obtained.

  As described above, in the present embodiment, in the radar device that transmits and receives pulses (or continuous waves), RD data is created from N times (N ≧ 1) CPI data, and the range-Doppler axis exceeds a predetermined threshold. Cluster analysis using the target cell, and by extracting the target range-Doppler from the representative value of the target candidate cluster, perform at least one of distance measurement, speed measurement, and angle measurement to determine the position coordinates, speed, etc. Outputs the target observation value. As a result, it is possible to suppress false detection and extract only the target by cluster analysis based on the difference in density distribution between the target and false detection on the three-dimensional coordinate axis.

(Second Embodiment) -Pseudo High Resolution In the first embodiment, a method of performing cluster analysis using a density difference between target and false detection in RD data has been described. In this case, the density difference may not be sufficient, and this embodiment will describe the countermeasure.

  As shown in FIG. 5, the radar apparatus according to the present embodiment has a configuration in which a pseudo high resolution processing unit 39 is disposed between the Σ observation processing unit 31 and the CFAR processing unit 32 of the first embodiment.

That is, in the present embodiment, pseudo high resolution is performed on at least one of the range axis and the Doppler axis for the RD data that has been received and processed in the same manner as in the first embodiment.

For this RDf, the range axis and the Doppler axis are zero-padded and the number of dimensions is Nz × Mz (Nz> N, Mz> M) is defined as RDfz. A two-dimensional FFT process is performed on this RDfz.

As a result, as shown in FIGS. 6A and 6B, RD data with pseudo-high resolution is obtained on the range-Doppler axis, and the same processing as that of the first embodiment is performed using this data. By doing so, the target can be detected. In this case, since the resolution is increased, the density difference between the target and the false detection becomes large, and it becomes easy to distinguish the false detection from the target.

  As described above, the radar apparatus according to the present embodiment performs two-dimensional inverse FFT processing on the RD data of each CPI, zero-pads the range axis and Doppler axis, and then uses the two-dimensional FFT processed RD data. At least one of distance measurement, speed measurement, and angle measurement is performed. As a result, since the RD data has a pseudo high resolution on the range-Doppler axis, the density difference between the target and the false detection becomes large, and the target and the false detection can be discriminated more accurately.

Third Embodiment—Multiple Cluster Analysis In the first and second embodiments, erroneous detection may remain. In this embodiment, the countermeasure is described.

  As shown in FIG. 7, the radar apparatus according to the present embodiment uses the cluster analysis unit 33 a disposed between the CFAR processing unit 32 and the distance measurement / speed measurement unit 34, in the clusters of the first and second embodiments. The cluster analysis process similar to that of the analysis unit 33 is performed a plurality of times while changing the parameters.

  That is, in this embodiment, a cluster is detected by the same method as in the first or second embodiment. At this time, since the density distribution of the observed values changes, as shown in FIGS. 8A to 8D, the cluster analysis parameters are changed again, and the cluster analysis is performed a plurality of times.

  As the parameters, for example, in the case of the DBSCAN system (Non-Patent Document 4) described in a fifth embodiment to be described later, the gate radius, the number of observation values in the gate, and the like. By repeating this cluster analysis a predetermined number of times (≧ 2), erroneous detection can be suppressed. At this time, although it is assumed that the target observed value is suppressed, the missing target observed value can be restored using the method described in the fourth embodiment.

  As described above, the radar apparatus according to the present embodiment suppresses erroneous detection by performing cluster analysis processing P (P ≧ 1) times. Thereby, residual erroneous detection that cannot be suppressed by one cluster analysis can be suppressed by a plurality of cluster analysis.

(Fourth Embodiment)-Target extraction by cluster interpolation
In the first to third embodiments, the technique for suppressing the erroneous detection has been described. At this time, the target cluster may be missing. In this embodiment, the countermeasure is described.

  As shown in FIG. 9, the radar apparatus according to the present embodiment has a configuration in which a target interpolation unit 3A is arranged between the cluster analysis unit 33a and the distance measurement / speed measurement unit 34 of the third embodiment.

  That is, in the present embodiment, as shown in FIGS. 10A to 10D, after extracting a cluster, a target candidate range is extracted using the observation values of L (L ≧ 1) frames. At this time, an interpolation curve is extracted using a least square curve (Non-Patent Document 8) or the like.

Here, a method of calculating an interpolation function by the least square method will be described. When the explanatory variable is expressed by the vector x and the objective variable is expressed by the vector y, the following expression is obtained.

The calculation of the least square curve of the data of equation (7) corresponds to the calculation of the parameter θ to be minimized in the following equation.

In the case of a polynomial, θ is a coefficient of each term as shown in the following equation.

The calculation method of θ in equation (9) is based on Non-Patent Document 8, for example.

  If the predetermined width shown in FIG. 10 (d) is set using the fitting curve by the least square curve, and there is a target candidate between them, the target suppressed in FIG. 10 (b) is added to the cluster. Good.

  According to the method of this embodiment, (xn, yn) data of N points are used when calculating the interpolation curve, but 1 to N, 2 to N + 1, etc. while sliding N data. If N is selected, an interpolation curve can be continuously obtained after N pieces of data have been acquired. Thereby, the missing data by the cluster analysis can be corrected.

  As described above, after extracting target candidates for N frames, the radar apparatus according to the present embodiment interpolates the target cluster region using the interpolation curve, and interpolates missing target observation values. As a result, when the target analysis value is missing due to the cluster analysis, the target cluster range can be interpolated by the interpolation curve to suppress the target loss.

(Fifth Embodiment) -DBSCAN Method In this embodiment, a method using DBSCAN (Density-based Spatial Clustering of Applications with Noise) (Non-Patent Document 9) will be described.

  As shown in FIG. 11, the radar apparatus according to this embodiment is characterized by a cluster analysis unit 33b arranged between the CFAR processing unit 32 and the target interpolation unit 3A of the fourth embodiment.

  That is, in the present embodiment, the cluster analysis unit 33b adopts the DBSCAN method, and as shown in FIG. 12, sets a circle range with a radius ε and the number of observations MinPts within this circle range, and the cluster difference due to the difference in density. Classify. A plurality of clusters are generated for each cluster satisfying the set radius ε and the number of observations MinPts, and the others can be classified as noise.

  Specifically, a point that has at least the set number of observations MinPts within the set radius ε is a core point, and if the number of adjacent points within the radius ε is less than MinPts, it is considered a border point. It is. Points that are neither core points nor border points are noise points. Thereby, the reflection point (core point and border point) by a target and misdetection (noise point) can be distinguished (nonpatent literature 9).

  As described above, the radar apparatus according to the present embodiment uses DBSCAN as the cluster analysis method. Thereby, the cluster analysis by the difference of the density distribution of a target and false detection can be performed efficiently.

(Sixth Embodiment) -Neural Network Method In this embodiment, attention is paid to a density difference between a target and a false detection as a cluster detection method. Furthermore, in order to enhance the discrimination capability between the target and the false detection, a method of identifying using a neural network (Non-patent Document 12) by inputting coordinate data in a predetermined range centered on the selected target cluster can be applied. As a specific example of the neural network (NN), a convolutional neural network (Non-Patent Document 13), a recursive neural network (Non-Patent Document 14), or the like can be used. In this case, in addition to the density difference between the target and the false detection, a difference in spatial spread characteristics can be extracted as a feature amount for discrimination.

  FIG. 13 shows a schematic configuration of the radar apparatus according to this embodiment, and FIG. 14 shows a specific configuration of the target extraction filter shown in FIG. That is, the radar apparatus according to the present embodiment has a configuration in which the target candidate extraction filter 3B is disposed between the target interpolation unit 3A and the distance measurement / speed measurement unit 34.

The target candidate extraction filter 3B includes a two-dimensional data generation unit 3B1, a column vector conversion unit 3B2, an NN (neural network) processing unit 3B3, and a filter 3B4. The target interpolation unit 3A extracts the detection result from the cluster distribution. Based on the target candidates, the neural network NN identifies whether it is a target or a false detection, and only the target candidates are extracted. FIG. 15 (multilayer perceptron system) shows a configuration example of the NN used for this purpose, and FIG. 16 shows the unit configuration (Non-patent Document 12). Each unit constituting the NN is configured as shown in FIG. 16, and is formulated as follows.

As the activation function, various functions such as a sigmoid function (Non-Patent Document 12) can be applied.

  As input data, plotting at two-dimensional coordinates (range-Doppler) points as shown in FIG. 17 based on the coordinates of the target candidates extracted by the cluster distribution detection (33a) and the target candidate extraction (3A). Then, the two-dimensional data (Nx, Ny) is converted into a one-dimensional column vector (1 to Nx × Ny) including the point where the target does not exist, and is input to the NN.

  As the learning of the weight of NN, by using a simulator or an actual measurement value, known three-dimensional learning data of a true value (target or false detection teacher signal) is generated, and the generated signal (X, Y, Z ) And a teacher signal (whether it is a target or a false detection). The identification of the target or false detection corresponds to binary classification or multi-class classification (number of classes = 2).

In general, when classifying into multiple classes, training data for learning can be defined by the following equation.

Further, the error function E is defined as 1/2 of the sum of the square error of the output NN and the square data of the teacher data d for all input data as in the following equation.

Select w so that this is the smallest. For this purpose, a stochastic gradient descent method (Non-Patent Document 12) or the like may be used.

  In actual processing after learning, the NN of FIG. 15 is operated using a fixed weight, and the target or false detection or the target candidate filter 3B is passed, and the distance / speed measurement is performed using only the target candidate. The observation value of the distance measurement / speed measurement is calculated and output by the unit 34. As described above, the observed value of the target signal with less influence of erroneous detection can be output.

  As described above, the radar apparatus according to the present embodiment plots two-dimensional coordinates (range-Doppler axis) using the observation value coordinates of the target cluster candidate extracted by the cluster analysis as input. Then, a filter process for target or false detection is performed using a neural network (NN). Thereby, the target and the erroneous detection can be identified using the NN process, and the process in which the erroneous detection is further suppressed can be performed.

(Seventh Embodiment) -Ranging In the first embodiment, the method of performing range analysis by measuring the HPRF and MPRF, extracting the range-Doppler cell, and measuring the distance and speed has been described. However, when SN (signal-to-noise ratio) is high, high-accuracy cells can be extracted by cluster analysis. However, in the case of low SN, the detected cell deviates from the true value, and the calculated values of distance and speed are obtained. An error may occur. Therefore, in this embodiment, an improvement measure will be described with reference to FIGS.

  FIG. 18 shows a schematic configuration of a radar apparatus according to the present embodiment, and FIG. 19 shows an example of a transmission pulse timing and a modulation code of each transmission pulse in the present embodiment. That is, the radar apparatus according to this embodiment has a configuration in which the distance measurement / speed measurement unit 34 is divided into a speed measurement unit 34a and a distance measurement unit 34b, and a reference signal generation unit 3C, a range compression unit 3D, and a CFAR processing unit 3E are added. is there.

  The reference signal generator 3C generates a reference signal based on the speed obtained by the speed measuring unit 34a. The range compressor 3D performs range compression on the Σ received signal (range observation value) based on the reference signal. The CFAR processing unit 3E extracts cells exceeding a predetermined threshold value from the range compressed signal. The distance measuring unit 34b measures the range of the extracted cell.

  In the above configuration, the ranging process according to the present embodiment will be described below.

  The radar apparatus according to the present embodiment divides a pulse train into a CW period and a ranging period, calculates a target speed using cluster analysis in the CW period, and corrects a reference signal for compression using the target speed in the ranging period. It is a method to measure the distance.

  Cluster analysis is applied only to the CW period, and target candidates are extracted even when there are many false detections. In the ranging period, the cluster analysis is not used, but the correlation is extracted using the reference signal corrected by the speed extracted in the CW period. As a result, not only can the target be extracted, but in the case of a speed due to erroneous detection, the correlation value becomes small, so that it is possible to suppress erroneous detection. Although FIG. 19 shows the case of single pulse transmission as an example, other methods such as multiple pulse transmission may be used.

  In the case of a single pulse train, code modulation is performed for each pulse. First, in the CW period, it is necessary to observe Doppler, and pulses of the same frequency are repeated Mcwall times as shown in FIG. In the ranging period in the case of HPRF, Mrng pulses are code-modulated using the code of the Mrng chip.

  As a coding method for the ranging period, SS modulation (Non-patent Document 3) is considered. Specifically, for example, there is an M series code (Non-Patent Document 4), and other codes may be used. This encoding includes a random signal (noise) including a decimal number within ± 1.

Further, the random signal is to make the phase random, and includes a method of modulating (frequency hopping) by changing the frequency, for example. In this case, if the local signal is the same and the frequency from the local signal is changed, the frequency can be changed while ensuring coherency. Using this signal code string, as shown in the following equation, the signal phase is changed to generate a transmission signal.

  The above is the case of BPSK (Binary Phase Shift Keying, Non-Patent Document 3), but other phase modulation methods may be used.

In the present embodiment, a reference signal for correlation processing is generated using a signal in the ranging period. As the reference reference signal, the target speed output in the CW period is used.

The set standard reference signal length is Mrng, and the code length (Mrng) is set to the ranging period for the range compression process (correlation process, Non-Patent Document 11). For this purpose, the zero-padded signal is used as a reference signal.

In order to calculate the correlation between the reference signal and the input signal, the reference signal is subjected to FFT processing.

On the other hand, the received signal during the ranging period can be expressed by the following equation.

The received signal is FFT processed

In the range compression process 42 (correlation process), the multiplication of the frequency axis is inverse FFTed to obtain the following expression.

  This is shown in FIG. The target distance can be calculated by detecting the threshold of srng (t) with CFAR (3D) or the like and converting the time axis to the distance axis in the distance extraction (34b). For the speed (34a), the result calculated from the data of the CW period is output.

  In the CW period, as shown in FIG. 18, detection by the cluster analysis (33a) is performed using the data detected by the CFAR (32) using the Σ received signal from the transceiver 2, and the target range-Doppler cell is specified. To do. On the other hand, with respect to the Δ received signals (ΔAZ and ΔEL) from the transmitter / receiver 2, angle measurement (37) can be performed by using the same detection cell as the Σ signal. The target three-dimensional position coordinate (38) can be calculated by using the angle measurement value (AZ angle and EL angle) and the range measured.

  As described above, the observation time is divided into the CW period and the ranging period, the target speed is calculated by cluster analysis in the CW period, and the target distance is output by compressing the range using the reference signal corrected by the target speed in the ranging period. Said.

  In the present embodiment, the case where the CW period and the ranging period are single pulses (HPRF) has been described. However, the same technique can be applied to the case where SS modulation is performed within a pulse using a long pulse.

  As described above, the radar apparatus according to the present embodiment plots two-dimensional coordinates (range-Doppler axis) using the observation value coordinates of the target cluster candidate extracted by the cluster analysis as input. Then, a filter process for target or false detection is performed using a neural network (NN). As a result, a modulated pulse by encoding or random signal (noise) is transmitted / received, a target being erroneously detected is detected, a target speed is detected, a range is compressed using a reference signal corrected by the speed, Speed and distance can be output.

(Eighth Embodiment) -Monopulse Angle Measurement In the seventh embodiment, a method is described in which angle measurement is performed using Σ and Δ in the CW period (Doppler observation). In this case, the angle measurement processing using the Σ and Δ signals is performed even for erroneous detection remaining after the cluster analysis, and the processing scale increases.

  As a countermeasure, in the present embodiment, a case where angle measurement processing is performed using data of a ranging period (range observation) will be described.

  As shown in FIG. 21, the radar apparatus according to the present embodiment includes a range compression unit 3F and a cell extraction unit 36 as a Δ system range observation system. That is, it is the same as in the seventh embodiment until the reference signal is generated using the speed of Σ in the CW period. The range compression of the Σ signal and the Δ signal is performed using the calculation speed detected by the cluster analysis using the Σ signal. CFAR detection is performed using the Σ signal (3D), and the distance is measured (34b). Further, using the data after the range compression processing of Σ and Δ, the cell of a range-Doppler cell having the same Δ signal as that detected by the Σ signal is extracted (36), and monopulse angle measurement is performed (37), Three-dimensional position coordinates are output (38). As described above, since the erroneous detection is suppressed by the correlation processing at the time of range compression, the angle can be measured only with respect to the target detection cell, and an effect of reducing the processing scale can be expected.

  As described above, the radar apparatus according to the present embodiment extracts a target speed by detecting a target by cluster analysis of a transmission / reception signal of a Σ signal, out of Σ (sum) and Δ (difference, ΔAZ or ΔEL) of a monopulse output. After that, a modulated pulse by encoding or random signal (noise) is transmitted / received, the received signal of the reflected signal from the target is range compressed using the reference signal corrected by the target speed, and the distance is output, and the monopulse output For the Δ signal, monopulse angle measurement processing is performed using a range-compressed signal using a reference signal corrected by the target speed. Thereby, even when there are many low SN signals and false detections, monopulse angle measurement is possible by using the Δ signal of the same cell as the Σ signal extracted by cluster analysis.

(Ninth embodiment)-Monopulse angle measurement by expansion array In the eighth embodiment, the case where angle measurement processing is performed using the data of the ranging period (range observation) is described. In this case, when the target SN is low, the angle measurement accuracy deteriorates. In this embodiment, this countermeasure will be described.

  In the radar apparatus according to the present embodiment, as shown in FIG. 22, an extended array 3 </ b> F is disposed between the distance measuring unit 34 b and the angle measuring unit 37. That is, in this embodiment, a reference signal is generated using the speed of Σ in the CW period, and range compression of the Σ signal and the Δ signal is performed (3D, 3F). CFAR detection is performed using the Σ signal (3E), and the distance is measured (34b). Further, a cell of the same range-Doppler as the detected cell is extracted (36), and an extended array process is performed (3G). This is shown in FIG.

In FIG. 23, on the A axis (EL axis), signals Xa1 and Xa2 are generated by dividing the aperture vertically from monopulse outputs Σ and ΔEL.

Similarly, on the B axis (AZ axis), signals Xb1 and Xb2 that are divided into left and right apertures are generated from the monopulse outputs Σ and ΔEL.

When the KR product is used as the extended array process, the following expression is obtained. For each of the P signals separated by the range-Doppler axis, the A-axis and B-axis array signals (Xa and Xb) are extracted, and a portion for performing the extended array processing is formulated. First, when the biaxial input signals including the observation direction (AZ, EL) are expressed as Xa and Xb, respectively, the following equation is obtained by the coordinate system of FIG. The phase center coincides with the A axis and the B axis.

In this embodiment, Na = 2 and Nb = 2.

From the above, as the signal Xin input to the phase center of the virtual plane array, the biaxial signals Xa and Xb are expressed by the following equations.

Next, using this signal (Xa, Xb), KR product array processing is performed as extended array processing.

When the left and upper end elements are vectorized, the following equation is obtained.

  A reception beam may be formed by using Xkra and Xkrb as element signals (Xa and Xb) of the new expansion array.

The received beam output is multiplied by a complex weight for beam directing direction control by the element of equation (27), and then added by DBF (Digital Beam Forming, Non-Patent Document 1) to obtain the following equation.

The weights Wapnm and Wbpnm for controlling the beam direction can be expressed by the following equations.

When Na = Nb = 2 in the present embodiment, the extended array is as shown in FIG. 25, and Σall and Δall can be formed using the entire subarray of the extended array.

Using this beam, the error voltage of FIG. 21 is calculated and tabulated, and the target angle is calculated from the error voltage of the observed value.

  As described above, the radar apparatus according to the present embodiment extracts a target speed by detecting a target by cluster analysis of a transmission / reception signal of a Σ signal, out of Σ (sum) and Δ (difference, ΔAZ or ΔEL) of a monopulse output. After that, the modulation pulse by encoding or random signal (noise) is transmitted and received, and the Σ signal obtained by performing range compression on the reflected signal from the target using the reference signal corrected by the target speed, and the Δ signal of the monopulse output, A range-compressed Δ signal is obtained using the reference signal corrected according to the target speed, and the signals Σ1 and Σ2 of the aperture divided into two (AZ plane or EL plane) are calculated from the Σ signal and Δ signal, and Σ1 and Σ2 The expanded array process obtains the virtual array signals Σa1 to Σa3, calculates Σall and Δall of the entire aperture from the virtual array signal, and outputs a measured value by monopulse angle measurement. As a result, even when there are many low SN signals and false detections, highly accurate monopulse angle measurement is possible by performing an extended array process using the Δ signal of the same cell as the Σ signal extracted by cluster analysis.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the components without departing from the scope of the invention in the implementation stage. In addition, various inventions can be formed by appropriately combining a plurality of components disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

1 ... antenna,
2 ... transceiver, 21 ... transceiver, 22 ... modulation controller,
DESCRIPTION OF SYMBOLS 3 ... Signal processor, 31 ... (Sigma) observation processing part, 32 ... CFAR processing part, 33, 33a, 33b ... Cluster analysis part, 34 ... Distance measurement / speed measurement part, 34a ... Speed measurement part, 34b ... Distance measurement part, 35 ... Δ observation processing unit, 36 ... cell extraction unit, 37 ... angle measurement unit, 38 ... three-dimensional position coordinate output unit, 39 ... pseudo high resolution processing unit, 3A ... target interpolation unit, 3B ... target candidate extraction filter, 3B1 ... 2D data generation unit, 3B2 ... column vector conversion unit, 3B3 ... NN (neural network) processing unit, 3B4 ... filter, 3C ... reference signal generation unit, 3D, 3F ... range compression unit, 3E ... CFAR processing unit, 3G ... Expansion array.

Claims (10)

In a radar apparatus that generates range-Doppler data from N times (N ≧ 1) CPI (Coherent Pulse Interval) data by transmitting and receiving pulses or continuous waves,
Extraction means for extracting cells of a range-Doppler axis exceeding a predetermined threshold for the range-Doppler data;
Cluster analysis means for selecting a representative value of the target cluster by analyzing the cluster using the cells extracted by the extraction means;
A radar apparatus comprising: an observation value output means for extracting a target range-Doppler from the representative value, performing at least one of ranging, speed measurement, and angle measurement to output a target observation value.   Further, the range-Doppler data of each CPI is subjected to two-dimensional inverse FFT processing, zero-filled with the range axis and the Doppler axis, and then subjected to two-dimensional FFT processing to increase the pseudo-high resolution to create the range-Doppler data. The radar apparatus according to claim 1, further comprising resolution means.   The radar apparatus according to claim 1, wherein the cluster analysis unit performs the cluster analysis processing a plurality of times.   Furthermore, after extracting target candidates for N frames from the analysis result of the cluster analysis means, a target interpolation means for interpolating a target cluster region using an interpolation curve to interpolate missing target observation values is provided. Item 2. The radar device according to item 1.   The radar apparatus according to claim 1, wherein the cluster analysis unit uses a DBSCAN (Density-based Spatial Clustering of Applications with Noise) method as the cluster analysis method.   Further, using the observed value coordinates of the target cluster candidate extracted by the analysis of the cluster, it is plotted on the two-dimensional coordinates of the range-Doppler axis, and the two-dimensional plot coordinates are used as input, and the target is erroneously detected using a neural network. The radar apparatus according to claim 1, further comprising filter processing means for performing such filter processing.   Further, the frame period to be transmitted / received is divided into a CW period and a ranging period. In the CW period, a target speed is calculated using the analysis processing of the cluster, and a reference signal for compression is corrected using the target speed in the ranging period. The radar apparatus according to claim 1, further comprising a period division processing unit that measures the distance.   In the transmission / reception, the target speed is extracted by cluster analysis of the Σ signal transmission / reception signal out of Σ (sum) and Δ (difference, ΔAZ or ΔEL) of the monopulse output, and then the encoded or random signal (noise) is detected. ), The range of the received signal of the reflected signal from the target is compressed using the reference signal corrected by the target speed, and the distance is output, and the Δ signal of monopulse output is corrected by the target speed The radar apparatus according to claim 1, wherein monopulse angle measurement processing is performed using a range-compressed signal using a reference signal.   In the transmission / reception, a target pulse is extracted by cluster analysis of the Σ signal transmission / reception signal out of Σ (sum) and Δ (difference) of the monopulse output, and then the target pulse is extracted, and then a modulated pulse by encoding or random signal (noise) The Σ signal that was range compressed using the reference signal corrected by the target speed and the Δ signal of the monopulse output was subjected to range compression using the reference signal corrected by the target speed. Obtain the Δ signal, calculate the signal divided by the aperture from the Σ signal and the Δ signal, obtain the virtual array signal by the extended array processing, calculate the Σ signal and the Δ signal of the entire aperture from the virtual array signal, The radar apparatus according to claim 1, wherein the angle measurement value is output by monopulse angle measurement. In a radar signal processing method of a radar apparatus that generates range-Doppler data from N times (N ≧ 1) CPI (Coherent Pulse Interval) data by transmitting and receiving pulses or continuous waves,
Extracting cells of the range-Doppler axis exceeding the predetermined threshold for the range-Doppler data;
Analyzing clusters using the extracted cells to select representative values for target candidate clusters,
A radar signal processing method of a radar apparatus that extracts a target range-Doppler from the representative value, performs at least one of distance measurement, speed measurement, and angle measurement, and outputs a target observation value.

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