High-speed target detection method and device for pulse Doppler radar

Abstract

The invention discloses a high-speed target detection method for a pulse Doppler radar, and the method is characterized in that the method comprises the following steps: 1, obtaining an echo signal, so as to obtain initial data; step 2, resampling the initial data in the slow time dimension to obtain resampled data; 3, compensating the resampling data through a speed fuzzy number matching filter to obtain corresponding correction data; 4, performing linear transformation processing on the corrected data to obtain target data; step 5, detecting the target data to construct an effective target parameter set; and 6, repeating the steps to output a final effective target parameter set. The invention further provides a high-speed target detection device. According to the method provided by the invention, the problem that the ultra-high-speed target strides a distance unit and the speed is fuzzy during multi-target detection in a complex scene can be solved, so that the high-precision estimation and detection of the ultra-high-speed multi-target distance and speed can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a high-speed target detection method and device for pulse Doppler radar. Background Art

[0002] Due to the rapid development of aviation and aerospace technology, aircraft, missiles, artificial satellites and spacecraft all use radar as a means of detection and control. Especially after the successful development of the anti-intercontinental ballistic missile system, radars are required to have long-range, high-precision, high-resolution and multi-target detection. At the same time, radar high-speed multi-target detection is of great significance in target imaging and space exploration. Generally speaking, for targets aligned with range images, a moving target detection (MTD) algorithm can be used. However, in the field of ultra-high-speed target detection, low radar cross-section (RCS) is the main problem faced by modern radar systems. To solve this difficulty, many solutions propose to extend the signal coherent integration time to achieve the purpose of improving the target signal-to-noise ratio, but the high-speed movement of the target will produce problems such as range offset and Doppler broadening, which seriously affects the effective integration of echo energy and reduces detection performance.

[0003] Patent document CN115308738A discloses an insect swarm density estimation algorithm based on centimeter-wave scanning radar. By dividing the three-dimensional space into independent resolution units, the detection units that detect the target are counted to better obtain the spatial distribution and three-dimensional shape of the insect swarm; the relationship between the echo intensity and the effective cross-sectional area of ​​the target reflection in the radar power formula is used to deduce the number of insect swarm individuals in the detection unit; combined with the distance and speed distribution diagram generated by the Doppler effect, the number of individuals in different detection units is verified, so that the statistics of the overall number of insect swarms in the airspace are more reasonable, ensuring the accuracy of the final density value estimation.

[0004] Patent document CN114063057A discloses a method for processing multifunctional sea-surface radar signals. The method adopts a DBF system and focuses on search to form three-dimensional coverage of the surveillance area and form a preliminary classification of surface and air targets. Stable sea surface targets are distinguished based on the amplitude of the echo, large targets are imaged with stepped frequency, and the size of the ship is distinguished based on the size of the target imaging. For air targets, low-altitude aircraft, flying birds, etc. are distinguished based on the stability of the track and micro-Doppler characteristics. Summary of the invention

[0005] The purpose of the present invention is to provide a high-speed target detection method and device for pulse Doppler radar, which can solve the problem of ultra-high-speed target cross-range unit and speed ambiguity when detecting multiple targets in complex scenes, thereby achieving high-precision estimation and detection of ultra-high-speed multiple target distances and speeds.

[0006] In order to achieve the first object of the present invention, the following technical solution is provided: a high-speed target detection method for pulse Doppler radar, which specifically includes:

[0007] Step 1: Acquire the echo signal emitted by the pulse Doppler radar, and perform linear processing on the echo signal to obtain initial data of the speed and distance coupling corresponding to each target;

[0008] Step 2: resampling the initial data in the slow time dimension to obtain resampled data after decoupling of speed and distance;

[0009] Step 3, compensating the resampled data by a pre-built velocity fuzzy number matched filter to obtain corresponding corrected data;

[0010] Step 4: performing linear transformation processing on the corrected data to obtain target data;

[0011] Step 5, extracting first data in the distance dimension and second data in the speed dimension of the target data, and calculating the square of the amplitude modulus of the first data and the second data, and taking the highest value point of the square of the amplitude modulus as the input of the pre-built two-dimensional constant false alarm detector for detection;

[0012] If a valid target is detected, the distance, speed and amplitude of the target in the input target data are added to the valid target parameter set;

[0013] Otherwise, the corresponding target data will be eliminated;

[0014] Step 6: Repeat steps 1 to 5 until the judgment termination condition of the two-dimensional constant false alarm detector is met to output a final valid target parameter set.

[0015] The present invention corrects the problem of high-speed target distance migration by resampling in the slow time dimension, thereby improving the accuracy of high-speed target distance estimation, and solves the problem of low detection accuracy caused by the mutual influence of multiple strong and weak targets by eliminating strong targets. Finally, a matched filter is constructed to search and compensate for the speed fuzzy number corresponding to the target, thereby improving the accuracy of high-speed target speed estimation.

[0016] Specifically, the pulse Doppler radar is a shore-based radar, an airborne radar or a ship-based radar.

[0017] Specifically, in step 2, in the slow time dimension, t m =[f c / (f c +f)]τ m Under the condition, resampling is completed by the sinc interpolation method, where τ m is the virtual time, t m The original time.

[0018] Specifically, the resampling expression is as follows:

[0019]

[0020] Among them, m is the original sampling point, m' is the sampling point after sinc interpolation, f is the fast time frequency, and f c is the carrier frequency and M is the number of pulses.

[0021] Specifically, the expression of the correction data is as follows:

[0022]

[0023] in, represents the velocity ambiguity number used to compensate the resampled data, τ m For virtual time, Represents the velocity fuzzy number matched filter.

[0024] Specifically, in step 4, the linear transformation process is as follows:

[0025] The corrected data is subjected to inverse fast Fourier transform in the fast time dimension and fast Fourier transform in the slow time dimension to obtain target data in the range-Doppler dimension.

[0026] Specifically, in step 5, the detection process of the two-dimensional constant false alarm detector is as follows:

[0027] Eliminate the protection units near the maximum square value of the input amplitude modulus value in all training units;

[0028] Calculate the average value of all data except the maximum square value of the amplitude modulus value to obtain the noise floor value;

[0029] A threshold value of a valid target is determined based on the background noise value, and a ratio of the maximum square value of the amplitude modulus value to the background noise value is compared with the threshold value:

[0030] If the ratio is greater than the threshold, it is considered that there is a valid target, otherwise it is considered that there is an ineffective target.

[0031] Specifically, the judgment termination conditions include:

[0032] a. The number of repetitions has reached the maximum number of iterations preset by the constant false alarm detector;

[0033] b. The constant false alarm detector determines that there is no target, that is, the detection statistic corresponding to the maximum point does not exceed the corresponding threshold.

[0034] In order to achieve the second objective of the present invention, the following technical solution is provided: a high-speed target detection device, used to execute the steps of the above-mentioned high-speed target detection method for pulse Doppler radar.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] Compared with the traditional pulse compression method based on matched filtering, the present invention performs correction by resampling in the slow time dimension, thereby overcoming the problem of distance migration and improving the accuracy of distance estimation;

[0037] By constructing a matched filter about the speed fuzzy number to search for the speed fuzzy number and compensate and eliminate the influence of speed fuzziness, the accuracy of speed estimation of high-speed multi-targets is further improved.

[0038] By eliminating the signal data of the estimated strong target before each detection, the probability of successful detection of the weak target is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a high-speed target detection method for pulse Doppler radar provided in this embodiment;

[0040] Figure 2 A schematic diagram of the structure of a high-speed target detection device provided in this embodiment;

[0041] Figure 3 The detection result of target one by the high-speed target detection method provided in this embodiment;

[0042] Figure 4 The detection result of target 2 by the high-speed target detection method provided in this embodiment;

[0043] Figure 5 The detection result of target three by the high-speed target detection method provided in this embodiment;

[0044] Figure 6 The speed fuzzy number detection results of target one, target two and target three provided in this embodiment;

[0045] Figure 7 The detection result of target one by the traditional pulse compression method provided in this embodiment;

[0046] Figure 8 The detection result of target 2 by the traditional pulse compression method provided in this embodiment;

[0047] Fig. 9 This embodiment provides the detection result of target three using the traditional pulse compression method. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, the high-speed target detection method for pulse Doppler radar provided in this embodiment includes:

[0050] Step 1: Receive the echo signal emitted by the pulse Doppler radar and obtain the speed and distance coupling data through linear processing method:

[0051] First, initialize the two-dimensional constant false alarm detector and input the false alarm probability, maximum number of iterations, oversampling factor, number of corrections, number of protection units, and number of training units. After down-conversion demodulation, the K target echo signals of the pulse Doppler radar are obtained. The formula is:

[0052]

[0053] Where N = F s T, r m,k =r 0,k +mTv k ,m=0,1,…,M-1,is the radial distance of the kth target from the radar when the mth pulse is emitted; F s is the sampling frequency, T s =1 / F s is the sampling time, f c is the carrier frequency, c is the speed of light, T is the pulse repetition period, v k is the speed of the kth target.

[0054] The speed of high-speed targets often exceeds the maximum unambiguous speed of the radar system, so speed ambiguity occurs. That is, the target speed can be expressed as an equation about the speed ambiguity number, which can be expressed as:

[0055] v k =γ k v amb +v k,dect

[0056] Among them, v amb is the blind speed, γ k is the Doppler fuzzy number corresponding to the kth target, v k,dect =mod(v k ,v amb )satisfy The pulse repetition frequency is PRF = f PRF , then when the Doppler frequency range is When , the blind speed is calculated as

[0057] Use the matched filter method to perform pulse compression and perform FFT in the fast time dimension to obtain the data of the coupling between the slow time dimension speed and the fast time frequency, which can be expressed as:

[0058]

[0059] Among them, t m =mT, f is the fast time frequency, and:

[0060] is the detection speed item corresponding to the kth target, is the velocity fuzzy term corresponding to the kth target.

[0061] Step 2: Resample in the slow time dimension to obtain data after decoupling speed and distance:

[0062] The resampling of the slow time dimension is done using the sinc interpolation method, and the velocity blur term is approximated as H key (f,τ m )≈H(f,τ m ), we can get the data after the slow time dimension speed and the fast time frequency are decoupled, which can be expressed as:

[0063]

[0064] Among them, m is the original sampling point, m' is the sampling point after sinc interpolation, τ m is the resampled slow time after interpolation, is the resampling detection speed item corresponding to the kth target, is the resampled velocity fuzzy term corresponding to the kth target.

[0065] Step 3: construct a matched filter of speed fuzzy number for the decoupled data to search for speed fuzzy number, and perform speed fuzzy compensation on the decoupled data to obtain corrected data:

[0066] The resampled formula for the speed blur phenomenon is organized into the relationship between the real speed and the blind speed, which can be expressed as follows:

[0067]

[0068] Among them, P k,real (f,τ m ) is the resampled true velocity item corresponding to the kth target.

[0069] For the speed fuzzy number range of γ min <γ<γ max Constructing a matched filter The matched filtering is performed on the resampled decoupled data, which can be expressed as follows:

[0070]

[0071] For the matched filter data R match In the fast time dimension, an inverse fast Fourier transform (IFFT) is performed and in the slow time dimension, a fast Fourier transform (FFT) is performed to obtain the range-Doppler information matrix R corresponding to the velocity ambiguity number γ compensation. γ (n,m);

[0072] Take R γ The maximum value of the (n,m) modulus value is obtained, and the protection units near the maximum value are eliminated. The training units are selected to construct the data set corresponding to the speed fuzzy number γ;

[0073] The interference level estimate for the data set is calculated using the formula:

[0074]

[0075] Among them, N r represents the number of training units, represents the unit number in the training unit set Γ(i,j), and i and j represent the coordinates of the training unit.

[0076] The compensation information corresponding to the speed fuzzy number γ is calculated by the interference level estimation value, which is expressed as follows:

[0077]

[0078] The corresponding compensation information is calculated in sequence within the range of the speed fuzzy number, and the γ corresponding to the maximum value of the compensation information is taken as the estimated value of the speed fuzzy number, which is expressed by the formula:

[0079]

[0080] And the compensation corresponding speed fuzzy number is Corrected data after The formula is:

[0081]

[0082] Step 4: Perform linear transformation on the corrected data to obtain target data: In this embodiment, the linear transformation includes performing inverse fast Fourier transform (IFFT) on the corrected data in the fast time dimension and performing fast Fourier transform (FFT) on the slow time dimension to obtain target data R (n, m) in the range-Doppler dimension, which is expressed as:

[0083]

[0084] Where λ = c / f c .

[0085] Step 5, extract the first data in the distance dimension and the second data in the speed dimension of the target data, and calculate the square of the amplitude modulus of the first data and the second data, and use the highest value point of the square of the amplitude modulus as the input of the pre-built two-dimensional constant false alarm detector for detection. If the detection is a valid target, the distance, speed and amplitude of the target in the input target data are added to the valid target parameter set; otherwise, the corresponding target data is discarded.

[0086] In this embodiment, whether there is a target at the detection unit is determined by the formula:

[0087]

[0088] in, It represents the threshold used to determine the effective target. If the judgment formula is satisfied, it is determined that there is a valid target at the detection unit, and the next step of target parameter estimation can be carried out; if it is not satisfied, the algorithm stops.

[0089] The echo signal can be expressed in matrix form as:

[0090]

[0091] in,

[0092]

[0093] a M (ω)=[1,e jω ,…,e j(M-1)ω ] T and

[0094] Let y = vec(Y), w=vec(W). After straightening the matrix to a vector, we get:

[0095]

[0096] The maximum likelihood estimate of the amplitude x, distance r and speed v corresponding to the first detection unit is It can be obtained by minimizing the energy of the residual, that is, there is a least squares problem:

[0097]

[0098] Among them, argmin(·) means the optimal After finding the minimum value of the entire expression, return the corresponding variable value.

[0099] In this embodiment, The horizontal and vertical coordinate positions corresponding to the maximum value of the modulus of the range-Doppler information matrix R(n,m) and One-to-one correspondence is obtained, which can be expressed as:

[0100]

[0101] Since the objective function is a quadratic function of the amplitude x, when r and v are estimated, the optimal x is:

[0102]

[0103] Where N p =F s T p , M is the number of pulses.

[0104] Finally, the distance, Doppler frequency and amplitude value of the first detection unit that has completed detection are added to the valid target set.

[0105] Step 6: Repeat steps 1 to 5 until the judgment termination condition of the two-dimensional constant false alarm detector is met to output the final valid target parameter set:

[0106] Assume that the effective target set detected is:

[0107]

[0108] Then the residual of the signal is:

[0109]

[0110] The residual signal y r The data is sent to the data correction module, and the corrected data is sent to the constant false alarm detection module to determine whether the additional target exists. If not, the algorithm stops. Otherwise, the target parameters are estimated.

[0111] Until all target information in the echo signal is detected or the maximum number of iterations of the two-dimensional constant false alarm detector is reached, the distance-speed information of all targets in the valid target set is output.

[0112] This embodiment also provides a high-speed target detection device, such as Figure 2 As shown, it includes an echo data processing unit 510, a resampling unit 520, a velocity blur correction unit 530, a target data acquisition unit 540, a parameter estimation unit 550, and a target output unit 560;

[0113] The echo data processing unit 510 is used to obtain the echo signal of the pulse Doppler radar and perform linear processing to obtain the phase coupling data of the slow time dimension;

[0114] The resampling unit 520 is used to resample the phase-coupled data in the slow time dimension to obtain decoupled data;

[0115] The speed fuzzy correction unit 530 is used to construct a matched filter of speed fuzzy number for the decoupled data to search the speed fuzzy number, and perform speed fuzzy compensation on the decoupled data to obtain corrected data;

[0116] The target data acquisition unit 540 is used to perform linear transformation on the correction data to obtain the target data;

[0117] The parameter estimation unit 550 is used to calculate the square of the amplitude modulus value of the two-dimensional data of the distance dimension and the speed dimension in the target data, and take the point with the highest value of the square of the amplitude modulus value as the first detection unit, input it into the pre-built two-dimensional constant false alarm detector for detection, and add the distance, speed and amplitude value corresponding to the first detection unit detected as a valid target into the valid target parameter set;

[0118] The target output unit 560 is used to remove the first result from the echo signal, and repeat the echo data processing unit, the resampling unit, the velocity ambiguity correction unit, the target data acquisition unit, and the parameter estimation unit until the judgment termination condition of the two-dimensional constant false alarm detector is met, and the final valid target set is output to obtain the distance and velocity information of all targets in the echo signal.

[0119] In order to better illustrate the technical effect of the method provided in this embodiment, the following test scenario is set: Pulse radar parameter setting in the experiment: carrier frequency f c =3GHz; pulse width T p =2μs; pulse repetition interval T = 100μs; bandwidth B = 3MHz; chirp rate μ = B / T p ; Blind speed v amb =500m / s; sampling frequency F s=7MHz; number of pulses = 1024, corresponding to a distance resolution of 21.4m and a speed resolution of 0.49m / s.

[0120] Target parameter settings: Target 1 speed v1 = 2400m / s, distance r1 = 6006m; Target 2 speed v2 = 1450m / s, distance r2 = 5576m; Target 3 speed v3 = -1880m / s, distance r3 = 4719m. The speed fuzzy numbers corresponding to the three targets are γ1 = 5, γ2 = 3, γ3 = -2; the cumulative signal-to-noise ratio is SNR = 18dB; the false alarm probability P fa =10 -8 , the targets span 11.46, 6.93, and -8.98 distance units, respectively (where “-” indicates the opposite direction).

[0121] The high-speed target detection method for pulse Doppler radar provided in this embodiment is used to detect each target in the test scene:

[0122] like Figure 3 As shown, this is the detection result of target one in the test scene. Figure 3 The white box in the middle is the actual target parameter reference range of target one.

[0123] like Figure 4 As shown, this is the detection result of target 2 in the test scene. Figure 4 The white box in the middle is the actual target parameter reference range of target two.

[0124] like Figure 5 As shown, this is the detection result of target three in the test scene. Figure 4 The white box in the middle is the actual target parameter reference range of target two.

[0125] like Figure 6 As shown, there are three target speed fuzzy number detection results, according to which it can be calculated that the distance error is less than one distance resolution unit, the speed error is less than one distance resolution unit, and the speed fuzzy number estimation accuracy is high.

[0126] Based on the same test scenario, the traditional pulse compression method based on matched filtering is used for detection:

[0127] like Figure 7 The following is the detection result of target one in the test scene. Figure 8 The following is the detection result of target 2 in the test scene. Fig. 9 The following is the detection result of target 3 in the test scene. The target energy is too dispersed and cannot be identified because the matched filter method cannot correct the distance migration. Figure 7 The goal of Figure 8 The second goal is to Fig. 9 For target three, the number of targets is overestimated due to the strong scattered energy.

[0128] In summary, the traditional pulse compression method cannot handle the problem of velocity ambiguity, so the velocity estimation range is limited to -250m / s to 250m / s, and it is impossible to estimate the velocity of high-speed targets.

[0129] The high-speed target detection method provided in this embodiment can meet the requirements for distance estimation and speed estimation of the target under high-speed conditions.

Claims

1. A high-speed target detection method for pulse Doppler radar, characterized in that: The following steps are involved: Step 1: Acquire the echo signal emitted by the pulse Doppler radar, and perform linear processing on the echo signal to obtain initial data of the speed and distance coupling corresponding to each target; Step 2: resampling the initial data in the slow time dimension to obtain resampled data after decoupling of speed and distance; Step 3, compensating the resampled data by a pre-built velocity fuzzy number matched filter to obtain corresponding corrected data; Step 4: performing linear transformation processing on the corrected data to obtain target data; Step 5, extracting first data in the distance dimension and second data in the speed dimension of the target data, and calculating the square of the amplitude modulus of the first data and the second data, and taking the highest value point of the square of the amplitude modulus as the input of the pre-built two-dimensional constant false alarm detector for detection; If a valid target is detected, the distance, speed and amplitude of the target in the input target data are added to the valid target parameter set; Otherwise, the corresponding target data will be eliminated; Step 6: Repeat steps 1 to 5 until the judgment termination condition of the two-dimensional constant false alarm detector is met to output a final valid target parameter set.

2. The high-speed target detection method for pulse Doppler radar according to claim 1, characterized in that: The pulse Doppler radar is a shore-based radar, an airborne radar or a ship-based radar.

3. The high-speed target detection method for pulse Doppler radar according to claim 1, characterized in that: In step 1, the specific process of the linear processing is as follows: The matched filtering method is used to compress the echo signal, and the signal data obtained by pulse compression is subjected to fast time dimension FFT to obtain the initial data of slow time dimension phase coupling.

4. The high-speed target detection method for pulse Doppler radar according to claim 1, characterized in that: In step 2, in the slow time dimension, satisfy t m =[f c / (f c +f)]τ m Under the condition, resampling is completed by the sinc interpolation method, where τ m is the virtual time, t m The original time.

5. The high-speed target detection method for pulse Doppler radar according to claim 4, characterized in that: The resampling expression is as follows: Among them, m is the original sampling point, m' is the sampling point after sinc interpolation, f is the fast time frequency, and f c is the carrier frequency and M is the number of pulses.

6. The high-speed target detection method for pulse Doppler radar according to claim 1, characterized in that: In step 3, the expression of the correction data is as follows: in, represents the velocity ambiguity number used to compensate the resampled data, τ m For virtual time, Represents the velocity fuzzy number matched filter.

7. The high-speed target detection method for pulse Doppler radar according to claim 1, characterized in that: In step 4, the linear transformation process is as follows: The corrected data is subjected to inverse fast Fourier transform in the fast time dimension and fast Fourier transform in the slow time dimension to obtain target data in the range-Doppler dimension.

8. The high-speed target detection method for pulse Doppler radar according to claim 1, characterized in that: In step 5, the detection process of the two-dimensional constant false alarm detector is as follows: Eliminate the protection units near the maximum square value of the input amplitude modulus value in all training units; Calculate the average value of all data except the maximum square value of the amplitude modulus value to obtain the noise floor value; A threshold value of a valid target is determined based on the background noise value, and a ratio of the maximum square value of the amplitude modulus value to the background noise value is compared with the threshold value: If the ratio is greater than the threshold, it is considered that there is a valid target, otherwise it is considered that there is an ineffective target.

9. The high-speed target detection method for pulse Doppler radar according to claim 1, characterized in that: The judgment termination conditions include: a. The number of repetitions has reached the maximum number of iterations preset by the constant false alarm detector; b. The constant false alarm detector determines that there is no target, that is, the detection statistic corresponding to the maximum point does not exceed the corresponding threshold.

10. A high-speed target detection device, characterized in that: The method is used to execute the steps of the high-speed target detection method for pulse Doppler radar as described in any one of claims 1 to 9.

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