Dual-channel parallel detection airborne phased array radar ground moving target extraction method

Abstract

The application discloses a dual-channel parallel detection airborne phased array radar ground moving target extraction method, comprising the following steps: acquiring coherent pulse train data of an airborne phased array radar; forming sum-difference beam data according to preset sum-difference beam weighting coefficients; constructing a pulse compression matching function to perform pulse compression on the sum-difference beam data; performing platform motion compensation and pulse Doppler processing on the pulse compressed sum-difference beam data; performing high-speed channel determination according to amplitude statistical results of each Doppler unit of the sum beam, and performing CFAR detection; performing low-speed channel determination according to the amplitude statistical results of each Doppler unit of the sum beam, processing low-speed channel data by using sum-difference 3DT-STAP algorithm, and performing CFAR detection and amplitude normalization processing on the processed data; merging the CFAR detection results of the high-speed channel data and the CFAR detection and amplitude normalization processing results of the low-speed channel data, and extracting a point track.

Description

TECHNICAL FIELD

[0001] The present application relates to radar target detection technology. More particularly, it relates to a dual-channel parallel detection method for extracting ground moving targets of airborne phased array radar. BACKGROUND

[0002] At present, when the airborne phased array radar works in the ground detection mode, target detection is seriously affected by strong ground and sea clutter. Due to the high-speed motion of the aircraft platform, there is serious folding and confusion in the Doppler velocity dimension between the clutter and the target, and compared with the static platform, the clutter spectrum of the moving platform radar exists a certain spread. Pulse Doppler processing can detect high-speed moving targets outside the clutter spectrum, but low-speed targets located at the edge of the clutter spectrum cannot be detected. Space Time Adaptive Processing (STAP) utilizes the difference in the angle-Doppler distribution between the target and the clutter, and calculates the space-time two-dimensional filter weight vector in real time according to the statistical characteristics of the clutter and noise, which can theoretically separate the low-speed moving target from the ground clutter, but the calculation amount is huge, and the engineering application faces great challenges. SUMMARY

[0003] The purpose of the present application is to provide a dual-channel parallel detection method for extracting ground moving targets of airborne phased array radar, so as to solve at least one of the problems existing in the prior art.

[0004] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows:

[0005] The present application provides a dual-channel parallel detection method for extracting ground moving targets of airborne phased array radar, comprising:

[0006] Obtaining coherent pulse train data of the airborne phased array radar;

[0007] Forming sum beam data and difference beam data from the coherent pulse train data according to preset sum beam weighting coefficients and difference beam weighting coefficients;

[0008] Constructing a pulse compression matching function to perform pulse compression on the sum beam data and the difference beam data;

[0009] Performing platform motion compensation and pulse Doppler processing on the pulse compressed sum beam data and difference beam data;

[0010] Performing high-speed channel determination according to the amplitude statistical results of each Doppler unit of the sum beam, and performing CFAR detection on the high-speed channel data;

[0011] Low speed channel is determined according to the sum and difference beam each Doppler unit amplitude statistics result, low speed channel data is processed using sum and difference 3D T-STAP algorithm, and the processed data is subjected to CFAR detection and amplitude normalization processing;

[0012] The high speed channel data CFAR detection result and the low speed channel data CFAR detection and amplitude normalization processing result are combined, and a point track is extracted.

[0013] Optionally, the acquiring the coherent pulse train data of the airborne phased array radar comprises

[0014] The acquired coherent pulse train data DataEcho of the airborne phased array radar is recorded,

[0015] The coherent pulse train data DataEcho is an N cell ×N range ×N pulse data matrix, wherein N cell is a number of radar front-end digital receiving units, N range is a number of distance units, and N pulse is a number of coherent pulses.

[0016] Optionally, the preset sum beam weighting coefficient is w sum , the preset difference beam weighting coefficient is w diff , and both are N cell ×1 vectors.

[0017] The sum beam data and the difference beam data are respectively

[0018]

[0019]

[0020] wherein

[0021] DataSum and DataDiff are both N range ×N pulse matrices, and [·] T is a transposition operation.

[0022] Optionally, the pulse compressed sum beam data and the pulse compressed difference beam data are respectively

[0023] DataPC sum (:,p)=conv(DataSum(:,p),h PC )

[0024] DataPC diff (:,p)=conv(DataDiff(:,p),h PC ),

[0025] where h PC is the pulse compression time-domain matching function, p = 1, …, N pulse , DataPC sum and DataPC diff are N range × N pulse matrices, and conv(·) is a convolution operation function.

[0026] Optionally, the platform motion compensation on the pulse compressed sum beam data and difference beam data comprises

[0027] Supposing the height of the airborne platform is H radar , the motion velocity is (v x , v y , v z ), and the beam center azimuth angle is Each range cell distance measurement value is Range(n), n = 1, …, N range , the corresponding down-view angle is calculated as α Rt = asin(H radar / Range(n)), and the radial velocity of the platform relative to the range cell is The platform motion compensation phase factor is φ compen = 4πPRT[0:(N pulse -1)]Vr compen / λ,

[0028] where PRT is the pulse repetition period, λ is the wavelength, and the sum beam and difference beam pulse compressed data are multiplied by the phase factor for each range cell to perform platform motion compensation, and the platform motion compensated sum beam data and difference beam data are respectively

[0029] DataPC sum (n,:) = DataPC sum (n,:)exp{-jφ compen}

[0030]

[0031] Optionally, the platform motion compensated sum beam data and difference beam data are pulse Doppler processed.

[0032] The platform motion compensated sum beam data and difference beam data are pulse Doppler processed by using fast Fourier transform.

[0033] Supposing the FFT point number is N FFT , and the windowing coefficient is Win FFT , 1 × N pulse, the pulse Doppler processed sum beam and difference beam data are respectively

[0034] DataRD sum (n,:) = fft(DataPC sum (n,:) Win FFT ,N FFT )

[0035] DataRD diff (n,:) = fft(DataPC diff (n,:) Win FFT ,N FFT ),

[0036] wherein DataRD sum and DataRD diff are both N range ×N FFT matrices, n = 1, …, N range , and fft(·) is an FFT operation function.

[0037] Optionally, the high-speed channel determination according to the amplitude statistical result of each Doppler unit of the sum beam and the CFAR detection of the high-speed channel data comprise

[0038] statistically averaging the amplitudes of the plurality of range cell data in each Doppler unit of the sum beam data, and the statistical average value of the range cell data in the kth Doppler unit is

[0039] A D (k) = mean(20log 10 (abs(DataRD sum (:,k)))),

[0040] wherein k = 1, …, N FFT , mean(·) is an average operation function, and abs(·) is a modulus operation;

[0041] Supposing that the amplitude detection threshold is Thr PD = mean(A D (N FFT / 2-1:N FFT / 2+1))+Thr0, and Thr0 is a threshold factor;

[0042] if A D (k) < Thr PD , the kth Doppler unit is determined as a high-speed channel,

[0043] the sum beam Doppler data DataRD sumCFAR detection is performed on the distance dimension to obtain CFAR detected data DataCFAR High (:,k), DataCFAR High is an N range ×N FFT data matrix;

[0044] The kth Doppler cell that does not satisfy the high-speed channel determination criterion is set to zero.

[0045] Optionally, low-speed channel determination is performed according to the amplitude statistical results of each Doppler cell of the sum and difference beams, and sum-difference 3D T-STAP algorithm is used to process low-speed channel data, including

[0046] If (A D (k)≥Thr PD ) & (k>k0), the kth Doppler cell is determined as a low-speed channel,

[0047] k0 is the Doppler cell serial number of the zero-speed channel, and & is an AND operator.

[0048] The low-speed channel data is processed by using the sum-difference 3D T-STAP algorithm; the sum-difference 3D T-STAP steps are as follows:

[0049] S1: The sum and difference beam data of the kth Doppler cell are processed, and each distance cell n = 1, …, N range is processed according to

[0050] Data sort (n,:)=

[0051] [DataRD sum (n,k-1), DataRD diff (n,k-1), DataRD sum (n,k), DataRD diff (n,k), DataRD sum (n,k+1), DataRD diff (n,k+1)]

[0052] Data rearrangement is performed; for the nth distance cell, the clutter noise covariance matrix is calculated as

[0053]

[0054] Wherein num(l), l = 1, …, L is the serial number of the selected reference distance cell, [·] H is a conjugate transpose operation;

[0055] S2: The target steering vector is set as The sum-difference 3D T-STAP weight vector is calculated as

[0056]

[0057] where [·] -1 is the matrix inversion operation, then the processing result of the kth Doppler cell and the nth range cell is

[0058] DataSTAP Low is an N range ×N FFT data matrix;

[0059] Set the kth Doppler cell to zero if it does not meet the low-speed channel determination criterion.

[0060] Optionally, the CFAR detection and amplitude normalization processing of the data processed by the sum-difference 3D T-STAP algorithm includes:

[0061] CFAR detection is performed on all Doppler cell data DataSTAP Low of the low-speed channel along the range dimension to obtain the data DataCFAR Low after CFAR detection, and DataCFAR Low is an N range ×N FFT data matrix;

[0062] If the kth Doppler cell and the nth range cell meet the CFAR detection threshold, the amplitude of the cell is normalized to the amplitude of the corresponding cell of the data after sum-beam Doppler processing, that is,

[0063] DataCFAR Low (n, k) = DataRD sum (n, k).

[0064] Optionally, the CFAR detection result of the high-speed channel data and the CFAR detection and amplitude normalization processing result of the low-speed channel data are combined, and a plot is extracted, including

[0065] The CFAR detection result of the high-speed channel and the CFAR detection result of the low-speed channel are combined to obtain the double-channel combined data DataCFAR = DataCFAR High + DataCFAR Low .

[0066] The data that meet the threshold in the double-channel combined data DataCFAR are correlated according to the range-Doppler cell two-dimensional sliding window, and a plot is extracted according to the amplitude weighted condensation, the actual range cell size and the speed size corresponding to the Doppler cell are converted into real range values and speed values, and the plot extraction result is output.

[0067] The beneficial effects of the present application are as follows:

[0068] The present application determines high-speed channels and low-speed channels for Doppler units, directly uses CFAR detection for high-speed channels, uses ∑△-3DT-STAP and then CFAR detection and amplitude normalization for low-speed channels, and finally combines and outputs point extraction results of two channels. BRIEF DESCRIPTION OF DRAWINGS

[0069] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0070] Figure 1 An exemplary method flowchart in which an embodiment of the present application can be applied is shown.

[0071] Figure 2 A schematic diagram of a matrix of coherent pulse train data of an airborne phased array radar obtained in step one of the present application is shown.

[0072] Figure 3 Sum beam and difference beam data formed by simulation in an embodiment of the present application are shown.

[0073] Figure 4 Pulse compression processing results of sum beam and difference beam data in an embodiment of the present application are shown.

[0074] Figure 5 Pulse Doppler processing results of sum beam and difference beam data after platform motion compensation in an embodiment of the present application are shown.

[0075] Figure 6 Sum beam Doppler unit amplitude mean values statistically obtained in an embodiment of the present application are shown.

[0076] Figure 7 High-speed channel Doppler unit and CFAR detection results in an embodiment of the present application are shown.

[0077] Figure 8 Low-speed channel Doppler unit and ∑△-3DT-STAP and CFAR detection results in an embodiment of the present application are shown.

[0078] Figure 9 Data combination results after high-speed channel and low-speed channel detection in an embodiment of the present application are shown.

[0079] Figure 10 Point extraction results after two-channel combination in an embodiment of the present application are shown. DETAILED DESCRIPTION

[0080] For a clearer explanation of the present application, the present application is further described below in conjunction with embodiments and drawings. Like components are denoted by like reference numerals in the drawings. It should be understood by those skilled in the art that the specific description below is illustrative rather than limiting, and the scope of protection of the present application should not be limited thereby.

[0081] In one specific embodiment, a dual-channel parallel detection airborne phased array radar ground moving target extraction method includes:

[0082] Step one: obtaining coherent pulse train data of an airborne phased array radar;

[0083] Obtaining coherent pulse train data DataEcho of the airborne phased array radar, DataEcho being an N cell ×N range ×N pulse data matrix, as shown in the schematic diagram Figure 2 , wherein N cell is the number of radar front-end digital receiving units, N range is the number of distance units, and N pulse is the number of coherent pulses;

[0084] Step two: forming sum beam data and difference beam data from the coherent pulse train data according to preset sum beam weighting coefficients and difference beam weighting coefficients;

[0085] The preset sum beam weighting coefficients and difference beam weighting coefficients are w sum and w diff , respectively, both of which are N cell ×1 vectors,

[0086] Sum beam data and difference beam data are and

[0087] DataSum and DataDiff are both N range ×N pulse matrices, [·] T is a transpose operation, and the sum and difference beam data simulated in the embodiment are shown in the schematic diagram Figure 3 ;

[0088] Step three: constructing a pulse compression matching function to perform pulse compression on the sum beam data and the difference beam data;

[0089] The constructed pulse compression time domain matching function is h PC , and the pulse compression is performed on the sum beam and the difference beam data, each pulse data being convoluted with the time domain matching function to obtain pulse-compressed sum beam and difference beam data, respectively

[0090] DataPCsum DataPC(:,p) = conv(DataSum(:,p), h PC )

[0091] DataPC diff (:,p) = conv(DataDiff(:,p), h PC ),

[0092] where p = 1,..., N pulse , DataPC sum and DataPC diff are N range x N pulse matrices, conv(·) is a convolution operation function, and the simulation data and the beam and difference beam pulse compression processing results are shown in Figure 4

[0093] Step four: platform motion compensation and pulse Doppler processing are performed on the sum beam data and the difference beam data after pulse compression.

[0094] Suppose the airborne platform height is H radar , the motion velocity is (v x , v y , v z ), the beam center azimuth angle is , and the distance measurement value of each distance unit is Range(n), n = 1,..., N range , the corresponding downward angle is a Rt = asin(H radar / Range(n)), and the radial velocity of the platform relative to the distance unit is The platform motion compensation phase factor is compen = 4pRT[0:(N pulse -1)]Vr compen / λ,

[0095] where PRT is the pulse repetition period, λ is the wavelength, the sum beam and difference beam data after pulse compression are multiplied by the phase factor for each distance unit to perform platform motion compensation, and the sum beam data and the difference beam data after platform motion compensation are respectively

[0096] DataPC sum (n,:) = DataPC sum (n,:)exp{-j compen}

[0097]

[0098] ​After the platform motion compensation, the fast Fourier transform (FFT) is used for pulse Doppler processing,

[0099] Let the FFT point number be N FFT = 64, and the windowing coefficient be Win FFT , 1 x N pulse , take the 60 dB Chebyshev window, and the pulse Doppler processing results of the sum beam and difference beam data are respectively

[0100] DataRD sum (n, :) = fft(DataPC sum (n, :) Win FFT , N FFT )

[0101] DataRD diff (n, :) = fft(DataPC diff (n, :) Win FFT , N FFT ),

[0102] wherein DataRD sum and DataRD diff are both N range x N FFT matrices, n = 1, …, N range , and fft(·) is an FFT operation function; the pulse Doppler processing results of the sum beam and difference beam data after the simulation data platform motion compensation in the embodiment are shown in Figure 5 ;

[0103] Step five: according to the amplitude statistical results of each Doppler unit of the sum beam, high-speed channel determination is performed, and CFAR detection is performed on the high-speed channel data;

[0104] The amplitude statistical average of a plurality of distance unit data in each Doppler unit of the sum beam data is taken, and the amplitude statistical average value of the distance unit data in the kth Doppler unit is

[0105] A D (k) = mean(20log 10 (abs(DataRD sum (:, k)))),

[0106] wherein k = 1, …, N FFT , mean(·) is an average operation function, and abs(·) is a modulus operation;

[0107] The amplitude average value of the Doppler unit of the sum beam of the simulation data in the embodiment is shown in Figure 6 ;

[0108] Let the amplitude detection threshold be Thr. PD =mean(A D (N FFT / 2-1:N FFT / 2+1))+Thr0, where Thr0 is the threshold factor;

[0109] If A D (k) <Thr PD Then the k-th Doppler unit is determined to be a high-speed channel.

[0110] DataRD for the k-th Doppler cell and beam Doppler data sum (:,k) performs Constant False Alarm Rate (CFAR) detection along the distance dimension to obtain the CFAR-detected data, DataCFAR. High (:,k), DataCFAR High For N range ×N FFT The data matrix;

[0111] Set the k-th Doppler cell that does not meet the high-speed channel determination criteria to zero. (DataCFAR) High (:,k)=0; The simulation data high-speed channel determination results and CFAR detection results in the embodiment are as follows: Figure 7 As shown, the average amplitude of the distance data within the Doppler cell of the high-speed channel data is less than that of the low-speed data channel data.

[0112] Step 6: Determine the low-speed channel based on the amplitude statistics of each Doppler unit of the sum beam, and process the low-speed channel data using the sum-difference 3DT-STAP algorithm;

[0113] For the k-th Doppler unit, if (A D (k)≥Thr PD If ) & (k>k0), then the k-th Doppler cell is determined to be the low-speed channel, k0 is the Doppler cell number of the zero-speed channel, and & is the AND operator.

[0114] Low-speed channel data is processed using the sum-difference 3DT-STAP (∑△-3DT-STAP) algorithm; the ∑△-3DT-STAP steps are as follows:

[0115] (1) For the k-th Doppler cell and beam difference beam data, each range cell n = 1, ..., N range All according to

[0116] Data sort (n,:)=[DataRD sum (n,k-1),DataRDdiff (n, k-1), DataRD sum (n, k), DataRD diff (n, k), DataRD sum (n, k+1), DataRD diff (n, k+1)

[0117] performing data rearrangement;

[0118] (2) For the nth range cell, the clutter noise covariance matrix is calculated as

[0119]

[0120] where num(l), l = 1, …, L is the selected reference range cell index, [·] H is the conjugate transpose operation;

[0121] (3) Let the target steering vector be The ∑△-3DT-STAP weight vector is calculated as

[0122]

[0123] where [·] -1 is the matrix inverse operation, then the processing result of the nth range cell of the mth Doppler cell is k

[0124]

[0125] DataSTAP Low is an N range ×N FFT data matrix, and the kth Doppler cell that does not satisfy the low-speed channel determination criterion is set to zero DataSTAP Low (:, k) = 0.

[0126] Step seven: performing CFAR detection and amplitude normalization processing on the data processed by the sum and difference 3DT-STAP algorithm;

[0127] The data DataSTAP Low of all Doppler cells of the low-speed channel is subjected to CFAR detection along the range dimension to obtain the data DataCFAR Low after CFAR detection, and DataCFAR Low is an N range ×N FFT data matrix;

[0128] ​If the (k, n)th Doppler cell meets the CFAR detection threshold, the amplitude of the cell is normalized to the amplitude of the corresponding cell of the data after the beam-pulse Doppler processing, i.e. DataCFAR Low (n, k) = DataRD sum (n, k) = DataRD

[0129] The low-speed channel determination result and the CFAR detection result of the ∑△-3D T-STAP of the simulation data in the embodiment are shown in Figure 8

[0130] Step eight: combine the CFAR detection result of the high-speed channel data and the CFAR detection result and the amplitude normalization processing result of the low-speed channel data, and extract the plot;

[0131] The high-speed channel CFAR detection result and the low-speed channel CFAR detection result are combined to obtain the dual-channel combined data DataCFAR = DataCFAR High + DataCFAR Low The dual-channel combined result of the simulation data in the embodiment is shown in Figure 9

[0132] The over-threshold data in DataCFAR is correlated according to the two-dimensional sliding window of the range-Doppler cell, and the plot is extracted based on the amplitude weighting condensation. Finally, the real range value and the speed value are converted according to the actual range cell size and the corresponding speed size of the Doppler cell, and the plot extraction result is output. The plot extraction result of the simulation data in the embodiment is shown in Figure 10

[0133] The five target speeds and distances of the simulation setting are (2 m / s, 11.25 km), (2.5 m / s, 12 km), (3 m / s, 12.75 km), (10 m / s, 12 km), and (20 m / s, 12 km). It can be seen from the plot extraction result that the five targets are all detected, and one plot at 13 km is a clutter plot generated by the remaining clutter; the simulation data processing result verifies the effectiveness of the dual-channel parallel detection airborne phased array radar ground moving target extraction method.

[0134] ​​​In the description of the present application, it needs to be explained that the terms "upper", "lower" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise expressly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication between two elements inside. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0135] It also needs to be explained that in the description of the present application, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or equipment. Without more limitation, the element defined by the sentence "including a…" does not exclude the presence of other identical elements in the process, method, article or equipment including the element.

[0136] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not a limitation on the embodiments of the present application. For those skilled in the art, on the basis of the above description, other different forms of changes or variations can also be made, and it is impossible to enumerate all the embodiments here. Any obvious changes or variations derived from the technical solutions of the present application are still within the protection scope of the present application.

Claims

1. A dual-channel parallel detection airborne phased array radar ground moving target extraction method, characterized in that, The method comprises the following steps: acquiring coherent pulse train data of an airborne phased array radar; forming sum beam data and difference beam data according to preset sum beam weighting coefficients and difference beam weighting coefficients based on the coherent pulse train data; constructing a pulse compression matching function to perform pulse compression on the sum beam data and the difference beam data; performing platform motion compensation and pulse Doppler processing on the pulse compressed sum beam data and the pulse compressed difference beam data; performing high-speed channel determination based on amplitude statistical results of each Doppler unit of the sum beam, and performing CFAR detection on high-speed channel data; performing low-speed channel determination based on amplitude statistical results of each Doppler unit of the sum beam, processing low-speed channel data by using sum-difference 3D T-STAP algorithm, and performing CFAR detection and amplitude normalization processing on the processed data; merging the CFAR detection result of the high-speed channel data and the CFAR detection and amplitude normalization processing result of the low-speed channel data, and extracting a track; the high-speed channel determination based on the amplitude statistical results of each Doppler unit of the sum beam and the CFAR detection on the high-speed channel data comprise Statistical averaging of the data amplitudes of the plurality of range cells within each Doppler cell in the beam data, and k Statistical averaging of the data amplitudes of the plurality of range cells within each Doppler cell in the beam data, and ， wherein , is an averaging function, is a modulo operation; Let the amplitude detection threshold be , is a threshold factor; If , then determine the first Doppler unit as a high-speed channel, To the first Doppler unit and beam Doppler data CFAR detection along the distance dimension, to obtain CFAR detection data , Data matrix ​ zeroing the Doppler units for the first that do not satisfy the high-speed lane criteria the low-speed channel determination based on the amplitude statistical results of each Doppler unit of the sum beam and the processing of the low-speed channel data by using the sum-difference 3D T-STAP algorithm comprise If , then determine the first Doppler unit as a low-speed channel, zero velocity channel Doppler unit number, is the "and" operator; the low-speed channel data are processed by using the sum-difference 3D T-STAP algorithm; the sum-difference 3D T-STAP step is: S1: The first Doppler unit and beam and difference beam data, each range unit is processed according to performing data rearrangement; , and are the pulse Doppler processed and beam pulse data, respectively; , and are the pulse Doppler processed difference pulse data, respectively; a first distance unit, and a second distance unit​ wherein is a selected reference distance unit index, is a conjugate transpose operation; S2: Set target steering vector as , calculate and difference 3DT-STAP weight vector as ， wherein is the inverse operation of the matrix, then the first Doppler unit the first range unit processing result is ; is a data matrix; The Doppler units of the first are zeroed for which the low speed channel criterion is not fulfilled.

2. The method of claim 1, wherein, the coherent pulse train data of the airborne phased array radar comprise Recording acquired coherent burst data of an airborne phased array radar , The coherent pulse train data is a data matrix, wherein is the number of radar front-end digital receiving units, is the number of distance units, is the number of coherent pulses.

3. The method of claim 2, wherein, The preset and the beam weighting coefficient are , the preset difference beam weighting coefficient is , both are vectors of , the sum beam data and the difference beam data are respectively ， wherein and are both matrices, is the transpose operation.

4. The method of claim 3, wherein, the pulse compressed sum beam data and the pulse compressed difference beam data are respectively ， wherein is a pulse compression time domain matching function, , and are matrices, is a convolution operation function.

5. The method of claim 4, wherein, the platform motion compensation on the pulse compressed sum beam data and the pulse compressed difference beam data comprises Let the height of the airborne platform be , the motion velocity be , the beam center azimuth be , and the distance measurement value of each distance unit be , , the corresponding down-view angle be , the radial velocity of the platform relative to the distance unit be , and the platform motion compensation phase factor be , wherein is the pulse repetition period, is the wavelength, and the sum and difference beam data are each multiplied by the phase factor to perform platform motion compensation, and the platform motion compensated sum and difference beam data are respectively ， ， 。 6. The method of claim 5, wherein, the pulse Doppler processing on the platform motion compensated sum beam data and the platform motion compensated difference beam data comprises the pulse Doppler processing on the platform motion compensated sum beam data and the platform motion compensated difference beam data by using fast Fourier transform; Let FFT point number be , the windowing coefficient be , the pulse Doppler processed sum beam pulse data and difference pulse data be ， wherein and are both matrices, , is an FFT operation function.

7. The method of claim 1, wherein, the CFAR detection and amplitude normalization processing on the data processed by using the sum-difference 3D T-STAP algorithm comprise all doppler cell data for low speed channel CFAR detection along range dimension to get post-CFAR detection data , data matrix for ​ If the Doppler cell and the range cell satisfy the CFAR detection threshold, the cell amplitude is normalized to the cell amplitude corresponding to the data after the beam pulse Doppler processing, i.e. ​​ 。 8. The method of claim 1, wherein, the merging of the CFAR detection result of the high-speed channel data and the CFAR detection and amplitude normalization processing result of the low-speed channel data and the extraction of a track comprise The high-speed channel CFAR detection result is combined with the low-speed channel CFAR detection result to obtain double-channel combined data ; Merging data of two channels The threshold-crossing data is correlated according to a two-dimensional sliding window of the range-Doppler unit, and a tracklet is extracted based on amplitude-weighted condensation. The tracklet extraction result is converted into a real range value and a speed value according to an actual range unit size and a speed size corresponding to the Doppler unit, and output.

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