Dimension reduction processing method for space-time joint foresight imaging of airborne array radar

Abstract

The invention discloses a dimension reduction processing method for space-time joint foresight imaging of an airborne array radar, and the method comprises the steps: constructing a foresight imaging geometric model of the airborne array radar, receiving a distance, direction and channel three-dimensional echo matrix, converting data in a single coherent processing time interval into a frequency domain for the echo matrix, and compensating a Doppler phase, and then, truncating the effective information of the frequency spectrum to obtain snapshot data after dimension reduction. And then applying the newly obtained space-time snapshots to space-time adaptive iterative super-resolution imaging, estimating space-time super-resolution spectrum curves corresponding to the space-time snapshots of the distance-azimuth units, and performing spectrum accumulation on the super-resolution spectrum curves of the distance-azimuth units according to the motion speed of an aerial carrier and antenna scanning parameters to finally obtain a two-dimensional forward view image. According to the method, effective echo information is intercepted in the Doppler domain, space-time data are effectively compressed, the operation complexity is remarkably reduced, and meanwhile the imaging quality is kept.

Description

Technical Field

[0001] The present invention belongs to the field of radar imaging technology, and relates to an airborne radar forward-looking imaging signal processing technology, and specifically relates to a dimensionality reduction processing method for space-time joint forward-looking imaging of an airborne array radar. Background Art

[0002] Radar (Radio Detection and Ranging), as a long-range detection tool, has played an indispensable role in military, civilian, scientific research and other fields since its birth. With the continuous advancement of radar technology, radar forward imaging technology has gradually become a research hotspot in recent years. It overcomes the problem of failure of traditional imaging algorithms SAR or DBS technology in forward imaging, and can form accurate two-dimensional or three-dimensional images of the radar forward echo.

[0003] There are many types of technologies currently used in radar forward-looking imaging, including single pulse imaging technology, dual-base SAR forward-looking imaging technology, array radar super-resolution imaging technology, etc. Existing research focuses on the space-time two-dimensional super-resolution forward-looking imaging algorithm in array radar super-resolution imaging technology. This algorithm estimates the spatial spectrum by using a single space-time snapshot of each range-azimuth unit, and then accumulates the incoherent spectrum to form a two-dimensional forward-looking image. However, since this algorithm uses a space-time joint processing method, the system's degree of freedom is the product of the single domain degree of freedom, which is large in number. The higher degree of freedom directly increases the amount of algorithm calculations, making it difficult to process radar signals in real time.

[0004] Although a large number of classic dimension reduction and rank reduction algorithms and theories for space-time adaptive processing (STAP) have been proposed in the field of array signal processing, such as the 3DT algorithm, these algorithms are difficult to directly apply to the existing array radar super-resolution forward imaging technology. At present, for the problem of large data volume and complex calculation in radar real-time signal processing, the more common approach is to downsample and pre-filter the echo data in azimuth. However, directly downsampling the data in azimuth will sacrifice a certain resolution and affect the final imaging effect. Summary of the invention

[0005] Purpose of the invention: The present invention provides a dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar, which achieves the purpose of dimensionality reduction by truncating the effective information of the echo spectrum, greatly reduces the algorithm calculation amount and improves the calculation speed.

[0006] Technical solution: The dimensionality reduction processing method for space-time joint forward-looking imaging of an airborne array radar described in the present invention comprises the following steps:

[0007] (1) Construct a geometric model of airborne array radar forward-looking imaging, and use an airborne forward-looking array radar to obtain multi-channel echoes. The radar works in a step-scan mode, and transmits several coherent pulses at the same azimuth in each coherent pulse interval. The echoes are sampled to obtain range-pulse-array three-dimensional echo data.

[0008] (2) Processing the range-pulse-array three-dimensional echo data pulse by pulse and performing phase compensation, transforming the signal to the Doppler frequency domain, and then reducing the dimension of the data in the frequency domain to extract the effective information of the echo, and then transforming it to the time domain, and updating the parameters related to the guidance vector;

[0009] (3) constructing a space-time snapshot signal corresponding to each range-azimuth unit based on the multi-channel data after pulse compression; calculating the space-time steering vector corresponding to each azimuth angle to form a space-time steering vector matrix;

[0010] (4) Processing each range-azimuth unit, establishing a cost function based on the least squares criterion, and initializing the scattering coefficient. By continuously iteratively updating the echo autocorrelation matrix until the algorithm converges, the super-resolution spectrum curve corresponding to the space-time snapshot of the range-azimuth unit is calculated;

[0011] (5) According to the carrier motion speed and antenna scanning parameters, the super-resolution spectrum curve of each range-azimuth unit is spliced ​​in azimuth, that is, the spectrum curve is accumulated in the range-azimuth domain, and finally a range-azimuth imaging image is formed.

[0012] Furthermore, the implementation process of step (1) is as follows:

[0013] The carrier moves at a speed v a The array antenna system is fixed on the mobile platform along the track direction during constant speed flight. The array antennas are evenly distributed at equal intervals to form N s The radar scans the front area by stepping through a narrow beam. When the center of the beam points to a fixed azimuth angle θ, θ is the angle between the center of the beam and the speed of the carrier. In a coherent pulse interval, the radar transmitter transmits N p A coherent linear frequency modulated pulse is generated, and then the beam is directed to the next azimuth angle and the operation is repeated; finally, the different pulse echoes received by the same array element are sampled and arranged in columns to obtain the original range-pulse two-dimensional echo matrix; the received data of multiple array elements are combined to obtain the range-pulse-array three-dimensional echo data;

[0014] When the beam center points to the azimuth angle θ, the phase difference N composed of the target reaching the horizontal linear array s ×1-dimensional space steering vector:

[0015]

[0016] N is composed of the Doppler frequency generated by the relative motion between the radar and the target. p ×1-dimensional time steering vector:

[0017]

[0018] So the two guidance vectors form N s N p ×1-dimensional space-time steering vector:

[0019]

[0020] in, represents the Kronecker product, d is the channel spacing, λ is the signal wavelength, θ is the beam center pointing angle, and v a The carrier speed.

[0021] Furthermore, the implementation process of step (2) is as follows:

[0022] Calculate the phase difference that needs to be compensated for the echo pulse by pulse, and compensate the spectrum to zero frequency. The phase value that needs to be compensated is:

[0023]

[0024] Among them, R l (t) is the instantaneous slant distance from the array radar to the center of the current pulse scene;

[0025] Perform FFT transformation on the data of a single CPI at each distance, plot the spectrum before and after compensation, intercept the spectrum in the center of the spectrum, and inversely transform the spectrum to the time domain; perform the same operation on all range gates and all channels to obtain the echo matrix after dimensionality reduction processing.

[0026] Furthermore, the updating of the steering vector related parameters in step (2) is:

[0027]

[0028] Where β is the angle of the beam center corresponding to the current pulse.

[0029] Furthermore, the implementation process of step (3) is as follows:

[0030] Assume that there are K targets in the front imaging area, and their azimuth angles relative to the radar are θ1, θ2, …, θ K ; In one cycle scan, the received data is a three-dimensional sampling of range-pulse-array. For a certain range-azimuth unit, the pulse-array sampling data constitutes a two-dimensional space-time snapshot:

[0031] y=Ax+N

[0032] Where x is the echo signal reflected from the target, N is additive white Gaussian noise, independent of other components; A is the NM×K-dimensional space-time guidance vector matrix, A=[a(θ1),a(θ2),…,a(θ K )],in a s (θ k ) is an M×1-dimensional spatial guidance vector composed of the phase difference of the target reaching the horizontal linear array; a t (θ k ) is an N×1-dimensional time-steering vector composed of the Doppler frequencies generated by the relative motion between the radar and the target, represents the Kronecker product.

[0033] Furthermore, the implementation process of step (4) is as follows:

[0034] The scattering coefficient x of the target is accurately reconstructed and the weighted least squares criterion is used to obtain the estimation of the scattering coefficient:

[0035]

[0036] Among them, the autocorrelation matrix of the echo R = AP k A H +ρI, ρ is the regularization coefficient.

[0037] Furthermore, the estimation of the scattering coefficient uses an iterative method to continuously reduce the error between the estimated value and the true value. Specifically, the ground scattering coefficient is first initialized, the autocorrelation matrix R is set to the unit matrix, and then the scattering coefficient estimation formula is substituted to obtain ; then use the estimated value Update the autocorrelation matrix of the echo signal and then bring it into the scattering coefficient calculation formula again to obtain a more accurate ground scattering coefficient estimation result; repeat the above process until the algorithm converges to obtain the final scattering coefficient estimation value

[0038] Furthermore, the implementation process of step (5) is as follows:

[0039] The azimuth super-resolution spectrum curve corresponding to the space-time snapshot of each range-azimuth unit obtained in step (4) is spliced ​​at the corresponding azimuth angle for the azimuth super-resolution spectrum curves of different azimuth units in the same range gate, that is, the accumulation of the spectrum curve in the range-azimuth domain is achieved, and the range-azimuth imaging result can be obtained by displaying the accumulated stored data.

[0040] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: the present invention does not rely on a large amount of independent and identically distributed snapshot data, and mainly achieves the purpose of dimensionality reduction by truncating the effective information of the echo spectrum, and establishes the objective function under the least squares criterion, and estimates the autocorrelation matrix of the array received signal in an iterative manner, which greatly reduces the algorithm calculation amount and improves the calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of the present invention;

[0042] Figure 2 This is a schematic diagram of the geometric model of airborne array radar forward imaging;

[0043] Figure 3 The spectrum comparison diagram before and after compensation, where (a) is the spectrum diagram before compensation; (b) is the spectrum diagram after compensation;

[0044] Figure 4 This is a flowchart of dimensionality reduction processing;

[0045] Figure 5 Schematic diagram of the snapshot model when it is empty;

[0046] Figure 6 It is a schematic diagram of azimuthal super-resolution spectrum splicing;

[0047] Figure 7 This is the result diagram of real beam imaging of point target;

[0048] Figure 8 This is the result of space-time iterative super-resolution imaging of point targets;

[0049] Fig. 9 It is a comparison diagram of the imaging azimuth profile of point targets including real beam imaging and space-time imaging;

[0050] Fig.10 It is a comparison diagram of the imaging azimuth profile of point targets including space-time imaging with reduced dimension and space-time imaging without reduced dimension;

[0051] Fig.11 is the target scene graph;

[0052] Fig.12 Comparison diagram of target scene imaging results, where (a) is the real beam imaging result diagram, and (b) is the dimension-reduced space-time joint imaging result diagram; DETAILED DESCRIPTION

[0053] The present invention is further described in detail below with reference to the accompanying drawings.

[0054] like Figure 1 As shown, the present invention proposes a dimensionality reduction processing method for space-time joint forward-looking imaging of an airborne array radar, which specifically includes the following steps:

[0055] Step 1: Construct an airborne array radar forward-looking imaging model. Use an airborne forward-looking array radar to obtain multi-channel echoes. The radar works in a step-scan mode, emitting several coherent pulses at the same azimuth in each coherent pulse interval, and sampling the echoes to obtain range-pulse-array three-dimensional echo data.

[0056] A horizontal array antenna receiving system along the azimuth direction is used to receive echo signals from multiple channels, thereby realizing the sampling of spatial data. The radar array adopts a step-by-step scanning mode during operation. When the radar beam is aimed at a specific direction, continuous linear frequency modulation pulses are emitted according to the pulse repetition frequency (PRF) or interval (PRI) during a coherent pulse interval (CPI). In this process, the system will receive a series of echoes of coherent pulses to collect time series sampling data.

[0057] The geometric model of airborne array radar forward imaging is as follows: Figure 2 As shown, the carrier moves at a speed v a The array antenna system is fixed on the mobile platform along the track direction during constant speed flight. The array antennas are evenly distributed at equal intervals to form N s The radar scans the area ahead by stepping through a narrow beam. When the center of the beam points to a fixed azimuth angle θ (θ is the angle between the center of the beam and the speed of the carrier), the radar transmitter transmits N pulses within a coherent pulse interval (CPI). p The beam is then directed to the next azimuth angle and the operation is repeated. Finally, the different pulse echoes received by the same array element are sampled and arranged in columns to obtain the original range-pulse two-dimensional echo matrix. The received data of multiple array elements are combined to obtain the range-pulse-array three-dimensional echo data.

[0058] When the beam center points to the azimuth angle θ, the phase difference N composed of the target reaching the horizontal linear array s ×1-dimensional space steering vector:

[0059]

[0060] N is composed of the Doppler frequency generated by the relative motion between the radar and the target. p ×1-dimensional time steering vector:

[0061]

[0062] So the two guidance vectors form N s N p ×1-dimensional space-time steering vector:

[0063]

[0064] in, represents the Kronecker product, d is the channel spacing, λ is the signal wavelength, θ is the beam center pointing angle, and v a The carrier speed.

[0065] Step 2: Perform phase compensation on the range-pulse-array three-dimensional echo data and perform dimensionality reduction processing on the compensated data, as follows: Figure 4 shown.

[0066] Calculate the phase difference that needs to be compensated for the echo pulse by pulse, and compensate the spectrum to zero frequency. The phase value that needs to be compensated is:

[0067]

[0068] Among them, R l (t) is the instantaneous slant distance from the array radar to the center of the current pulse scene.

[0069] Perform FFT transformation on the data of a single CPI at each distance, and draw the spectrum before and after compensation. Figure 3 As shown, it can be seen that the effective information of the echo spectrum is moved to the vicinity of the zero frequency.

[0070] Then the spectrum of the central part of the spectrum is intercepted and the spectrum is inversely transformed to the time domain. The same operation is performed on all range gates and all channels, and finally the echo matrix with dimensionality reduction is obtained.

[0071] Since the echo is phase compensated, N generated by the relative motion between the radar and the target is required. p ×1-dimensional time steering vector, the updated steering vector is:

[0072]

[0073] Where β is the angle of the beam center corresponding to the current pulse.

[0074] Step 3: Construct the space-time snapshot signal corresponding to each range-azimuth unit based on the multi-channel data after pulse compression; calculate the space-time steering vector corresponding to each azimuth angle to form a space-time steering vector matrix.

[0075] The pulse compression operation in the range dimension is performed using the data collected by the airborne horizontal array radar system. By selecting the two-dimensional received data generated by each array element in each range-azimuth unit, the space-time snapshot signal of the corresponding range-azimuth unit can be constructed. Subsequently, these space-time snapshot signals are superimposed to convert the original two-dimensional data matrix into a one-dimensional data vector, such as Figure 5Then, for all azimuth angles in the imaging area, the corresponding space-time steering vector a(θ) is calculated and combined to form a space-time steering vector matrix A.

[0076] Assume that there are K targets in the front imaging area, and their azimuth angles relative to the radar are θ1, θ2, …, θ K In one cycle scan, the received data is a three-dimensional matrix of range-pulse-channel. For a certain range-azimuth unit, the pulse-array sampling data constitutes a two-dimensional space-time snapshot:

[0077] y=Ax+N

[0078] Where A is N p N s ×K-dimensional space-time steering vector matrix, A=[a(θ1),a(θ2),…,a(θ K )],in x is the echo signal reflected from the target, and N is additive white Gaussian noise, which is independent of other components.

[0079] Step 4: After receiving the space-time snapshot, the target distribution function after the antenna pattern weighting needs to be accurately reconstructed. Processing is done by range-azimuth unit. Considering that when the beam points to a certain direction, the interference and noise in the echo actually have a certain correlation, the weighted least squares criterion is used to establish the loss function:

[0080]

[0081] To minimize the sum of squared errors, expand the loss function and Take the derivative and set it to 0 to calculate the estimate of the scattering coefficient:

[0082]

[0083] The echo autocorrelation matrix R = AP k A H +ρI does not rely on a large amount of snapshot data, but is obtained through iteration, where ρ is the regularization coefficient and I is the unit matrix.

[0084] When an accurate estimate of x is required, an iterative method can be used to continuously reduce the error between the estimated value and the true value. The iterative process is established as follows:

[0085] First, the ground scattering coefficient is initialized, the autocorrelation matrix R is set to the unit matrix, and then the scattering coefficient estimation formula is substituted to obtain ; then use the estimated value Update the autocorrelation matrix of the echo signal and then bring it into the scattering coefficient calculation formula again to obtain a more accurate ground scattering coefficient estimation result; repeat the above process until the algorithm converges to obtain the final scattering coefficient estimation value

[0086] Step 5: According to the carrier movement speed and antenna scanning parameters (beam scanning range, beam scanning speed), the spectrum curve of each range-azimuth unit is spliced ​​in azimuth, that is, the spectrum curve is accumulated in the range-azimuth domain, and finally a range-azimuth imaging image is formed.

[0087] The azimuth spectrum curve corresponding to each range-azimuth unit space-time snapshot obtained in step 4 is summed up for the corresponding azimuth angles of the azimuth spectrum curves of different azimuth units in the same range gate. The schematic diagram is shown in Figure 6 As shown, the distance and azimuth imaging results can be obtained by displaying the accumulated and stored data.

[0088] Next, we simulate point targets. The three point targets are located at [2000m, -0.7°], [2000m, 1.2°], and [1900m, 1.2°], with the same amplitude and 32 dwell pulses. The 3dB width of the real beam is about 2.1 degrees. The real beam scanning imaging results are shown in Figure 1. Figure 7 As shown in Figure 2, it can be seen that after real beam imaging, two close targets at the same range gate cannot be distinguished. However, after imaging processing by the space-time adaptive iterative super-resolution algorithm, the three point targets are clearly visible. The imaging results are shown in Figure 2. Figure 8 shown. Fig. 9 This is a comparison diagram of the azimuth profile of real beam imaging and reduced-dimensional space-time imaging at the same range gate (2000m). Fig.10 The comparison of the azimuth profile of the reduced-dimensional space-time imaging is drawn for intercepting different length spectra, including 8, 16 and without interception. It can be seen that interception has little effect on the result, but it can significantly reduce the amount of algorithm calculation. The two targets at the same range gate are located at -0.7° and 1.2° respectively after imaging. This proves that the algorithm can effectively distinguish multiple targets in the main lobe, which provides feasibility for dimensionality reduction processing of airborne radar forward imaging.

[0089] The existing airborne SAR high-resolution complex image is selected as the ground simulation scene of the forward-looking area to be imaged, where the simulation scene is the ground building area. The target scene is shown in the figure Fig.11 As shown, radar forward-looking echo data is generated, and the number of dwell pulses is 64. During the dimensionality reduction process, the spectrum cutoff length is set to 16 and the dimensionality reduction is performed, and then the space-time joint imaging is performed. Fig.12(a) and (b) show the results of real beam imaging and reduced-dimensional space-time imaging, respectively. It can be seen that the imaging effect is good, the strong scattering points are clearly visible, and the contours of the surface targets are clear, which proves that the algorithm can effectively image the forward-looking scene while significantly reducing the amount of computation.

[0090] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar, characterized in that: The following steps are involved: (1) Construct a geometric model of airborne array radar forward-looking imaging, and use an airborne forward-looking array radar to obtain multi-channel echoes. The radar works in a step-scan mode, and transmits several coherent pulses at the same azimuth in each coherent pulse interval. The echoes are sampled to obtain range-pulse-array three-dimensional echo data. (2) Processing the range-pulse-array three-dimensional echo data pulse by pulse and performing phase compensation, transforming the signal to the Doppler frequency domain, and then reducing the dimension of the data in the frequency domain to extract the effective information of the echo, and then transforming it to the time domain, and updating the parameters related to the guidance vector; (3) constructing the space-time snapshot signal corresponding to each range-azimuth unit based on the multi-channel data after pulse compression; Calculate the space-time steering vector corresponding to each azimuth angle to form a space-time steering vector matrix; (4) Processing each range-azimuth unit, establishing a cost function based on the least squares criterion, and initializing the scattering coefficient. By continuously iteratively updating the echo autocorrelation matrix until the algorithm converges, the super-resolution spectrum curve corresponding to the space-time snapshot of the range-azimuth unit is calculated; (5) According to the carrier motion speed and antenna scanning parameters, the super-resolution spectrum curve of each range-azimuth unit is spliced ​​in azimuth, that is, the spectrum curve is accumulated in the range-azimuth domain, and finally a range-azimuth imaging image is formed.

2. The dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar according to claim 1 is characterized in that: The implementation process of step (1) is as follows: The carrier moves at a speed v a The array antenna system is fixed on the mobile platform along the track direction during constant speed flight. The array antennas are evenly distributed at equal intervals to form N s The radar scans the front area by narrow beam stepping. When the beam center points to a fixed azimuth angle θ, θ is the angle between the beam center and the carrier speed. In a coherent pulse interval, the radar transmitter transmits N p A coherent linear frequency modulated pulse is generated, and then the beam is directed to the next azimuth angle and the operation is repeated; finally, the different pulse echoes received by the same array element are sampled and arranged in columns to obtain the original range-pulse two-dimensional echo matrix; The received data of multiple array elements are combined to obtain the range-pulse-array three-dimensional echo data; When the beam center points to the azimuth angle θ, the phase difference N composed of the target reaching the horizontal linear array s ×1-dimensional space steering vector: N is composed of the Doppler frequency generated by the relative motion between the radar and the target. p ×1-dimensional time steering vector: So the two guidance vectors form N s N p ×1-dimensional space-time steering vector: in, represents the Kronecker product, d is the channel spacing, λ is the signal wavelength, θ is the beam center pointing angle, and v a The carrier speed.

3. The dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar according to claim 1, characterized in that: The implementation process of step (2) is as follows: Calculate the phase difference that needs to be compensated for the echo pulse by pulse, and compensate the spectrum to zero frequency. The phase value that needs to be compensated is: Among them, R l (t) is the instantaneous slant distance from the array radar to the center of the current pulse scene; Perform FFT transformation on the data of a single CPI at each distance, plot the spectrum before and after compensation, intercept the spectrum in the center of the spectrum, and inversely transform the spectrum to the time domain; perform the same operation on all range gates and all channels to obtain the echo matrix after dimensionality reduction processing.

4. The dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar according to claim 1, characterized in that: The updating parameters related to the steering vector in step (2) are: Where β is the angle of the beam center corresponding to the current pulse.

5. The dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar according to claim 1, characterized in that: The implementation process of step (3) is as follows: Assume that there are K targets in the front imaging area, and their azimuth angles relative to the radar are θ1, θ2, …, θ K ; In one cycle scan, the received data is a three-dimensional sampling of range-pulse-array. For a certain range-azimuth unit, the pulse-array sampling data constitutes a two-dimensional space-time snapshot: y=Ax+N Where x is the echo signal reflected from the target, N is additive white Gaussian noise, independent of other components; A is the NM×K-dimensional space-time guidance vector matrix, A=[a(θ1),a(θ2),…,a(θ K )],in a s (θ k ) is an M×1-dimensional spatial guidance vector composed of the phase difference of the target reaching the horizontal linear array; a t (θ k ) is an N×1-dimensional time-steering vector composed of the Doppler frequencies generated by the relative motion between the radar and the target, represents the Kronecker product.

6. The dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar according to claim 1, characterized in that: The implementation process of step (4) is as follows: The scattering coefficient x of the target is accurately reconstructed and the weighted least squares criterion is used to obtain the estimation of the scattering coefficient: Among them, the autocorrelation matrix of the echo R = AP k A H +ρI, ρ is the regularization coefficient.

7. The dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar according to claim 1, characterized in that: The estimation of the scattering coefficient adopts an iterative method to continuously reduce the error between the estimated value and the true value. Specifically, the ground scattering coefficient is initialized first, the autocorrelation matrix R is set to the unit matrix, and then the scattering coefficient estimation formula is substituted to obtain ; then use the estimated value Update the autocorrelation matrix of the echo signal and then bring it into the scattering coefficient calculation formula again to obtain a more accurate ground scattering coefficient estimation result; repeat the above process until the algorithm converges to obtain the final scattering coefficient estimation value 8. The dimensionality reduction processing method for space-time joint forward-looking imaging of airborne array radar according to claim 1, characterized in that: The implementation process of step (5) is as follows: The azimuth super-resolution spectrum curve corresponding to the space-time snapshot of each range-azimuth unit obtained in step (4) is spliced ​​at the corresponding azimuth angle for the azimuth super-resolution spectrum curves of different azimuth units in the same range gate, that is, the accumulation of the spectrum curve in the range-azimuth domain is achieved, and the range-azimuth imaging result can be obtained by displaying the accumulated stored data.

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