CN108845318B - Satellite-borne high-resolution wide-range imaging method based on Relax algorithm - Google Patents

Abstract

The invention discloses a satellite-borne high-resolution wide-width HRWS imaging method based on the Relax algorithm, and belongs to the technical field of radar target detection. According to the characteristics of spaceborne multi-channel radar echoes, the present invention designs an iterative method that can realize azimuth blur suppression after performing range compression, azimuth FFT, and converting the data to the range-Doppler domain, combined with the Relax algorithm . Finally, azimuth compression is performed on the signal after the azimuth blur suppression, and the blur-free imaging result can be obtained. The simulation results verify the feasibility and effectiveness of the method.

Description

Spaceborne high-resolution wide-format imaging method based on Relax algorithm

technical field

The invention belongs to the technical field of radar target detection, and relates to a signal processing algorithm of a multi-channel radar, in particular to a spaceborne high-resolution wide-width imaging method based on the Relax algorithm.

Background technique

Resolution and mapping bandwidth are two important imaging metrics for spaceborne synthetic aperture radar (SAR). On the one hand, high resolution can reflect the target feature information more accurately, which is convenient for target recognition and feature extraction, which is of great significance in military reconnaissance, urban mapping and disaster assessment. On the other hand, wide swaths can provide broader scene information to obtain global interpretation capabilities, which is beneficial to the observation of large areas such as land, forests, and oceans. However, for the traditional spaceborne SAR system, due to the limitation of the minimum antenna area, high resolution and wide swath are a pair of irreconcilable contradictory quantities, and the two performance indicators cannot be improved at the same time.

Realizing that the traditional single-transmit and single-receive system of spaceborne SAR cannot achieve high resolution and wide swath at the same time, researchers began to seek new methods. It has been found that the problem can be effectively solved by adopting the working mode of multiple channel reception. When imaging a wide area, a low pulse repetition frequency (PRF) needs to be used to avoid range ambiguity, which will result in azimuth ambiguity if the PRF is too low for the azimuth antenna. Therefore, the key to the spaceborne high-resolution wide-format imaging method lies in the design of the blur suppression method.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is:

Aiming at the shortcomings of the blur suppression method in high-resolution wide-field imaging of spaceborne SAR radar, an azimuth blur suppression method with better azimuth blur suppression effect is provided.

The present invention adopts the following technical solutions for solving the above-mentioned technical problems:

A spaceborne high-resolution wide-format imaging method based on the Relax algorithm, comprising the following steps:

Step 1: Perform range compression and azimuth FFT on the echo signals received by the spaceborne multi-channel radar to obtain data in the range-Doppler domain, and then quantify the same distance-multiple channels in the spaceborne multi-channel radar. The data from the Puller cells are combined to obtain the original signal:

Z(r,f _d )=(Z ₁ (r,f _d ),Z ₂ (r,f _d ),…,Z _M (r,f _d )) ^T

Among them, M is an odd number, indicating the number of channels, r is the distance unit, f _d is the Doppler frequency, and T is the transposed matrix;

Step 2, performing azimuth blur suppression on each range-Doppler unit of the original signal obtained in step 1 based on the Relax algorithm;

In step 3, the result obtained after the azimuth blurring is suppressed is compressed in the azimuth direction to obtain an imaging result.

Preferably, the azimuthal blur suppression in the step 2 includes the following steps:

Step 2-1, initialization, to obtain the preliminary estimated signal amplitude, that is, to complete the following operations:

where p represents the fuzzy number, f _p represents the pulse repetition frequency, H represents the conjugate transpose matrix, Z _rec (r,f _d +pf _p ) represents the preliminary estimated signal amplitude under different fuzzy numbers,

表示不同模糊数对应的导向矢量，其中

表示各通道的等效相位中心，v_a表示雷达平台的运动速度；

represents the steering vectors corresponding to different fuzzy numbers, where

represents the equivalent phase center of each channel, and v _a represents the moving speed of the radar platform;

即依次完成下述运算：Step 2-2, according to the original signal obtained in step 1 and the signal amplitudes under different fuzzy numbers preliminarily estimated in step 2-1, obtain signal components Z _p (r, f _d ) corresponding to different fuzzy numbers; then The steering vectors under different fuzzy numbers are used to match the corresponding signal components, and the estimated signal amplitudes are obtained.

That is, the following operations are performed in sequence:

Step 2-3: Repeat step 2-2 until the convergence condition is met.

Preferably, the convergence condition in the steps 2-3 is a formula

的值小于或等于10^-3。

is less than or equal to 10 ^-3 .

Preferably, in the step 2, the azimuth blur suppression for each range-Doppler unit is traversed by using two nested for loops, wherein one for loop traverses all Doppler units, and the other for Loop through all distance cells, the order of nesting of the two for loops is arbitrarily chosen.

Preferably, in the step 3, the azimuth compression is performed after arranging the azimuthal blur suppressed signals in the order of the blur multiples.

Compared with the prior art, the present invention adopts the above technical scheme, and has the following technical effects:

Compared with the traditional fuzzy suppression algorithm, the algorithm does not need to construct a reconstruction filter vector, and can directly use the known steering vector to suppress the blur through an iterative method, which is simpler and easier to implement, so it can be effectively used in the actual system. Applications.

Description of drawings

Fig. 1 is the spatial geometric relationship diagram of the spaceborne multi-channel radar.

Figure 2 is a flow chart of azimuth blur suppression based on Relax algorithm.

Figures 3a to 3c show the space-time sampling relationship, the phase distribution, and the eigenvalue distribution results of the signal-plus-noise covariance matrix, respectively.

Figures 4a to 4e show the simulated original echo signals of each channel, the range compression results of each channel, the range-Doppler domain results, the range-Doppler domain amplification results, and the azimuth spectrum of the original echoes. .

Figures 5a to 5d are the range-Doppler domain results after azimuth blur suppression based on the Relax algorithm, the range-Doppler domain amplification results, the range migration correction results, and the azimuth spectrum after azimuth blur suppression. .

Figures 6a to 6f are the imaging results after azimuth compression, in which Figure 6a is the imaging result before azimuth blur suppression, Figure 6b is the imaging result after Relax algorithm azimuth blur suppression, and Figure 6c is the image before azimuth blur suppression The 3D display of the results, Figure 6d is the 3D display of the imaging result after the azimuthal blurring of the Relax algorithm is suppressed, Figure 6e is the azimuthal slice before the azimuthal blurring is suppressed, and Figure 6f is the azimuthal slice after the Relaxation algorithm is suppressed.

Detailed ways

The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments, examples of the embodiments are shown in the accompanying drawings, and the embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and It should not be construed as a limitation of the present invention.

Fig. 1 is the spatial geometric relationship diagram of the spaceborne multi-channel radar.

The present invention adopts the following technical solutions for realizing the purpose of the invention:

Step 1, data preprocessing. The echoes received by the spaceborne multi-channel radar are subjected to range compression and azimuth FFT respectively, that is, the data is transformed into the range-Doppler domain, and then the data of the same range-Doppler units in the M channels are combined, Get the raw signal and wait for further processing.

Z(r,f _d )=(Z ₁ (r,f _d ),Z ₂ (r,f _d ),…,Z _M (r,f _d )) ^T

Among them, M is an odd number, indicating the number of channels, r is the distance unit, f _d is the Doppler frequency, and T is the transposed matrix.

Step 2, azimuth blur suppression based on Relax algorithm. The designed azimuth blur suppression algorithm uses two nested for loops to complete the traversal for each range-Doppler unit in the range-Doppler domain (one for loop traverses all Doppler cells, and one for loop traverses all Doppler cells. distance unit, the nesting order of the two for loops can be chosen arbitrarily). The specific steps are as follows:

Step 2-1, initialization. In the initialization process, by matching the steering vectors under different fuzzy numbers with the original signals obtained in step 1, the fuzzy signals are preliminarily distinguished from the unambiguous signals, and the preliminary estimates of the signal amplitudes under different fuzzy numbers are obtained. That is, the following operations are completed:

where p is the fuzzy number, f _p is the pulse repetition frequency, and H is the conjugate transpose matrix. Z _rec (r,f _d +pf _p ) represents the preliminary estimated signal amplitude.

表示不同模糊数对应的导向矢量，其中

表示各通道的等效相位中心，v_a表示雷达平台的运动速度。

represents the steering vectors corresponding to different fuzzy numbers, where

represents the equivalent phase center of each channel, and _va represents the moving speed of the radar platform.

Step 2-2, use the Relax algorithm to suppress azimuth blurring. Firstly, according to the original signal obtained in step 1 and the signal amplitudes under different fuzzy numbers preliminarily estimated in step 2-1, the signal components Z _p (r, f _d ) corresponding to different fuzzy numbers can be obtained; The steering vectors under the numbers are matched with the corresponding signal components respectively, that is, the following operations are completed in sequence:

表示估计的信号幅度。Among them, Z _p (r,f _d ) represents the signal corresponding to different blur multiples,

represents the estimated signal amplitude.

的值小于或等于10^-3。然后进入步骤3。Step 2-3, repeat step 2-2 until convergence is achieved. The criterion for convergence is, the formula

is less than or equal to 10 ^-3 . Then go to step 3.

Step 3, azimuth compression. That is, after arranging the signals after azimuth blur suppression in the order of blur multiples, azimuth compression is performed to obtain the imaging result after azimuth blur suppression.

The entire processing flow chart is shown in Figure 2.

A new spaceborne high-resolution wide-format imaging method has been introduced above. The simulated spaceborne multi-channel echo data is used for verification and analysis. The simulated system parameters are as follows: the carrier frequency is 9.45GHz, the pulse repetition frequency is 1187Hz, the platform flight speed is 7480m/s, and the number of channels is 7. In order to simplify the problem, the echo of the scene in the case where the beam pointing is 90° (front side view) is simulated here (assuming that the ground backscatter coefficient obeys a Gaussian distribution). Set a point target, and at the same time, add the noise component of Gaussian distribution to the echo.

Spaceborne high-resolution wide-format imaging experiment based on Relax algorithm:

Before the experiment, it is necessary to analyze the space-time sampling relationship, phase distribution and eigenvalue distribution of the signal-plus-noise covariance matrix under the selected PRF. Figure 3a shows the space-time sampling relationship. It can be seen that the sampling in space and time is perfectly interleaved. The lack of time sampling caused by the too small value of PRF can be compensated by spatial sampling. The phase difference between different channels in Fig. 3b is uniformly distributed over 2π. It can be observed in Fig. 3c that the distribution of the eigenvalues of the signal-plus-noise covariance matrix in the unwrapped frequency domain is continuous. This continuity means that a perfect reconstruction of the signal is possible, at this time with f _p A sampled M-channel radar system can be equivalent to a single-channel radar system sampled with _Mfp . In the unwrapped frequency domain, the smoother the distribution of eigenvalues, the closer the signal obtained after reconstruction is to the signal obtained by a single-channel system at high PRF.

Then proceed to step 1, that is, data preprocessing. The simulated echoes are respectively subjected to range compression and azimuth FFT, that is, the data is transformed into the range-Doppler domain for further processing. Fig. 4a is the original echo signal after adding noise; Fig. 4b is the result after distance compression of each channel; Fig. 4c is the result after transforming the data to the range-Doppler domain, Fig. 4d is the range-Doppler domain It can be clearly seen that the azimuth direction is ambiguous; Figure 4e shows the azimuth direction spectrum of the original signal, and it can be seen that the spectrum is aliased.

Then proceed to step 2, that is, use the Relax algorithm to suppress azimuth blurring. Figure 5a shows the range-Doppler domain result after azimuth blur suppression using the Relax algorithm, and Figure 5b is the zoomed-in result of the range-Doppler domain after azimuth blur suppression. It can be seen that the azimuth blur is significantly suppressed ; Figure 5c is the result of distance migration correction in the distance direction; Figure 5d is the azimuth spectrum after azimuth blur suppression, it can be seen that after processing with the Relax algorithm, the azimuth spectrum is no longer aliased.

Step 3 performs azimuth compression processing. The results obtained after the azimuth blur suppression of the Relax algorithm are compressed in the azimuth direction. Figure 6a shows the imaging results before the azimuth blur suppression. It can be seen that there are many blurred point targets in the azimuth direction; Figure 6b shows the azimuth blur after suppression. There is only one real point target, and all the blurred points are suppressed; Figure 6c and Figure 6d are the 3D display of the imaging results before and after the azimuth blur suppression, which can be seen more intuitively. Effect; Figure 6e and Figure 6f are the azimuth slice images of the imaging results before and after the azimuth blur suppression. It can be seen from the figures that after the azimuth blur suppression using the Relax algorithm, the normalized amplitudes of all azimuth blurs are - 42dB or less.

The above simulation results demonstrate the effectiveness of spaceborne high-resolution wide-format imaging based on the Relax algorithm.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as herein, are not to be taken in an idealized or overly formal sense. explain.

As will be appreciated by those skilled in the art, the present invention may relate to apparatus for performing one or more of the operations described in this application. The apparatus may be specially designed and manufactured for the required purposes, or it may comprise known apparatuses in a general-purpose computer selectively activated or reconfigured with a program stored therein. Such a computer program may be stored in a device (eg, computer) readable medium including, but not limited to, any type of medium suitable for storing electronic instructions and coupled to a bus, respectively Types of disks (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), random access memory (RAM), read only memory (ROM), electrically programmable ROM, electrically erasable ROM (EPROM), electrically erasable Programmable ROM (EEPROM), flash memory, magnetic card or optical card. A readable medium includes any mechanism for storing or transmitting information in a form readable by a device (eg, a computer). For example, readable media include random access memory (RAM), read only memory (ROM), magnetic disk storage media, optical storage media, flash memory devices, signals (such as carrier waves, infrared signal, digital signal), etc.

It will be understood by those skilled in the art that each block in these structural diagrams and/or block diagrams and/or flow diagrams, and blocks in these structural diagrams and/or block diagrams and/or flow diagrams, can be implemented by computer program instructions The combination. These computer program instructions may be provided to a processor of a general purpose computer, specialized computer or other programmable data processing method to create a machine whereby the instructions executed by the processor of the computer or other programmable data processing method create a structure for implementing A method specified in a block or blocks of a diagram and/or block diagram and/or flow diagram.

It can be understood by those skilled in the art that the various operations, methods, steps, measures, and solutions discussed in the present invention may be alternated, modified, combined or deleted. Further, other steps, measures, and solutions in the various operations, methods, and processes that have been discussed in the present invention may also be alternated, modified, rearranged, decomposed, combined, or deleted. Further, steps, measures and solutions in the prior art with various operations, methods, and processes disclosed in the present invention may also be alternated, modified, rearranged, decomposed, combined or deleted.

The above embodiments are only to illustrate the technical idea of the present invention, and cannot limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the present invention. Inside.

Claims (4)

1. A satellite-borne high-resolution wide-range imaging method based on a Relay algorithm is characterized by comprising the following steps:

step 1, performing range-wise compression and azimuth FFT on echo signals received by a satellite-borne multi-channel radar to obtain data of a range-Doppler domain, and then combining data of the same range-Doppler units in a plurality of channels of the satellite-borne multi-channel radar to obtain original signals:

Z(r,f_d)＝(Z₁(r,f_d),Z₂(r,f_d),…,Z_M(r,f_d))^T

wherein M is an odd number and represents the number of channels, r represents a distance unit, f_dDenotes the doppler frequency, T denotes the transpose matrix;

step 2, carrying out azimuth fuzzy suppression on each distance-Doppler unit of the original signal obtained in the step 1 based on a Relay algorithm;

the azimuth ambiguity suppression comprises the following steps:

step 2-1, initializing to obtain the signal amplitude of the initial estimation, namely completing the following operation:

wherein p represents the blur number, f_pDenotes the pulse repetition frequency, H denotes a conjugate transpose matrix, Z_rec(r,f_d+pf_p) Representing the signal amplitude at different ambiguity numbers of the preliminary estimate,

representing steering vectors corresponding to different blur numbers, wherein

d₁…d_MRepresenting the equivalent phase centre, v, of each channel_aRepresenting the speed of motion of the radar platform;

step 2-2, obtaining signal components Z corresponding to different fuzzy numbers according to the original signal obtained in the step 1 and the signal amplitudes under different fuzzy numbers preliminarily estimated in the step 2-1_p(r,f_d) (ii) a Then, the guide vectors under different fuzzy numbers are respectively matched with corresponding signal components to obtain estimated signal amplitude

Namely, the following operations are sequentially completed:

step 2-3, repeating iteration on the step 2-2 until the convergence condition is met;

and 3, carrying out azimuth compression on the result obtained after azimuth fuzzy suppression to obtain an imaging result.

2. The method for satellite-borne high-resolution wide-width imaging based on the Relax algorithm of claim 1, wherein the convergence condition in the steps 2-3 is a formula

Has a value of less than or equal to 10^-3。

3. The method of claim 1, wherein in the step 2, the azimuth-wise blur suppression performed for each range-doppler cell performs traversal using two nested for loops, wherein one for loop traverses all doppler cells and the other for loop traverses all range cells, and the nesting order of the two for loops is arbitrarily selected.

4. The satellite-borne high-resolution wide-amplitude imaging method based on the Relay algorithm according to claim 1, wherein in the step 3, after the signals after the azimuth blur suppression are arranged in the order of blur multiples, azimuth compression is performed.

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