CN108845318B - Satellite-borne high-resolution wide-range imaging method based on Relax algorithm - Google Patents
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Abstract
The invention discloses a satellite-borne high-resolution wide-range HRWS imaging method based on a Relay algorithm, and belongs to the technical field of radar target detection. According to the characteristics of the satellite-borne multi-channel radar echo, after distance compression and azimuth FFT are carried out and data are converted into a distance-Doppler domain, an iteration method capable of realizing azimuth fuzzy suppression is designed by combining a Relay algorithm. And finally, carrying out azimuth compression on the signals subjected to azimuth blur suppression to obtain an imaging result without blur. The simulation result verifies the feasibility and the effectiveness of the method.
Description
Technical Field
The invention belongs to the technical field of radar target detection, relates to a signal processing algorithm of a multi-channel radar, and particularly relates to a satellite-borne high-resolution wide-range imaging method based on a Relay algorithm.
Background
Resolution and mapping bandwidth are two important imaging indicators of a satellite-borne Synthetic Aperture Radar (SAR). On one hand, the high resolution can reflect the target characteristic information more accurately, and is convenient for target identification and characteristic extraction, which has important significance in the aspects of military reconnaissance, urban drawing, disaster assessment and the like. On the other hand, the wide swath can provide wider scene information to obtain the global interpretation capability, which is beneficial to the observation of large-area areas such as land, forest, ocean and the like. However, for the conventional space-borne SAR system, due to the limitation of the minimum antenna area, the high resolution and the wide swath are a pair of irreconcilable contradictory quantities, and the two performance indexes cannot be improved at the same time.
Under the condition that the satellite-borne SAR of the traditional single-transmitting single-receiving system cannot realize high resolution and wide swath at the same time, researchers begin to search for a new method. It has been found that the use of multiple channel reception modes of operation allows the problem to be solved effectively. In imaging a wide area, it is desirable to use a low Pulse Repetition Frequency (PRF) to avoid range-wise ambiguity, which would occur in the azimuth direction if the PRF used was too low for the azimuth antenna. Therefore, the key of the satellite-borne high-resolution wide-width imaging method is the design of a fuzzy suppression method.
Disclosure of Invention
The technical problem to be solved by the invention is as follows:
the azimuth ambiguity suppression method with the excellent azimuth ambiguity suppression effect is provided for the defects of the ambiguity suppression method in the high-resolution wide-width imaging of the satellite-borne SAR.
The invention adopts the following technical scheme for solving the technical problems:
a satellite-borne high-resolution wide-range imaging method based on a Relay algorithm comprises the following steps:
Z(r,fd)＝(Z1(r,fd),Z2(r,fd),…,ZM(r,fd))T
wherein M is an odd number and represents the number of channels, r represents a distance unit, fdDenotes the doppler frequency, T denotes the transpose matrix;
and 3, carrying out azimuth compression on the result obtained after azimuth fuzzy suppression to obtain an imaging result.
Preferably, the azimuth ambiguity suppression in step 2 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, fpDenotes the pulse repetition frequency, H denotes a conjugate transpose matrix, Zrec(r,fd+pfp) Representing the signal amplitude at different ambiguity numbers of the preliminary estimate,
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 2-1p(r,fd) (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:
and 2-3, repeatedly iterating the step 2-2 until the convergence condition is met.
Preferably, the convergence condition in the step 2-3 is a formula
Preferably, in the step 2, the azimuth ambiguity suppression performed for each range-doppler cell completes traversal using two nested for loops, where 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.
Preferably, in the step 3, the azimuth compression is performed after arranging the azimuth blur-suppressed signals in the order of blur multiples.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
compared with the traditional fuzzy suppression algorithm, the algorithm does not need to construct a reconstruction filtering vector, can directly utilize the known guide vector and suppress the fuzzy by an iteration method, is simpler and easier to implement, and can be effectively applied to an actual system.
Drawings
FIG. 1 is a space geometry diagram of a satellite-borne multi-channel radar.
Fig. 2 is a flow chart of azimuth-based blur suppression based on the Relax algorithm.
Fig. 3a to 3c are the eigenvalue distribution results of the space-time sampling relationship, the phase distribution and the signal-plus-noise covariance matrix, respectively.
Fig. 4a to 4e are respectively the original echo signal of each channel, the range compression result, the range-doppler domain amplification result, and the azimuth spectrum of the original echo obtained by simulation.
Fig. 5a to 5d are a range-doppler domain result, a range-doppler domain amplification result, a range migration correction result, and an azimuth spectrum after azimuth blur suppression based on the Relax algorithm, respectively.
Fig. 6a to 6f are imaging results after azimuth compression, where fig. 6a is the imaging result before azimuth blur suppression, fig. 6b is the imaging result after the azimuth blur suppression by the Relax algorithm, fig. 6c is a 3D display of the imaging result before azimuth blur suppression, fig. 6D is a 3D display of the imaging result after the azimuth blur suppression by the Relax algorithm, fig. 6e is an azimuth slice before azimuth blur suppression, and fig. 6f is an azimuth slice after the azimuth blur suppression by the Relax algorithm.
Detailed Description
The invention will be described in further detail below with reference to the drawings and specific embodiments, examples of which are illustrated in the drawings, and the embodiments described below with reference to the drawings are exemplary only and are not to be construed as limiting the invention.
FIG. 1 is a space geometry diagram of a satellite-borne multi-channel radar.
The invention adopts the following technical scheme for realizing the purpose of the invention:
Z(r,fd)＝(Z1(r,fd),Z2(r,fd),…,ZM(r,fd))T
Wherein M is an odd number and represents the number of channels, r represents a distance unit, fdDenotes the doppler frequency and T denotes the transpose matrix.
And 2, carrying out azimuth fuzzy suppression based on a Relay algorithm. The designed azimuth ambiguity suppression algorithm adopts two nested for loops to complete traversal aiming at each range-Doppler unit of the range-Doppler domain (one for loop traverses all Doppler units, one for loop traverses all range units, and the nesting order of the two for loops can be arbitrarily selected). The specific steps are as follows:
and 2-1, initializing. In the initialization process, the guide vectors under different fuzzy numbers are respectively matched with the original signals obtained in the step 1, and then the fuzzy signals and the non-fuzzy signals are preliminarily distinguished, so that the preliminary estimation of the signal amplitude under different fuzzy numbers is obtained. The following operations are completed:
wherein p represents the blur number, fpDenotes the pulse repetition frequency and H denotes the conjugate transpose matrix. Zrec(r,fd+pfp) Representing the preliminarily estimated signal amplitude.
And 2-2, carrying out azimuth fuzzy suppression by using a Relay algorithm. Firstly, according to the original signal obtained in step 1 and the signal amplitudes under different fuzzy numbers preliminarily estimated in step 2-1, signal components Z corresponding to different fuzzy numbers can be obtainedp(r,fd) (ii) a Then, the guide vectors under different fuzzy numbers are respectively matched with corresponding signal components, namely the following operations are sequentially completed:
wherein Z isp(r,fd) Representing the signals corresponding to different blur multiples,
representing the estimated signal amplitude.
And 2-3, repeatedly iterating the step 2-2 until convergence is achieved. Wherein the convergence criterion is the formula
Has a value of less than or equal to 10-3. Then step 3 is entered.
And 3, compressing in the azimuth direction. After the signals after the azimuth blur suppression are arranged according to the sequence of the blur multiples, azimuth compression is carried out, and the imaging result after the azimuth blur suppression is obtained.
The whole processing flow chart is shown in fig. 2.
A novel satellite-borne high-resolution wide-range imaging method is introduced, and simulated satellite-borne multi-channel echo data are 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 flying speed of the platform is 7480m/s, and the number of channels is 7. To simplify the problem, the echoes of the scene with a beam pointing 90 ° (front side view) are simulated here (assuming that the ground backscattering coefficients follow a gaussian distribution). 1 point target is set, and simultaneously, a noise component of gaussian distribution is added to the echo.
Satellite-borne high-resolution wide-range imaging experiment based on a Relay algorithm:
before performing the experiment, the spatial-temporal sampling relationship, the phase distribution and the eigenvalue distribution of the signal-plus-noise covariance matrix under the selected PRF need to be analyzed. Fig. 3a is a space-time sampling relationship, and it can be seen that the samples in space and time are perfectly interleaved, and the lack of time sampling caused by too small a PRF value can be compensated by the spatial sampling. The phase difference between the different channels in fig. 3b is evenly distributed over 2 pi. It can be observed in fig. 3c that the distribution of eigenvalues of the signal plus noise covariance matrix over the frequency domain without convolution is continuous, which means that a perfect reconstruction of the signal is possible, now at fpA radar system with M channels to sample may be equivalent to MfpA single channel radar system that performs sampling. In the frequency domain without convolution, the smoother the distribution of the eigenvalues, the closer the signal obtained after reconstruction is to that obtained for a single channel system at high PRF.
Then, step 1, i.e., data preprocessing, is performed. And respectively performing range-direction compression and azimuth-direction FFT on the simulated echo, namely converting the data into a range-Doppler domain for further processing. FIG. 4a is a diagram of the original echo signal after noise addition; FIG. 4b is the result after distance compression for each channel; FIG. 4c is the result after transforming the data into the range-Doppler domain, and FIG. 4d is an enlarged result of the range-Doppler domain, it being clearly seen that the azimuth direction is blurred; fig. 4e is the azimuth spectrum of the original signal, and it can be seen that the spectrum is aliased.
And step 2, adopting a Relay algorithm to carry out azimuth fuzzy suppression. Fig. 5a is the range-doppler domain result after the azimuth blur suppression by the Relax algorithm, and fig. 5b is the enlarged result of the range-doppler domain after the azimuth blur suppression, which shows that the azimuth blur is obviously suppressed; fig. 5c shows the result after range migration correction in the range-up direction; fig. 5d shows the azimuth spectrum after azimuth blur suppression, and it can be seen that the azimuth spectrum is not aliased after the processing by the Relax algorithm.
And step 3, performing azimuth compression processing. Azimuth compression is carried out on a result obtained after azimuth fuzzy suppression of the Relax algorithm, and fig. 6a is an imaging result before azimuth fuzzy suppression, so that a plurality of fuzzy point targets are seen in the azimuth direction; FIG. 6b shows the imaging result after the azimuth-direction blur suppression, where only one real point target exists and the blur points are all suppressed; fig. 6c and 6D are respectively 3D displays of imaging results before and after azimuth blur suppression, which can more intuitively see the effect of the Relax algorithm on azimuth blur suppression; fig. 6e and 6f are azimuth slice images of imaging results before and after azimuth blur suppression, and it can be seen from the images that the normalized amplitude of all azimuth blurs is below-42 dB after azimuth blur suppression by the Relax algorithm.
The simulation result proves the effectiveness of satellite-borne high-resolution wide-width imaging based on the Relay 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 will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
One skilled in the art will appreciate that the present invention may be directed to an apparatus for performing one or more of the operations described in the present application. The apparatus may be specially designed and constructed for the required purposes, or it may comprise any known apparatus in a general purpose computer selectively activated or reconfigured by a program stored in the general purpose computer. Such a computer program may be stored in a device (e.g., computer) readable medium, including, but not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magnetic-optical disks, Random Access Memories (RAMs), Read Only Memories (ROMs), electrically programmable ROMs, electrically erasable ROMs (eproms), electrically erasable programmable ROMs (eeproms), flash memories, magnetic cards, or optical cards, or in any type of media suitable for storing electronic instructions, and each coupled to a bus. A readable medium includes any mechanism for storing or transmitting information in a form readable by a device (e.g., a computer). For example, a readable medium includes Random Access Memory (RAM), Read Only Memory (ROM), magnetic disk storage media, optical storage media, flash memory devices, signals propagating in electrical, optical, acoustical or other forms (e.g., carrier waves, infrared signals, digital signals), etc.
It will be understood by those within the art that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the methods specified in the block or blocks of the block diagrams and/or flowchart block or blocks.
Those of skill in the art will appreciate that the various operations, methods, steps in the processes, acts, or solutions discussed in the present application can be interchanged, modified, combined, or eliminated. Further, various operations, methods, steps in the flows, which have been discussed in the present application, may be interchanged, modified, rearranged, decomposed, combined, or eliminated. Further, steps, measures, schemes in the various operations, methods, procedures disclosed in the prior art and the present invention can also be alternated, changed, rearranged, decomposed, combined, or deleted.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
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,fd)＝(Z1(r,fd),Z2(r,fd),…,ZM(r,fd))T
wherein M is an odd number and represents the number of channels, r represents a distance unit, fdDenotes 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, fpDenotes the pulse repetition frequency, H denotes a conjugate transpose matrix, Zrec(r,fd+pfp) Representing the signal amplitude at different ambiguity numbers of the preliminary estimate,
representing steering vectors corresponding to different blur numbers, wherein
d1…dMRepresenting the equivalent phase centre, v, of each channelaRepresenting 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 2-1p(r,fd) (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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