A SAR imaging cognitive motion compensation system

Abstract

The present application relates to a SAR imaging cognitive motion compensation system, which belongs to the field of radar imaging and is used to meet the requirements of missiles for SAR imaging motion compensation under large maneuvering conditions and large dynamic scenes. The system includes: a transmitter, which generates a radar signal and transmits the radar signal through a radar array; a receiver, which receives the radar signal's echo signal through the radar array and transmits the echo signal to a signal processor; a circulator, which is used to isolate the transmitter's signal transmission channel from the receiver's signal reception channel; a signal processor, which is used to obtain sub-aperture data corresponding to the image; based on the sub-aperture data, determining the number of strong points in the image, the strong point location information in the image, and the current entropy value of the image; based on the strong point location information in the image, determining the predicted entropy value of the image; based on the number of strong points, the predicted entropy value, and the current entropy value, determining a modulation frequency for motion compensation; and performing motion compensation using the determined modulation frequency. The present application can ensure the timeliness of motion compensation.

Description

Technical Field

[0001] The present invention relates to the field of radar imaging, and is a SAR imaging cognitive motion compensation system. Background Art

[0002] Synthetic Aperture Radar (SAR) has the characteristics of all-day, all-weather and high-resolution imaging. With the development of SAR imaging technology, missile-borne SAR, which combines SAR with missile scene matching and positioning, has become a research hotspot in recent years.

[0003] The probability of a missile detecting and recognizing a target is closely related to the focusing effect of the SAR image. Since it is difficult for a missile to maintain uniformly accelerated linear motion during flight, its maneuverability in flight can cause envelope and phase distortion in the echo signal, affecting the precise focusing of the radar image. Therefore, it is necessary to extract high-precision missile motion parameters for motion error compensation.

[0004] The missile's flight path will pass through a variety of terrain, but existing compensation methods are usually only applicable to a single terrain or a single flight state (such as constant-speed flight). Therefore, they cannot meet the missile's requirements for SAR imaging motion compensation under large maneuvering conditions and large dynamic scenes. Summary of the Invention

[0005] In view of the above analysis, this application aims to propose a SAR imaging cognitive motion compensation system to achieve motion compensation for missiles based on the imaging scene during the flight process.

[0006] The purpose of this application is mainly achieved through the following technical solutions:

[0007] In one aspect, the present application provides a SAR imaging cognitive motion compensation system, comprising: a circulator, a receiver, a transmitter, a radar array, and a signal processor;

[0008] The transmitter generates a radar signal and transmits the radar signal through the radar array;

[0009] The receiver receives an echo signal of the radar signal through the radar array and sends the echo signal to the signal processor;

[0010] The circulator is used to isolate the signal transmission channel of the transmitter and the signal receiving channel of the receiver;

[0011] The signal processor includes: an acquisition module, a data processing module and a motion compensation module;

[0012] The acquisition module is used to acquire sub-aperture data corresponding to the image;

[0013] The data processing module is used to determine the number of strong points in the image, the position information of the strong points in the image, and the current entropy value of the image based on the sub-aperture data; determine the predicted entropy value of the image based on the strong point position information in the image; and determine the modulation rate for motion compensation based on the number of strong points, the predicted entropy value, and the current entropy value;

[0014] The motion compensation module is configured to perform motion compensation using the determined modulation rate.

[0015] Furthermore, the sub-aperture data is a two-dimensional data matrix, including: range data and azimuth data; the strong points include range strong points and contrast strong points.

[0016] Furthermore, the data processing module is used to perform pulse compression, time domain correction, range walk correction and range curvature correction on the range data in sequence to obtain processed sub-aperture data; superimpose the processed sub-aperture data along the azimuth direction to obtain one-dimensional range data; determine the amplitude mean and amplitude variance of the one-dimensional range data; and determine the number of range strong points and their position information in the image based on the amplitude mean and the amplitude variance.

[0017] Furthermore, the data processing module is used to perform azimuth compression on the azimuth data to obtain compressed azimuth data; determine the amplitude mean and amplitude variance of the compressed azimuth data; determine the contrast intensity of the distance data based on the amplitude mean and amplitude variance; determine the mean and variance of the contrast intensity of the distance data; and determine the number of contrast-strong points and their position information in the image based on the mean and variance of the contrast intensity.

[0018] Furthermore, the data processing module is used to perform distance compression on the azimuth data; determine the probability of occurrence of each pixel in the image based on the compressed azimuth data; and obtain the current entropy value based on the probability of occurrence of each pixel in the image.

[0019] Furthermore, the predicted entropy value includes an MD entropy value and a contrast entropy value.

[0020] Furthermore, the MD entropy value of the image is determined by a contrast strong point, and the data processing module is used to determine first target azimuth data based on the position information of the strong point; perform azimuth compression on the first target azimuth data to obtain first compressed azimuth data; divide the first compressed azimuth data into two parts based on the number of matrix elements of the sub-aperture data in the azimuth direction; determine the translation amount of the two parts of data based on Fourier transform, normalization and cross-correlation; determine the MD modulation frequency based on the translation amount; determine the MD filter coefficient based on the MD modulation frequency; and perform azimuth compression on the first target azimuth data based on the MD filter coefficient;

[0021] The MD entropy value is obtained according to the compressed first target azimuth data.

[0022] Furthermore, the data processing module is configured to determine second target azimuth data based on the position information of the contrast-strong point; perform azimuth compression on the second target azimuth data to obtain second compressed azimuth data; and determine a frequency modulation search sequence based on the MD frequency modulation rate and a preset step size.

[0023] A filter function is constructed using the frequency modulation search sequence as an independent variable set; the second compressed azimuth data is filtered according to the filter function; an amplitude mean and an amplitude variance of the filtered second compressed azimuth data are determined; the contrast corresponding to each frequency modulation in the frequency modulation search sequence is determined according to the amplitude mean and the amplitude variance of the second compressed azimuth data; the frequency modulation corresponding to the maximum contrast is determined as the contrast frequency modulation; a contrast filter coefficient is determined according to the contrast frequency modulation; the second target azimuth data is azimuthally compressed based on the contrast filter coefficient; and a contrast entropy value is obtained according to the re-compressed second target azimuth data.

[0024] Furthermore, the data processing module is configured to determine the sum of the MD frequency modulation rate and the contrast frequency modulation rate as the frequency modulation rate when the number of the contrast strong points is greater than a first threshold and the contrast entropy value is less than the current entropy value.

[0025] Furthermore, the data processing module is used to determine the MD modulation frequency as the modulation frequency when the distance to the strong point is greater than a second threshold, the number of the contrast strong points is less than a first threshold, and the MD entropy value is less than the current entropy value; otherwise, the modulation frequency is determined using inertial navigation data.

[0026] Compared with the existing technology, this application can achieve at least one of the following technical effects:

[0027] 1. Classify the shooting scenes of radar images based on the two dimensions of strong points and image entropy, and select the corresponding modulation rate under the corresponding shooting scene to facilitate subsequent accurate motion compensation.

[0028] 2. Based on the MD algorithm, contrast algorithm and inertial navigation data, motion compensation is achieved solely with the missile's own resources and data, ensuring the timeliness of motion compensation.

[0029] Other features and advantages of the present application will be described in the subsequent description, and some will become apparent from the description or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered as limiting the present application. Like reference symbols denote like components throughout the drawings.

[0031] Figure 1 A schematic diagram of the structure of a SAR imaging cognitive motion compensation system provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of the structure of a signal processor provided in an embodiment of the present application;

[0033] Figure 3 This is a flowchart of the signal processor provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The preferred embodiments of the present application are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present application and are used together with the embodiments of the present application to illustrate the principles of the present application, and are not used to limit the scope of the present application.

[0035] High-maneuverability guided missiles cannot maintain a constant speed during flight, so the missile needs to perform motion compensation on the captured images.

[0036] The flight paths of guided missiles in highly dynamic scenarios often need to be temporarily set or even constantly changed. Therefore, the scene being filmed cannot be predicted in advance, and is therefore dynamic. In contrast, other aircraft, such as airplanes, have stable flight paths, making their scenes predictable. Therefore, missiles face more complex scenes than other aircraft. Complex scenes make it difficult to ensure effective motion compensation. For example, when passing through cities and villages, the difference between images captured between adjacent capture cycles is often significant, facilitating motion compensation. However, in scenes such as deserts, grasslands, and Gobi deserts, the difference between images captured between adjacent capture cycles is often less pronounced, making motion compensation less effective.

[0037] Furthermore, missiles are less easily steered than other aircraft, making them more susceptible to deviation. To compensate for this drawback, missiles require rapid, accurate positioning during flight. For example, aircraft can manually steer their trajectory, allowing them to transmit image data to a server for high-quality image processing. This process takes time, which can be sufficient to cause irreversible deviations in the missile's trajectory, leading to unpredictable outcomes. Therefore, missiles place high demands on the timeliness of motion compensation.

[0038] In summary, when performing motion compensation on missile SAR imaging, it is necessary to consider the shooting scene and the timeliness of the information.

[0039] The key to SAR imaging is the selection of strong points in the scene. Strong points are the objects being photographed. Based on the principles of SAR imaging, strong points usually have at least one of the following characteristics:

[0040] 1. In the shooting scene, the distance between the subject and other objects makes the subject easy to identify, such as individual plants, animals and rocks in the desert or Gobi, or sculptures in the square in the city.

[0041] 2. The shape, appearance, or other attributes of the subject in the scene create a sharp contrast with its surroundings. For example, the color and length of an airport runway contrast sharply with the surrounding environment, or the length of a river contrasts sharply with the objects on both sides.

[0042] Different shooting scenes contain different numbers, locations, and characteristics of strong points, all of which affect the effectiveness of motion compensation. Existing compensation methods are typically only applicable to a single terrain or a single flight state (such as constant speed flight), and therefore are not well suited for missile motion compensation.

[0043] Based on the above theoretical and technical issues, the present application provides a SAR imaging cognitive motion compensation system, such as Figure 1 As shown, it includes: a circulator 101, a receiver 102, a transmitter 103, a radar array 104 and a signal processor 105;

[0044] The transmitter 103 generates a radar signal and transmits the radar signal through the radar array 104;

[0045] The receiver 102 receives the echo signal of the radar signal through the radar array 104 and sends the echo signal to the signal processor 105;

[0046] The circulator 101 is used to isolate the signal transmission channel of the transmitter 103 and the signal receiving channel of the receiver 102;

[0047] like Figure 2 As shown, the signal processor 105 includes: an acquisition module 201, a data processing module 202 and a motion compensation module 203;

[0048] The acquisition module 201 is used to acquire sub-aperture data corresponding to the image;

[0049] The data processing module 202 is configured to determine the number of strong points in the image, the position information of the strong points in the image, and the current entropy value of the image based on the sub-aperture data; determine a predicted entropy value of the image based on the position information of the strong points in the image; and determine a modulation rate for motion compensation based on the number of strong points, the predicted entropy value, and the current entropy value.

[0050] The motion compensation module 203 is configured to perform motion compensation using the determined modulation rate.

[0051] The working process of the signal processor 105 is as follows: Figure 3 As shown, the following steps are included:

[0052] Step 1: Obtain sub-aperture data corresponding to the SAR image;

[0053] In the embodiment of the present application, the sub-aperture data is SAR echo data, and the image is a SAR radar image. The sub-aperture data is a two-dimensional data matrix, including range data and azimuth data.

[0054] Step 2: Determine the number of strong points in the image, the position information of the strong points in the image, and the current entropy value of the image based on the sub-aperture data.

[0055] In the embodiment of the present application, based on the characteristics of the aforementioned strong points, the strong points in the image include: range strong points and contrast strong points;

[0056] Methods for determining the number of strong points and strong point location information in the direction of strong points include:

[0057] The range data are sequentially processed with pulse compression, time domain correction, range walk correction, and range curvature correction. The sub-aperture data are superimposed along the azimuth direction to obtain one-dimensional range data. The amplitude mean and amplitude variance of the one-dimensional range data are determined. Based on the amplitude mean and amplitude variance, the number of range strong points and their position information in the image are determined.

[0058] Specifically, 1) for the sub-aperture data x(t r ,t m ) in the range direction data are sequentially subjected to pulse compression, time domain correction, range movement correction, and range bending correction to obtain the processed sub-aperture data x(t r ,t m ); where tr Representing distance data, t m Representing azimuth data;

[0059] 2) For the processed sub-aperture data x(t r ,t m ), and superimpose along the azimuth direction to obtain the one-dimensional distance data x1(t r ), calculate x1(t r )'s amplitude mean x 1_mean , amplitude variance x 1_std , extract x1(t r ) is greater than x 1_mean +3 x 1_std The point is the distance strong point, and then the number of distance strong points N is obtained. r_q , and the location of the strong point Index_MD.

[0060] The method for determining the number of strong points and the position information of the strong points of contrast includes:

[0061] Azimuth compression is performed on the azimuth data to obtain compressed azimuth data; the amplitude mean and amplitude variance of the compressed azimuth data are determined; the contrast intensity of the range data is determined based on the amplitude mean and amplitude variance; the mean and variance of the contrast intensity of the range data are determined; and the number of contrast-strong points and their position information in the image are determined based on the mean and variance of the contrast intensity.

[0062] Specifically, construct the azimuth dechirp processing factor, in

[0063]

[0064] R is the distance unit coordinate, v is the carrier and speed The spatial squint angle θ is the Doppler center frequency f estimated by dc The obtained value, f dc =2vsinθ0 / λ, λ is the signal wavelength;

[0065] The dechirp processing factor is multiplied with the azimuth data to achieve azimuth compression of the azimuth data and obtain the compressed azimuth data x2(t m );

[0066] Calculate x2(t m )'s amplitude mean x 2_mean , amplitude variance x 2_std , and obtain the contrast of the compressed azimuth data

[0067] Calculate Q db (t r )db_mean , variance Q db_std , extract Q db (t r ) is greater than Q db_mean +3·Q db_std The points with strong contrast are the points with strong contrast, and then the number of points with strong contrast is obtained. And the location of the strong point Index_DB.

[0068] The process of determining the current entropy value includes:

[0069] The azimuth data is compressed in the distance direction; the probability of each pixel appearing in the image is determined based on the compressed azimuth data; and the current entropy value is obtained based on the probability of each pixel appearing in the image.

[0070] Specifically, the azimuth data is taken and dechirped along the range direction to achieve range compression;

[0071] For an M×N radar image, its pixels are {x m,n}, where m is the azimuth number and n is the distance number; let the probability of pixel occurrence be in The current entropy value is:

[0072]

[0073] Step 3: Based on the strong point position information in the image, determine the predicted entropy value of the image.

[0074] In the embodiment of the present application, the predicted entropy value includes: MC entropy value and contrast entropy value; MC is the abbreviation of sub-aperture correlation method.

[0075] Strong points in the image include: range-oriented strong points;

[0076] The method for determining an MD entropy value includes: determining first target azimuth data based on position information of a strong point; performing azimuth compression on the first target azimuth data to obtain first compressed azimuth data; dividing the first compressed azimuth data into two parts based on the number of matrix elements of sub-aperture data in the azimuth direction; determining a translation amount of the two parts of data based on Fourier transform, normalization, and cross-correlation; determining an MD frequency modulation rate based on the translation amount; determining an MD filter coefficient based on the MD frequency modulation rate; and performing azimuth compression on the target azimuth data based on the MD filter coefficient to obtain an MD entropy value.

[0077] Specifically, 1) according to the position information Index_MD and Index_DB of the strong point, determine the first target direction data x(t m ) is multiplied by the azimuth matching filter coefficient to achieve azimuth compression and obtain the first compressed azimuth data y(tm ):

[0078]

[0079] 2) Divide the first compressed azimuth data into two partial sub-apertures, the data is y1(t m )=y(t m1 ) and y2(t m )=y(t m2 ), the value range of m1 is (1, Na / 2), and the value range of m2 is (Na / 2+1, Na), where Na is the number of elements corresponding to the azimuth direction.

[0080] 3) For y1(t m ) and y2(t m ) are Fourier transformed to the azimuth frequency domain y1(f m ) and y2(f m );

[0081] 4) For y1(f m ) and y2(f m ) Perform normalization processing and modulus value;

[0082] 5) Use the correlation function to find y1(f m ) and y2(f m ) translation,

[0083] z=(FFT(y2(f m )·conj(FFT(y1(f m ))), according to z, determine the amplitude peak position, which is the translation Δn a ;

[0084] 6) Using Δn a The estimated MD modulation frequency is calculated. The corresponding MD filter coefficient is Where PRF is the repetition frequency of SAR.

[0085] 7) First target direction data x(t m ) and MD filter coefficient Multiply them together and perform dechirp processing, that is, perform azimuthal compression;

[0086] 8) Calculate the MD entropy value S_md based on the compressed first target azimuth data.

[0087] The method for determining the contrast entropy value includes:

[0088] According to the position information of the contrast strong point, the second target azimuth data is determined; the second target azimuth data is azimuthally compressed to obtain second compressed azimuth data; according to the MD modulation frequency and the preset step size, a modulation frequency search sequence is determined; a filter function is constructed with the modulation frequency search sequence as an independent variable set; the second compressed azimuth data is filtered according to the filter function; the amplitude mean and amplitude variance of the filtered second compressed azimuth data are determined; according to the amplitude mean and amplitude variance of the filtered second compressed azimuth data, the contrast corresponding to each modulation frequency in the modulation frequency search sequence is determined; the modulation frequency corresponding to the maximum contrast is determined as the contrast modulation frequency; according to the contrast modulation frequency, a contrast filter coefficient is determined; based on the contrast filter coefficient, the second target azimuth data is azimuthally compressed; and a contrast entropy value is obtained according to the compressed azimuth data.

[0089] Specifically,

[0090] 1) Based on the contrast strong point position Index_DB, determine the second target azimuth data. The second target azimuth data z(t m ) is multiplied by the MD filter coefficient to achieve azimuth compression and obtain the second compressed azimuth data s(t m ):

[0091]

[0092] 2) In a certain step Obtain frequency modulation search sequence ΔK = [-2Δk:2Δk];

[0093] 3) Construct a filter function based on ΔK as the independent variable set use With s(t m ) performs matched filtering to determine the amplitude mean I of the second compressed azimuth data after filtering mean , amplitude variance I std , get the contrast of the second compressed azimuth data after filtering Among them, Q db_jj There is a one-to-one correspondence with each modulation frequency of ΔK.

[0094] 4) Determine the modulation frequency corresponding to the maximum contrast as the contrast modulation frequency ΔK DB ,max(Q db_jj ) corresponding to ΔK i =ΔK DB , then the contrast filter coefficient is

[0095] 5) Second target direction data x(t m) is multiplied by the contrast filter coefficient to perform dechirp processing, i.e. azimuth compression, and the contrast entropy value S_db is calculated based on the compressed azimuth data.

[0096] Step 4: Determine the modulation rate for motion compensation based on the number of strong points, the predicted entropy value, and the current entropy value.

[0097] In an embodiment of the present application, a method for determining a modulation rate for motion compensation includes:

[0098] When the number of contrast-strong points is greater than the first threshold and the contrast entropy is less than the current entropy, the modulation rate is the sum of the MD modulation rate and the contrast modulation rate;

[0099] When the distance to the strong point is greater than the second threshold, the number of contrast strong points is less than the first threshold, and the MD entropy value is less than the current entropy value, the modulation frequency is the MD modulation frequency;

[0100] In other cases, the inertial navigation data is used to determine the modulation rate.

[0101] The first threshold is the frequency modulation threshold estimated by the MD method, and the second threshold is the frequency modulation threshold estimated by the contrast method.

[0102] Use inertial navigation data to determine the modulation rate, specifically:

[0103]

[0104] ΔK a is the frequency modulation, a r_i is the inertial navigation acceleration during the echo collection process corresponding to each sub-aperture data, and λ is the signal wavelength.

[0105] Step 5: Perform motion compensation using the determined modulation rate.

[0106] In the embodiment of the present application, after the modulation rate is determined, motion compensation can be implemented using technical means available to those skilled in the art.

[0107] In the embodiment of the present application, the influence of the current shooting scene on motion compensation is evaluated based on the strong points and image entropy, and the influence is divided into three cases according to the evaluation results.

[0108] In the first case, there are enough points with strong contrast. During motion compensation, the points with strong contrast can also be used as distance strong points. In this case, the sum of the MD modulation frequency and the contrast modulation frequency is taken as the modulation frequency.

[0109] In the second case, the number of strong contrast points is insufficient, but the number of strong distance points is sufficient. Due to the insufficient number of strong contrast points, the obtained contrast modulation frequency may have a large error, so only the MD modulation frequency is used as the modulation frequency.

[0110] In the third case, the number of strong contrast points and the number of strong range points in other scenarios are insufficient, and there are huge errors in the MD modulation frequency and the contrast modulation frequency. In this case, the inertial navigation data is used to determine the modulation frequency.

[0111] Through the above method, the missile can distinguish the shooting scenes without relying on external forces (such as servers) and select the corresponding frequency modulation for motion compensation, taking into account the timeliness of the data and performing motion compensation more accurately.

[0112] The above is only a preferred specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in this application should be covered by the scope of protection of the present application.

Claims

1. A SAR imaging cognitive motion compensation system, characterized in that: include: circulators, receivers, transmitters, radar arrays, and signal processors; The transmitter generates a radar signal and transmits the radar signal through the radar array; The receiver receives an echo signal of the radar signal through the radar array and sends the echo signal to the signal processor; The circulator is used to isolate the signal transmission channel of the transmitter and the signal receiving channel of the receiver; The signal processor includes: an acquisition module, a data processing module and a motion compensation module; The acquisition module is used to acquire sub-aperture data corresponding to the image; the sub-aperture data is a two-dimensional data matrix, including: range data and azimuth data; The data processing module is used to determine the number of strong points in the image, the position information of the strong points in the image and the current entropy value of the image based on the sub-aperture data; determine the predicted entropy value of the image based on the strong point position information in the image; determine the modulation frequency for motion compensation based on the number of strong points, the predicted entropy value and the current entropy value; the strong points include range-oriented strong points and contrast-oriented strong points; the range-oriented data is sequentially subjected to pulse compression, time domain correction, range walk correction and range warp correction processing to obtain the processed sub-aperture data x(t r ,t m ); where t r Representing distance data, t m Characterize the azimuth data; along the azimuth direction, the processed sub-aperture data x(t r ,t m ), and superimpose along the azimuth direction to obtain the one-dimensional distance data x1(t r ), calculate x1(t r )'s amplitude mean x 1_mean , amplitude variance x 1_std , extract x1(t r ) is greater than x 1_mean +3 x 1_std The points are range strong points, and the number of the range strong points and the position information in the image are determined; the azimuth data are azimuthally compressed to obtain compressed azimuth data; according to the amplitude mean value x of the compressed azimuth data, the azimuth data is compressed to obtain the compressed azimuth data; 2_mean , amplitude variance x 2_std , and obtain the contrast of the compressed azimuth data Calculate Q db (t r ) db_mean , variance Q db_std , extract Q db (t r ) is greater than Q db_mean +3·Q db_std points with high contrast as points with high contrast, and determining the number of points with high contrast and their position information in the image; The motion compensation module is configured to perform motion compensation using the determined modulation rate.

2. The system according to claim 1, wherein: The data processing module is used to perform distance compression on the azimuth data; determine the probability of occurrence of each pixel in the image based on the compressed azimuth data; and obtain the current entropy value based on the probability of occurrence of each pixel in the image.

3. The system according to claim 1, wherein: The predicted entropy value includes an MD entropy value and a contrast entropy value.

4. The system according to claim 3, characterized in that The MD entropy value of the image is determined based on the contrast strength point, including: The data processing module is used to determine first target azimuth data based on the position information of the strong point; perform azimuth compression on the first target azimuth data to obtain first compressed azimuth data; divide the first compressed azimuth data into two parts based on the number of matrix elements of the sub-aperture data in azimuth; determine the translation amount of the two parts of data based on Fourier transform, normalization and cross-correlation; determine the MD modulation frequency based on the translation amount; determine the MD filter coefficient based on the MD modulation frequency; and perform azimuth compression on the first target azimuth data based on the MD filter coefficient; The MD entropy value is obtained according to the re-compressed first target azimuth data.

5. The system according to claim 3, wherein: The data processing module is configured to determine second target azimuth data based on the position information of the contrast-strong point; perform azimuth compression on the second target azimuth data to obtain second compressed azimuth data; and determine a frequency modulation search sequence based on the MD frequency modulation rate and a preset step size. A filter function is constructed using the frequency modulation search sequence as an independent variable set; the second compressed azimuth data is filtered according to the filter function; an amplitude mean and an amplitude variance of the filtered second compressed azimuth data are determined; the contrast corresponding to each frequency modulation in the frequency modulation search sequence is determined according to the amplitude mean and the amplitude variance of the second compressed azimuth data; the frequency modulation corresponding to the maximum contrast is determined as the contrast frequency modulation; a contrast filter coefficient is determined according to the contrast frequency modulation; the second target azimuth data is azimuthally compressed based on the contrast filter coefficient; and a contrast entropy value is obtained according to the re-compressed second target azimuth data.

6. The system according to claim 4, characterized in that The data processing module is configured to determine the sum of the MD modulation frequency and the contrast modulation frequency as the modulation frequency when the number of the contrast strong points is greater than a first threshold and the contrast entropy value is less than the current entropy value.

7. The system according to claim 4, characterized in that: The data processing module is used to determine the MD modulation frequency as the modulation frequency when the distance to the strong point is greater than the second threshold, the number of the contrast strong points is less than the first threshold, and the MD entropy value is less than the current entropy value; otherwise, use inertial navigation data to determine the modulation frequency.

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