High-speed platform carries small array radar forward-looking super-resolution imaging method and device

Abstract

The application discloses a high-speed platform carries small array radar forward-looking super-resolution imaging method and device, belongs to digital signal processing technical field, including: obtaining the echo signal produced by strong scattering point in radar forward-looking super-resolution imaging scene and carrying out the de-loading frequency processing, the obtained echo signal after de-loading frequency is successively pulse compression, distance walk correction and Doppler frequency offset correction processing, obtains the echo signal after correction;According to the echo signal after correction, the forward-looking super-resolution imaging model is constructed;The imaging algorithm based on sparse Bayesian learning is used for sparse reconstruction of the forward-looking super-resolution imaging model, and the super-resolution image is obtained;According to the precalculated space-variable phase error, the super-resolution image is corrected, and the corrected super-resolution image is obtained.The application can effectively correct the image distortion caused by azimuth space-variable error, improve the focusing effect and precision of the corrected super-resolution image, and be suitable for practical application scene.

Description

Technical Field

[0001] This invention belongs to the field of digital signal processing technology, specifically relating to a forward-looking super-resolution imaging method and device for a high-speed platform-borne small array radar. Background Technology

[0002] In recent years, the development of radar technology has made radar imaging increasingly important. In practical applications, hypersonic vehicles have become a research hotspot. These platforms often fly at speeds several times or even tens of times the speed of sound. Achieving high-resolution forward-looking radar imaging on high-speed moving platforms is of great significance. Due to the special nature of these platforms, their imaging systems have high requirements for system structure, real-time imaging performance, and imaging resolution.

[0003] Forward-looking imaging scenarios generally refer to the range of -10° to 10° in front of the radar, where the Doppler frequency gradient of the echo signal is relatively small. Traditional Synthetic Aperture Radar (SAR) imaging techniques are limited to frontal and side-looking scenarios. Even the improved range-Doppler (RD) algorithm with time-domain corrected range-walk and the improved Chrip Scaling (CS) algorithm can only achieve an oblique angle of about 70°, which cannot be applied to forward-looking imaging scenarios. To achieve high-resolution forward-looking imaging, researchers have proposed scanning beam imaging technology based on real aperture. This technology processes the azimuth echo obtained after the radar scans the scene to obtain the forward-looking scene image. However, in real-world scenarios, high-speed platforms are mostly small in size, and the aperture of their onboard radars is much smaller than that of airborne and shipborne radars. Therefore, their azimuth resolution is poor and cannot meet the requirements of high-resolution forward-looking radar imaging. In recent years, super-resolution technology has received widespread attention due to its ability to overcome resolution limitations and its simple implementation.

[0004] However, existing methods that use super-resolution technology have problems such as the platform's motion speed being much less than the speed of sound, resulting in errors in the imaging results. Therefore, they are not suitable for situations where the platform's motion speed is several times the speed of sound. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a method and apparatus for forward-looking super-resolution imaging of a small array radar on a high-speed platform.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a forward-looking super-resolution imaging method for a high-speed platform-borne small array radar, the method comprising:

[0008] The echo signal generated by a strong scattering point in the radar forward-looking super-resolution imaging scene is acquired, and the echo signal is processed to remove the carrier frequency to obtain the echo signal after removing the carrier frequency.

[0009] The echo signal after carrier frequency removal is sequentially processed by pulse compression, distance travel correction and Doppler frequency offset correction to obtain the corrected echo signal;

[0010] A forward-looking super-resolution imaging model is constructed based on the corrected echo signal;

[0011] The super-resolution image is obtained by sparsely reconstructing the forward-looking super-resolution imaging model using an imaging algorithm based on sparse Bayesian learning.

[0012] The super-resolution image is corrected based on the pre-calculated spatially varying phase error to obtain the corrected super-resolution image.

[0013] Optionally, the process of sequentially performing pulse compression, range travel correction, and Doppler frequency offset correction on the carrier-de-frequency echo signal to obtain the corrected echo signal includes:

[0014] The echo signal after carrier frequency removal is pulse-compressed to obtain the pulse-compressed signal.

[0015] The pulse-compressed signal is subjected to FFT along the fast time dimension to obtain the transformed signal;

[0016] The transformed signal is multiplied by a pre-constructed linear frequency phase factor and then subjected to IFFT to obtain the distance-corrected signal.

[0017] The Doppler frequency offset corrected signal is obtained based on the distance-corrected signal and the pre-constructed Doppler frequency offset correction factor;

[0018] The Doppler frequency offset corrected signal is corrected based on the pre-calculated spatial phase error to obtain the corrected echo signal.

[0019] Optionally, the pre-constructed linear frequency phase factor is represented as follows:

[0020] ;

[0021] in, Represents an exponential function. Indicates the imaginary part. Represents the speed of light. This represents the range coordinate of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the speed of the radar platform. Represents the distance coordinate. Indicates slow time. Indicates the range frequency;

[0022] The pre-constructed Doppler frequency offset correction factor represents:

[0023] ;

[0024] in, Indicates the signal wavelength.

[0025] Optionally, the spatially varying phase error is expressed as follows:

[0026] ;

[0027] in, This indicates the spatially variable phase error. This represents the azimuth coordinates of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the center slope distance.

[0028] Optionally, constructing a forward-looking super-resolution imaging model based on the corrected echo signal includes:

[0029] Extract any range cell data from the corrected echo signal;

[0030] Construct a dictionary matrix based on a predefined discrete Doppler sequence;

[0031] The forward-looking super-resolution imaging model is constructed based on the dictionary matrix.

[0032] Optionally, the step of correcting the super-resolution image based on the pre-calculated spatially varying phase error to obtain the corrected super-resolution image includes:

[0033] The preset image is divided into grids using the azimuth resolution and range resolution of the super-resolution image to obtain the divided preset image; wherein, the super-resolution image and the preset image have the same size;

[0034] Based on the spatially variable phase error, the coordinates of each pixel in the super-resolution image at the corresponding pixel in the segmented preset image are calculated to obtain the corrected coordinates.

[0035] Based on the corrected coordinates, the segmented preset image is geometrically corrected using sinc kernel interpolation to obtain the corrected super-resolution image.

[0036] Optionally, the corrected coordinates are represented as follows:

[0037] ;

[0038] in, This represents the coordinates of a pixel in the super-resolution image. This indicates the relationship between the predefined image after segmentation and the super-resolution image. The coordinates of the pixel corresponding to the pixel at that location. This indicates the spatially variable phase error. Indicates the signal wavelength. Indicates the altitude of the radar platform. Indicates the speed of the radar platform. It represents the equivalent velocity of the array element's azimuth direction.

[0039] Secondly, the present invention provides a forward-looking super-resolution imaging device for a high-speed platform-borne miniature array radar, the device comprising:

[0040] The signal acquisition module is used to acquire the echo signal generated by strong scattering points in the radar forward-looking super-resolution imaging scene, and to perform carrier frequency removal processing on the echo signal to obtain the carrier frequency removed echo signal.

[0041] The signal correction module is used to sequentially perform pulse compression and distance travel correction processing on the echo signal after carrier frequency removal to obtain the corrected echo signal;

[0042] The model building module is used to construct a forward-looking super-resolution imaging model based on the corrected echo signal.

[0043] The image acquisition module is used to sparsely reconstruct the forward-looking super-resolution imaging model using an imaging algorithm based on sparse Bayesian learning to obtain a super-resolution image.

[0044] The image correction module is used to correct the super-resolution image based on the pre-calculated spatially varying phase error to obtain the corrected super-resolution image.

[0045] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0046] In the above technical solutions, the present invention considers the influence of azimuth spatial variation error on the signal after distance travel correction, resulting in higher accuracy; the present invention utilizes an imaging algorithm based on sparse Bayesian learning to sparsely reconstruct the forward-looking super-resolution imaging model. This algorithm automatically estimates hyperparameters during the iteration process, making it more suitable for practical application scenarios; and the present invention performs geometric correction on the super-resolution image based on spatial variation phase error, which can effectively correct image distortion caused by azimuth spatial variation error, resulting in a better focusing effect for the corrected super-resolution image.

[0047] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] Figure 1 This is a flowchart of a forward-looking super-resolution imaging method for a high-speed platform-borne small array radar provided in an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of signal propagation of a high-speed motion platform array in radar single-transmit and single-receive mode, provided by an embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram of a scenario provided by an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of a real beam imaging result provided in an embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of the results from a traditional super-resolution imaging algorithm;

[0053] Figure 6 This is a schematic diagram of an imaging result provided by an embodiment of the present invention;

[0054] Figure 7 This is a schematic diagram of a strong scattering point provided in an embodiment of the present invention;

[0055] Figure 8 This is a schematic diagram of another real-beam imaging result provided by an embodiment of the present invention;

[0056] Figure 9 This is a schematic diagram of the results of a traditional super-resolution imaging algorithm in another scene;

[0057] Figure 10 This is a schematic diagram of another imaging result provided by an embodiment of the present invention;

[0058] Figure 11 This is a block diagram of a high-speed platform-borne small array radar forward-looking super-resolution imaging device provided in an embodiment of the present invention. Detailed Implementation

[0059] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0060] Figure 1 This is a flowchart of a forward-looking super-resolution imaging method for a high-speed platform-borne small array radar provided in an embodiment of the present invention, such as... Figure 1 As shown, the method may include the following steps:

[0061] S101. Acquire the echo signal generated by strong scattering points in the radar forward-looking super-resolution imaging scene, and perform carrier frequency removal processing on the echo signal to obtain the carrier frequency removed echo signal.

[0062] In one implementation, Figure 2 This is a schematic diagram of signal propagation of a high-speed motion platform array in radar single-transmit and single-receive mode according to an embodiment of the present invention. With the radar position as the origin, a Cartesian coordinate system plane for forward-looking super-resolution imaging can be established, and a strong scattering point P in the imaging scene is selected. ,in, This represents the range coordinate of the strong scattering point P in the Cartesian coordinate plane. Let P represent the azimuth coordinates of the strong scattering point P in the Cartesian coordinate plane. The LFM (Linear Frequency Modulation) signals emitted sequentially by the array elements during radar operation can be represented as follows:

[0063] ;

[0064] in, Indicates LFM signal, Indicates a fast time. Represents the distance-to-window function. For carrier frequency, To adjust the slope, The pulse width. Represents a rectangular window function. Represents an exponential function. Indicates the imaginary part.

[0065] Based on the LFM signals sequentially emitted by the array elements during radar operation, the echo signal generated by the strong scattering point P, after carrier frequency removal processing, can be represented as follows:

[0066] ;

[0067] in, This represents the echo signal after carrier frequency removal. This indicates the total time for the array elements to transmit signals. For signal amplitude, Indicates slow time. Represented as in slow time The position of the time array element at the strong scattering point P. At the speed of light, The wavelength is the signal wavelength.

[0068] right along Perform a second-order Taylor expansion:

[0069] ;

[0070] Among them, the central slope distance , Indicates the altitude of the radar platform. Indicates the speed of the radar platform. This represents the equivalent velocity of the array element in the azimuth direction. In a forward-looking imaging scene... Much larger ,and Much larger and Therefore, the phase difference caused by the quadratic term is much smaller than that caused by the quadratic term. Therefore, regarding Discarding the quadratic term, we get:

[0071] .

[0072] S102. The echo signal after carrier frequency removal is sequentially processed by pulse compression, distance travel correction and Doppler frequency offset correction to obtain the corrected echo signal.

[0073] Optionally, S102 may include:

[0074] The echo signal after carrier frequency removal is pulse compressed to obtain the pulse-compressed signal;

[0075] The pulse-compressed signal is subjected to FFT (Fast Fourier Transform) along the fast time dimension to obtain the transformed signal;

[0076] The transformed signal is multiplied by a pre-constructed linear frequency phase factor and then subjected to IFFT (Inverse Fast Fourier Transform) to obtain the distance-corrected signal.

[0077] The Doppler frequency offset corrected signal is obtained by using the distance-corrected signal and the pre-constructed Doppler frequency offset correction factor;

[0078] The Doppler frequency offset corrected signal is corrected based on the spatially varying phase error to obtain the corrected echo signal.

[0079] The echo signal after carrier frequency removal is pulse compressed to obtain the pulse-compressed signal:

[0080] ;

[0081] in, Indicates signal bandwidth. Let Singer's function be represented. A Fast Fourier Transform (FFT) is performed on the pulse-compressed signal along the fast time dimension, resulting in the following transformed signal representation:

[0082] ;

[0083] in, For range frequency, according to It can be known that the increase in distance traveled by the echo per unit time point is ,because Unknown, therefore can be adopted To approximate ,here, Represents the range coordinates. A linear frequency phase factor is pre-constructed as follows: After multiplying with the transformed signal, perform an IFFT to obtain the distance-corrected signal:

[0084] ;

[0085] Will Substituting the distance-corrected signal into the equation, we obtain a new expression for the distance-corrected signal:

[0086] ;

[0087] in, It can be considered a constant within a distance cell. The Doppler frequency is caused by the equivalent motion of the array antenna and contains the azimuth information of the target (strong scattering point P). The Doppler frequency is caused by the platform's motion.

[0088] because Unknown, therefore can be adopted To approximate The Doppler frequency offset correction factor is pre-constructed as follows: The Doppler frequency offset corrected signal, obtained from the distance-travel-corrected signal and the pre-constructed Doppler frequency offset correction factor, can be expressed as follows:

[0089] ;

[0090] The spatially variable phase error is expressed as follows:

[0091] ;

[0092] in, Indicates spatially variable phase error. This represents the range coordinate of the strong scattering point in a Cartesian coordinate system plane with the radar position as the origin. This represents the azimuth coordinates of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the speed of the radar platform. Indicates the center slope distance.

[0093] Substituting the spatially variable phase error into the signal after Doppler frequency offset correction, the corrected echo signal is obtained as follows:

[0094] ;

[0095] in, Indicates signal bandwidth. This represents the Singer function.

[0096] S103. Construct a forward-looking super-resolution imaging model based on the corrected echo signal.

[0097] Optionally, S103 may include:

[0098] Extract any range cell data from the corrected echo signal;

[0099] Construct a dictionary matrix based on a predefined discrete Doppler sequence;

[0100] A forward-looking super-resolution imaging model is constructed based on the dictionary matrix.

[0101] Specifically, any range cell data is extracted from the corrected echo signal and denoted as the observation signal. It can be represented as:

[0102] ;

[0103] in, , Indicates Gaussian noise. Indicates the first The amplitude of the signal at each strong scattering point. This represents the number of strong scattering points within a range cell. The discrete Doppler sequence is defined as... ,in, The Doppler frequency interval, The pulse repetition frequency, Construct dictionary matrix , represented as:

[0104] ;

[0105] in, Indicates the first time in slow time Each element.

[0106] Then the distance cell data, i.e., the observed signal, can be determined based on the dictionary matrix. Rewritten as the product of the Fourier matrix and the sparse signal, it can be expressed as follows:

[0107] ;

[0108] Among them, sparse signals It contains information on the location and amplitude of strong scattering points.

[0109] S104. Use an imaging algorithm based on sparse Bayesian learning to sparsely reconstruct the forward-looking super-resolution imaging model to obtain a super-resolution image.

[0110] Specifically, the process of sparsely reconstructing the forward-looking super-resolution imaging model using an imaging algorithm based on sparse Bayesian learning to obtain a super-resolution image can be referenced as follows:

[0111] Initialize the hyperparameters of the sparse Bayesian learning algorithm and ,in, Used to describe The state of each element in the data. Let represent the variance of the Gaussian noise. , residual , express The first in Each element is the input observation signal. and dictionary matrix and initialize , The length is equal to , The length is equal to .

[0112] Assume that each element in the recovered signal has a mean of 0 and a variance of . If it follows a normal distribution, then about The probability density function can be expressed as:

[0113] ;

[0114] in, express The first in One element, This represents the probability density.

[0115] according to Then, the Gaussian likelihood model under the sparse Bayesian learning algorithm framework can be expressed as:

[0116] ;

[0117] The observed signal can be obtained using Bayes' theorem. about Marginal probability density function:

[0118] ;

[0119] in, variance diagonal matrix , The identity matrix is ​​the sparse signal to be estimated. Given the observed signal Regarding the situation The marginal probability density function is:

[0120] ;

[0121] in, The above formula also follows a Gaussian distribution, so under the current parameters... The maximum a posteriori (MAP) estimate can be expressed as:

[0122] ;

[0123] ;

[0124] in, Marginal probability density function The mean, Marginal probability density function The variance.

[0125] This invention employs the Expectation Maximization (EM) algorithm to estimate hyperparameters and cost functions. It can be represented as:

[0126] ;

[0127] Estimation under the old hyperparameter conditions Cost function The first item and It is irrelevant; in the E-step of the EM algorithm (i.e., calculating the cost function), the cost function can be... Simplified to cost function :

[0128] ;

[0129] in, It indicates a desire for the expected value.

[0130] because , This indicates the calculation of variance. Represents the first sparse signal Substitute each element into the cost function. have to:

[0131] ;

[0132] in, Marginal probability density function The first of the mean One element;

[0133] In the M-step (maximizing the cost function) of the EM algorithm, the cost function is maximized. To obtain the parameters:

[0134] ;

[0135] right Take the derivative, set it to 0, and proceed to the hyperparameter. Update formula:

[0136] ;

[0137] Fixed update ,renew At this point, the cost function The second item in Regardless, in step E, the cost function Simplified to :

[0138] ;

[0139] because, , Represents any matrix, The mean, For variance, This indicates the transpose operation, for Simplify the second term:

[0140] ;

[0141] in, for Expectations The trace of the matrix;

[0142] From the marginal probability density function available:

[0143] ;

[0144] Substituting the above equation into... ,get:

[0145] ;

[0146] in, These are the hyperparameters from the previous iteration. In the M-step, for... By taking the derivative and setting it to zero, we obtain the hyperparameters. Update formula:

[0147] ;

[0148] Determine the sparse signal under the current conditions If convergence is detected, or if the maximum number of iterations is reached, the loop exits and a sparse signal is output. and for sparse signals The absolute value of each element is taken to obtain the super-resolution image. Then, the next range data is processed. If the condition is not met, the calculation is returned. about probability density function The steps.

[0149] The convergence condition can be expressed as:

[0150] ;

[0151] in, This indicates the result of the previous iteration.

[0152] S105. Correct the super-resolution image based on the pre-calculated spatial phase error to obtain the corrected super-resolution image.

[0153] Optionally, S105 may include:

[0154] The preset image is divided into grids using the azimuth and range resolutions of the super-resolution image to obtain the divided preset image; wherein, the super-resolution image and the preset image have the same size;

[0155] Based on the spatially variable phase error, the coordinates of each pixel in the super-resolution image are calculated to the corresponding pixel in the pre-defined image after segmentation, and the corrected coordinates are obtained.

[0156] Based on the corrected coordinates, the pre-defined image after segmentation is geometrically corrected using sinc kernel interpolation to obtain the corrected super-resolution image.

[0157] Understandably, using super-resolution images The azimuth resolution and range resolution of the preset image The image is divided into a uniform grid, with each pixel representing a small region of the imaging scene. This is useful for super-resolution images. and preset images In the preset image In the middle The pixel at a given location can be used to calculate its corresponding super-resolution image based on the spatially varying phase error. pixels in coordinate:

[0158] The corrected coordinates are represented as follows:

[0159] ;

[0160] in, This represents the coordinates of a pixel in the super-resolution image. This indicates the relationship between the predefined image after segmentation and the super-resolution image. The coordinates of the pixel corresponding to the pixel at that location. This indicates the spatially variable phase error. Indicates the signal wavelength. Indicates the altitude of the radar platform. Indicates the speed of the radar platform. It represents the equivalent velocity of the array element's azimuth direction.

[0161] The detailed derivation process can be found below:

[0162] When the platform is moving at low speed or stationary, it is in a distance-oriented position. Orientation Doppler frequency of the target's echo for:

[0163] ;

[0164] It is important to note here that... ,in, The distance coordinates of the target point on the plane. The azimuth coordinates of the target point on the plane.

[0165] When the platform's movement speed is When, the echo Doppler frequency It then becomes:

[0166] ;

[0167] because , Therefore, it can be simplified:

[0168] ;

[0169] The final coordinate transformation formula is:

[0170] .

[0171] From super-resolution images Find the closest point and take that point as the center The data for the region is denoted as The corresponding sinc interpolation kernel for:

[0172] ;

[0173] in, , , Indicates the range resolution. Indicates the azimuth resolution after super-resolution. The value at that location is:

[0174] ;

[0175] in, The Hadamard product represents the matrix, and after traversing all pixels, the corrected super-resolution image is obtained.

[0176] The following simulation experiments further illustrate the effectiveness of the invention:

[0177] Simulation conditions:

[0178] The parameters of the small array radar system for high-speed moving platforms are set as shown in Table 1.

[0179] Table 1

[0180]

[0181] The simulation experiment compares the traditional super-resolution algorithm with the present invention.

[0182] Simulation content:

[0183] Experiment 1. Set up three point targets within a distance of 6km, at locations of -57m, 0m, and 45m respectively. Figure 3 This is a schematic diagram of a scenario provided by an embodiment of the present invention. Figure 4 This is a schematic diagram of a real-beam imaging result provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the results from a traditional super-resolution imaging algorithm. Figure 6 This is a schematic diagram of an imaging result provided by an embodiment of the present invention. Figures 3-6 All have been normalized. Figures 3-6This indicates that the real beam imaging results could not distinguish the three points, and the results of the traditional super-resolution imaging algorithm showed a shift. The target points appeared at -70m, 0m, and 35m respectively. The two points on the left and right sides of the scene center deviated from the ideal position due to the spatial variation error of the azimuth. The target points of the imaging results of this invention appeared at -58.5m, 0m, and 46.8m respectively, which is consistent with the pre-set scene.

[0184] Experiment 2. Set up an imaging scene for the ship target, and place an interference source on each side of the ship. Figure 7 This is a schematic diagram of a strong scattering point provided in an embodiment of the present invention. Figure 8 This is a schematic diagram of another real-beam imaging result provided by an embodiment of the present invention. Figure 9 This is a schematic diagram illustrating the results of a traditional super-resolution imaging algorithm in another scene. Figure 10 This is a schematic diagram of another imaging result provided by an embodiment of the present invention. Figures 7-10 This indicates that real-beam imaging results cannot distinguish between ship targets and interference sources on both sides. Traditional super-resolution imaging algorithms can distinguish between ship targets and interference sources, but the recovered strong scattering point positions are offset compared to the original positions. The imaging results of this invention are more accurate, eliminating image offset and accurately reconstructing the scene.

[0185] Figure 11 This is a block diagram of a forward-looking super-resolution imaging device for a high-speed platform-borne small array radar provided in an embodiment of the present invention, such as... Figure 11 As shown, the device 1100 may include:

[0186] The signal acquisition module 1101 is used to acquire the echo signal generated by strong scattering points in the radar forward-looking super-resolution imaging scene, and to perform carrier frequency removal processing on the echo signal to obtain the carrier frequency removed echo signal.

[0187] Signal correction module 1102 is used to sequentially perform pulse compression and distance travel correction processing on the echo signal after carrier frequency removal to obtain the corrected echo signal;

[0188] Model building module 1103 is used to build a forward-looking super-resolution imaging model based on the corrected echo signal;

[0189] The image acquisition module 1104 is used to sparsely reconstruct the forward super-resolution imaging model using an imaging algorithm based on sparse Bayesian learning to obtain a super-resolution image.

[0190] The image correction module 1105 is used to correct the super-resolution image based on the pre-calculated spatial phase error to obtain the corrected super-resolution image.

[0191] This invention considers the impact of azimuth spatial variation error on the signal after distance travel correction, resulting in higher accuracy. It utilizes an imaging algorithm based on sparse Bayesian learning to sparsely reconstruct the forward-looking super-resolution imaging model. This algorithm automatically estimates hyperparameters during the iteration process, making it more suitable for practical applications. Furthermore, this invention performs geometric correction on the super-resolution image based on spatial variation phase error, effectively correcting image distortion caused by azimuth spatial variation error, resulting in a better focusing effect for the corrected super-resolution image.

[0192] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention.

[0193] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0194] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0195] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A high-speed platform-borne small array radar forward-looking super-resolution imaging method, characterized in that, The method includes: The echo signal generated by a strong scattering point in the radar forward-looking super-resolution imaging scene is acquired, and the echo signal is processed to remove the carrier frequency to obtain the echo signal after removing the carrier frequency. The echo signal after carrier frequency removal is sequentially processed by pulse compression, distance travel correction and Doppler frequency offset correction to obtain the signal after Doppler frequency offset correction. The Doppler frequency offset corrected signal is corrected based on the pre-calculated spatial phase error to obtain the corrected echo signal. A forward-looking super-resolution imaging model is constructed based on the corrected echo signal; The super-resolution image is obtained by sparsely reconstructing the forward-looking super-resolution imaging model using an imaging algorithm based on sparse Bayesian learning. The super-resolution image is corrected based on the pre-calculated spatial phase error to obtain the corrected super-resolution image. The spatially variable phase error is expressed as follows: ； in, This indicates the spatially variable phase error. This represents the azimuth coordinates of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the center slope distance. This represents the range coordinate of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the speed of the radar platform. Indicates the distance coordinate; The step of correcting the super-resolution image based on the pre-calculated spatially varying phase error to obtain the corrected super-resolution image includes: The preset image is divided into grids using the azimuth resolution and range resolution of the super-resolution image to obtain the divided preset image; wherein, the super-resolution image and the preset image have the same size; Based on the spatially variable phase error, calculate the coordinates of each pixel in the super-resolution image at the corresponding pixel in the divided preset image to obtain the corrected coordinates; Based on the corrected coordinates, geometric correction is performed on the segmented preset image using sinc kernel interpolation to obtain the corrected super-resolution image; The corrected coordinates are represented as follows: ； in, This represents the coordinates of a pixel in the super-resolution image. This indicates the relationship between the predefined image after segmentation and the super-resolution image. The coordinates of the pixel corresponding to the pixel at that location. This indicates the spatially variable phase error. Indicates the signal wavelength. Indicates the speed of the radar platform. It represents the equivalent velocity of the array element's azimuth direction.

2. The forward-looking super-resolution imaging method for a high-speed platform-borne small array radar according to claim 1, characterized in that, The echo signal after carrier frequency removal is sequentially subjected to pulse compression, range travel correction, and Doppler frequency offset correction to obtain the corrected echo signal, including: The echo signal after carrier frequency removal is pulse-compressed to obtain the pulse-compressed signal. The pulse-compressed signal is subjected to FFT along the fast time dimension to obtain the transformed signal; The transformed signal is multiplied by a pre-constructed linear frequency phase factor and then subjected to IFFT to obtain the distance-corrected signal. The Doppler frequency offset corrected signal is obtained based on the distance-corrected signal and the pre-constructed Doppler frequency offset correction factor; The Doppler frequency offset corrected signal is corrected based on the pre-calculated spatial phase error to obtain the corrected echo signal.

3. The forward-looking super-resolution imaging method for a high-speed platform-borne small array radar according to claim 2, characterized in that, The pre-constructed linear frequency phase factor is expressed as follows: ； in, Represents an exponential function. Indicates the imaginary part. Represents the speed of light. This represents the range coordinate of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the speed of the radar platform. Represents the distance coordinate. Indicates slow time. Indicates the range frequency; The pre-constructed Doppler frequency offset correction factor represents: ； in, Indicates the signal wavelength.

4. The forward-looking super-resolution imaging method for a high-speed platform-borne small array radar according to claim 1, characterized in that, The step of constructing a forward-looking super-resolution imaging model based on the corrected echo signal includes: Extract any range cell data from the corrected echo signal; Construct a dictionary matrix based on a predefined discrete Doppler sequence; The forward-looking super-resolution imaging model is constructed based on the dictionary matrix.

5. A forward-looking super-resolution imaging device for a high-speed platform-borne miniature array radar, characterized in that, The device includes: The signal acquisition module is used to acquire the echo signal generated by strong scattering points in the radar forward-looking super-resolution imaging scene, and to perform carrier frequency removal processing on the echo signal to obtain the carrier frequency removed echo signal. The first signal correction module is used to sequentially perform pulse compression and distance travel correction processing on the echo signal after carrier frequency removal to obtain the Doppler frequency offset corrected signal. The second signal correction module is used to correct the Doppler frequency offset corrected signal according to the pre-calculated spatial phase error to obtain the corrected echo signal; the model construction module is used to construct a forward-looking super-resolution imaging model according to the corrected echo signal. The image acquisition module is used to sparsely reconstruct the forward-looking super-resolution imaging model using an imaging algorithm based on sparse Bayesian learning to obtain a super-resolution image. An image correction module is used to correct the super-resolution image based on a pre-calculated spatially varying phase error, thereby obtaining the corrected super-resolution image. The spatially variable phase error is expressed as follows: ； in, This indicates the spatially variable phase error. This represents the azimuth coordinates of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the center slope distance. This represents the range coordinate of the strong scattering point in a Cartesian coordinate system with the radar position as the origin. Indicates the speed of the radar platform. Indicates the distance coordinate; The image correction module is further configured to divide the preset image into grids using the azimuth resolution and range resolution of the super-resolution image to obtain the divided preset image; wherein the super-resolution image and the preset image have the same size; calculate the coordinates of each pixel in the super-resolution image at the corresponding pixel in the divided preset image based on the spatially varied phase error to obtain the corrected coordinates; and perform geometric correction based on sinc kernel interpolation on the divided preset image based on the corrected coordinates to obtain the corrected super-resolution image; The corrected coordinates are represented as follows: ； in, This represents the coordinates of a pixel in the super-resolution image. This indicates the relationship between the predefined image after segmentation and the super-resolution image. The coordinates of the pixel corresponding to the pixel at that location. This indicates the spatially variable phase error. Indicates the signal wavelength. Indicates the speed of the radar platform. It represents the equivalent velocity of the array element's azimuth direction.

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