Lunar observation radar data alignment processing method and device, equipment and medium

Abstract

The application provides a lunar surveying radar data alignment processing method, device, equipment and medium, and relates to the technical field of lunar surveying radar. The method comprises the following steps: S1, reading the lunar surveying radar data to be processed, performing walking data selection and splicing on the lunar surveying radar data, and obtaining a radar data matrix; S2, performing shape-preserving segmented cubic interpolation on the radar data matrix; and S3, performing cross-correlation operation between two signals in the interpolated radar data matrix, and aligning the data by combining the delay amount between the signals. The application utilizes the correlation of radar data to efficiently and accurately perform alignment processing on the lunar surveying radar data.

Description

Technical Field

[0001] This invention relates to the field of lunar radar technology, and in particular to a method, apparatus, equipment and medium for aligning lunar radar data. Background Technology

[0002] Lunar penetrating radar (LPR) is a time-domain sounding radar operating in a carrier-free nanosecond pulse state, employing separate transmit and receive antennas. The LPR transmitter generates ultra-wideband carrier-free nanosecond pulses, which are radiated / coupled into the lunar subsurface via the transmitting antenna. During propagation through the lunar regolith and crustal rock, electromagnetic wave signals are reflected and scattered upon encountering discontinuous interfaces. The LPR receiving antenna receives these reflected and scattered signals, which are then amplified and sampled by the receiver to obtain the corresponding detection data. The scientific data transmitted by the LPR is divided into seven levels after different processing stages: "Preprocessed Input Data," "LPR Source Packet Data 0A," "LPR Scientific Data Block 0B," "LPR Detection Scientific Data Level 1," "LPR Detection Scientific Data Level 2A," "LPR Scientific Data 2B after Geometric Positioning," and "LPR Scientific Data Level 2C." The processing of Level 2B data includes data alignment operations.

[0003] During its operation on the lunar surface, the lunar penetrating radar does not work continuously. Instead, it stops at each navigation point and even shuts down to survive the lunar night. Due to the limited sampling rate of the equipment, there are inevitably some deviations in the sampling points of each column of the lunar penetrating radar data matrix. These deviations will result in inconsistent zero points and poor continuity of the data after all the walks are stitched together, requiring correction. Therefore, it is necessary to align the lunar penetrating radar walk data.

[0004] Currently, the commonly used data alignment method aligns all radar data by using the first trough sampling point of a single waveform as the calibration point. This involves first obtaining the trough positions of all data columns, then performing histogram analysis, and finally correcting the trough points to their maximum possible positions. The drawback of this method is that it requires manual judgment and selection of abnormal trough points for correction in practice, and abnormal trough points can still appear, causing spikes in the radar image, making it inefficient and inaccurate. Therefore, a more efficient and accurate lunar radar data alignment method is desired. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method, apparatus, device and medium for aligning lunar radar data, which utilizes the correlation of radar data to efficiently and accurately align lunar radar data.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for aligning lunar radar data, comprising: step S1, reading lunar radar data to be processed, selecting and stitching the lunar radar data to obtain a radar data matrix; step S2, performing conformal segmented cubic interpolation on the radar data matrix; and step S3, performing cross-correlation operations between pairs of signals in the interpolated radar data matrix, and aligning the data by combining the delay between the signals.

[0007] Further, in step S1, the lunar radar data is selected and stitched together, specifically including: step S11, drawing a radar profile based on the lunar radar data; step S12, extracting multiple data segments with continuous walking characteristics from the radar profile, deduplicating the multiple data segments, and stitching the deduplicated data segments together to form a radar data matrix.

[0008] Furthermore, the lunar radar data includes one-channel radar data and two-channel radar data. The radar data of each channel is stored in binary format, and is divided into channels, containing multiple channels of radar data. Specifically, the radar data of each channel includes channel head and channel scientific data. The number of sampling points for the radar data of the two channels is different, while the number of sampling points for each channel of radar data within the same channel is the same.

[0009] Furthermore, the radar profile is obtained by arranging the radar data of each channel in the two channels according to the radar travel sequence.

[0010] Further, step S2 specifically includes: step S21, pre-setting an interpolation factor, using the interpolation factor to expand the radar data matrix, and calculating the size of the expanded radar data matrix; step S22, using columns as processing units, calculating the interpolation data of each column in the expanded radar data matrix using the conformal piecewise cubic interpolation method.

[0011] Further, step S3 specifically includes: Step S31, using columns as the processing unit, traversing all columns of the expanded radar data matrix, using the cross-correlation of the sequences and combining the sequence cross-correlation formula, calculating the cross-correlation between the nth column signal and the 1st column signal respectively, to obtain a cross-correlation sequence, where n is the number of columns in the expanded radar data matrix, n≥1; Step S32, using the cross-correlation sequence to determine the position of the maximum cross-correlation between any column signal in the expanded radar data matrix and the 1st column signal, and calculating the delay τ of the nth column signal relative to the 1st column signal; Step S33, based on the delay τ, aligning the nth column signal with the 1st column signal by truncating or padding the nth column signal.

[0012] Furthermore, in step S33, according to the delay amount τ, the truncation or zero-padding operation of the nth column signal further includes: determining whether the delay amount τ is greater than or equal to 0; if so, adding τ zero data points before the nth column signal; otherwise, truncating the first τ data points in the nth column signal.

[0013] A second aspect of the present invention provides a lunar radar data alignment and processing device, comprising: a data selection and splicing module for reading lunar radar data to be processed, selecting and splicing the lunar radar data to obtain a radar data matrix; a matrix interpolation module for performing conformal piecewise cubic interpolation on the radar data matrix; and a data alignment module for performing cross-correlation operations between pairs of signals in the interpolated radar data matrix and aligning the data by combining the delay between the signals.

[0014] A third aspect of the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described method.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the above-described method.

[0016] Compared with the prior art, the lunar radar data alignment processing method, apparatus, equipment, and medium provided by the present invention have at least the following beneficial effects:

[0017] (1) First, use interpolation algorithm to obtain more sample data, and then combine the correlation between lunar radar data to perform data alignment using sequence cross-correlation, which is beneficial for subsequent data editing and imaging of radar data.

[0018] (2) Aligning the lunar radar walking data helps to eliminate data deviation caused by the limited sampling rate of the equipment, facilitates further processing of the data, obtains better imaging results, and ultimately facilitates data interpretation. Attached Figure Description

[0019] The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0020] Figure 1 A flowchart illustrating a lunar radar data alignment processing method according to an embodiment of the present invention is shown schematically.

[0021] Figure 2 A flowchart illustrating the process of selecting and stitching walking data according to an embodiment of the present invention is shown in the schematic diagram.

[0022] Figure 3 The diagram schematically illustrates a radar profile corresponding to lunar radar data using two-channel 2B-level data according to an embodiment of the present invention.

[0023] Figure 4 A flowchart illustrating the conformal piecewise cubic interpolation process according to an embodiment of the present invention is shown schematically.

[0024] Figure 5 A flowchart illustrating the cross-correlation calculation and data alignment process according to an embodiment of the present invention is shown in the schematic diagram.

[0025] Figure 6 This schematically illustrates a comparison of radar profiles before and after data alignment of lunar radar data from two-channel 2B-level data according to an embodiment of the present invention.

[0026] Figure 7 A block diagram of a lunar radar data alignment and processing apparatus according to an embodiment of the present invention is shown schematically.

[0027] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a lunar radar data alignment processing method according to an embodiment of the present disclosure. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0031] Figure 1 A flowchart illustrating a lunar radar data alignment processing method according to an embodiment of the present invention is shown.

[0032] like Figure 1As shown, the lunar radar data alignment processing method according to this embodiment may include steps S1 to S3.

[0033] Step S1: Read the lunar radar data to be processed, select and stitch the lunar radar data to obtain the radar data matrix.

[0034] Step S2: Perform conformal piecewise cubic interpolation on the radar data matrix.

[0035] Step S3: In the interpolated radar data matrix, perform cross-correlation calculations between pairs of signals and align the data by combining the delay between signals.

[0036] Through the embodiments of the present invention, the correlation of radar data is utilized to efficiently and accurately align lunar radar data.

[0037] The lunar penetrating radar comprises two sets of transceiver antennas: one channel (low frequency) and two channels (high frequency). The first channel radar data has 4096 sampling points, and the second channel radar data has 2048 sampling points. In this embodiment of the invention, the lunar penetrating radar data includes one-channel and two-channel radar data. The radar data for each channel is stored in binary format, and each channel contains multi-channel radar data (2B-level data). Each channel's radar data includes the channel head and channel scientific data. The number of sampling points differs between the two channels, but the number of sampling points for each radar data point within the same channel is the same. For example, this lunar penetrating radar data is two-channel 2B-level lunar penetrating radar data.

[0038] Because the radar does not operate continuously on the lunar surface, the original lunar penetrating radar data includes not only travel data but also a small amount of repetitive data. Based on this, Figure 2 The flowchart illustrating the process of selecting and stitching walking data according to an embodiment of the present invention is shown in the illustration.

[0039] like Figure 2 As shown, in step S1 above, the lunar radar data is selected and stitched together, which may specifically include steps S11 to S12.

[0040] Step S11: Draw a radar profile based on the lunar penetrating radar data.

[0041] Among them, based on the storage format of the lunar radar data mentioned above, the radar profile is obtained by arranging the radar data of each of the two channels in the radar travel order.

[0042] It should be noted that lunar penetrating radar data is divided into single-channel and two-channel data, which are acquired using different methods and are subsequently processed and analyzed separately. Since a radar profile can only be drawn from data from the same channel, there will be two different radar profiles displaying different subsurface information. However, in this embodiment of the invention, the radar profile only processes Class 2B radar data within the second channel; therefore, the radar profile in step S11 specifically refers to the one drawn using data from the second channel.

[0043] Step S12: Extract multiple data segments with continuous walking characteristics from the radar profile, remove duplicates from the multiple data segments, and splice the deduplicated data segments to form a radar data matrix.

[0044] The aforementioned walking data refers to multiple data segments exhibiting continuous walking characteristics. Based on the radar profile drawn in the previous step, the data segments with continuous walking characteristics are extracted and then stitched together to obtain the required radar data matrix.

[0045] Figure 3 The diagram schematically illustrates a radar profile corresponding to lunar radar data using two-channel 2B-level data according to an embodiment of the present invention.

[0046] like Figure 3 As shown, the reason for the generation of horizontally uniform striped images is that the lunar penetrating radar did not move during that time period or was in a powered-off state. The uneven positions of the images are the walking data that needs to be selected, that is, multiple data segments with continuous walking characteristics.

[0047] Figure 4 A flowchart illustrating a conformal piecewise cubic interpolation process according to an embodiment of the present invention is shown.

[0048] like Figure 4 As shown in the embodiment of the present invention, step S2 may specifically include steps S21 to S22.

[0049] Step S21: Pre-set the interpolation factor, use the interpolation factor to expand the radar data matrix, and calculate the size of the expanded radar data matrix.

[0050] Optionally, the interpolation factor can be preset to 30 times, for example, and then the size of the expanded data matrix is ​​calculated by setting the interpolation factor to 30 times.

[0051] Step S22: Using columns as the processing unit, the interpolated data of each column in the expanded radar data matrix is ​​calculated using the conformal piecewise cubic interpolation method.

[0052] Taking the two-channel 2B level data as an example, a total of 1309 walk data points were selected from 5 2B level data points. Each walk data point contains 2048 sampling points. The original radar data matrix size is 2048×1309. After 30 times interpolation, the radar data matrix is ​​expanded to 61441×1309.

[0053] Figure 5 A flowchart illustrating the cross-correlation calculation and data alignment process according to an embodiment of the present invention is shown.

[0054] like Figure 5 As shown in the embodiment of the present invention, step S3 may specifically include steps S31 to S33.

[0055] Step S31: Using columns as the processing unit, traverse all columns in the expanded radar data matrix, and use the cross-correlation of the sequences and the sequence cross-correlation formula to calculate the cross-correlation between the signal in the nth column and the signal in the 1st column respectively, to obtain the cross-correlation sequence, where n is the number of columns in the expanded radar data matrix, and n≥1.

[0056] The cross-correlation formula can be:

[0057] R xy (l)=y(m)*x * (-m)

[0058] In the formula, R xy (l) represents the cross-correlation sequence; y(m) represents the signal value of the first column; x * (-m) represents the signal value of the m-th column.

[0059] Step S32: Use the cross-correlation sequence to determine the position of the maximum cross-correlation between any column of signal in the expanded radar data matrix and the first column of signal, and calculate the delay τ of the nth column of signal relative to the first column of signal.

[0060] Step S33: Based on the delay amount τ, the nth column signal is aligned with the 1st column signal by truncating or padding the nth column signal with zeros.

[0061] Furthermore, in step S33, based on the delay amount τ, the operation of truncating or padding the nth column signal further includes: determining whether the delay amount τ is greater than or equal to 0; if so, padding the nth column signal with τ zero data points; otherwise, truncating the first τ data points in the nth column signal.

[0062] Figure 6 The diagram schematically illustrates a comparison of radar profiles before and after data alignment of lunar radar data from two-channel 2B-level data according to an embodiment of the present invention.

[0063] like Figure 6As shown, the left image represents the radar profile without data alignment, while the right image represents the radar profile after alignment. It can be seen from the images that the effect of alignment is mainly reflected at the data zero point, approximately the first 4000 data points.

[0064] Through the embodiments of the present invention, a radar profile of the lunar penetrating radar two-channel 2B data is first drawn, and the radar walking data is selected from it. Then, conformal segmented cubic interpolation is performed on it, and cross-correlation calculation is performed between pairs of signals. The data is aligned by combining the delay between signals, which effectively corrects the inconsistency of the zero point of the lunar penetrating radar walking data time. This is beneficial for us to carry out the next step of data editing and processing, and thus obtain a better imaging effect. It is also beneficial for layer identification and target extraction in radar data.

[0065] Therefore, this invention first utilizes interpolation algorithms to obtain more sample data, and then combines the correlation between lunar radar data to perform data alignment using sequence cross-correlation, which is beneficial for subsequent data editing and imaging. Furthermore, this invention aligns the lunar radar walking data, which helps eliminate data bias caused by the limited sampling rate of the equipment, facilitates further data processing, obtains better imaging results, and ultimately benefits data interpretation.

[0066] Based on the methods disclosed above, the present invention also provides a lunar radar data alignment and processing device, which will be described below in conjunction with... Figure 7 The device is described in detail.

[0067] Figure 7 A block diagram of a lunar radar data alignment and processing apparatus according to an embodiment of the present invention is shown schematically.

[0068] like Figure 7 As shown, the lunar radar data alignment processing device 700 according to this embodiment includes a data selection and splicing module 710, a matrix interpolation module 720 and a data alignment module 730.

[0069] The data selection and stitching module 710 is used to read the lunar radar data to be processed, select and stitch the lunar radar data to obtain a radar data matrix.

[0070] The matrix interpolation module 720 is used to perform conformal piecewise cubic interpolation on the radar data matrix.

[0071] The data alignment module 730 is used to perform cross-correlation calculations between pairs of signals in the interpolated radar data matrix and align the data by combining the delay between the signals.

[0072] It should be noted that the embodiments of the device section are similar to those of the method section, and the technical effects achieved are also similar. For specific details, please refer to the above-mentioned method embodiment section, which will not be repeated here.

[0073] According to embodiments of the present invention, any plurality of the data selection and splicing module 710, matrix interpolation module 720, and data alignment module 730 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present invention, at least one of the data selection and splicing module 710, matrix interpolation module 720, and data alignment module 730 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the data selection and splicing module 710, matrix interpolation module 720, and data alignment module 730 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0074] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing a lunar radar data alignment processing method according to an embodiment of the present disclosure.

[0075] like Figure 8 As shown, an electronic device 800 according to an embodiment of this disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.

[0076] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0077] According to embodiments of this disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0078] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the lunar radar data alignment processing method according to the embodiments of this disclosure.

[0079] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803 described above.

[0080] The accompanying drawings show some block diagrams and / or flowcharts. It should be understood that some blocks or combinations thereof in the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when executed by the processor, these instructions can create means for implementing the functions / operations described in these block diagrams and / or flowcharts.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

[0082] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for aligning and processing lunar radar data, characterized in that, include: Step S1: Read the lunar radar data to be processed, select and stitch the lunar radar data to obtain a radar data matrix; Step S2: Perform conformal segmented cubic interpolation on the radar data matrix; Step S3: In the interpolated radar data matrix, perform cross-correlation calculations between pairs of signals and align the data by combining the delay between signals. Step S3 specifically includes: Step S31: Using columns as the processing unit, traverse all columns in the expanded radar data matrix, and use the cross-correlation of the sequences and the sequence cross-correlation formula to calculate the cross-correlation between the signal in the nth column and the signal in the 1st column respectively to obtain the cross-correlation sequence, where n is the number of columns in the expanded radar data matrix, and n≥1. Step S32: Use the cross-correlation sequence to determine the position of the maximum cross-correlation between any column of signal in the expanded radar data matrix and the first column of signal, and calculate the delay τ of the nth column of signal relative to the first column of signal; Step S33: Based on the delay amount τ, the nth column signal is aligned with the 1st column signal by truncating or padding the nth column signal with zeros.

2. The lunar radar data alignment processing method according to claim 1, characterized in that, In step S1, the lunar penetrating radar data is selected and stitched together, specifically including: Step S11: Draw a radar profile based on the lunar penetrating radar data; Step S12: Extract multiple data segments with continuous walking characteristics from the radar profile, remove duplicates from the multiple data segments, and splice the deduplicated data segments to form a radar data matrix.

3. The lunar radar data alignment processing method according to claim 2, characterized in that, The lunar penetrating radar data includes one-channel radar data and two-channel radar data. Each channel's radar data is stored in binary format, on a channel-by-channel basis, and contains multi-channel radar data, wherein: The radar data for each channel includes track head and track science data; The number of sampling points for radar data in the two channels is different, while the number of sampling points for each radar data channel within the same channel is the same.

4. The lunar radar data alignment processing method according to claim 3, characterized in that, The radar profile is obtained by arranging the radar data of each channel in the two channels according to the radar travel sequence.

5. The lunar radar data alignment processing method according to claim 1, characterized in that, Step S2 specifically includes: Step S21: Pre-set the interpolation factor, use the interpolation factor to expand the radar data matrix, and calculate the size of the expanded radar data matrix; Step S22: Using columns as the processing unit, the interpolated data of each column in the expanded radar data matrix is ​​calculated using the conformal piecewise cubic interpolation method.

6. The lunar radar data alignment processing method according to claim 1, characterized in that, In step S33, based on the delay amount τ, the process of truncating or padding the nth column signal with zeros further includes: Determine whether the delay τ is greater than or equal to 0. If it is, add τ zero data points before the nth column of the signal; otherwise, truncate the first τ data points in the nth column of the signal.

7. A lunar radar data alignment and processing device, characterized in that, include: The data selection and stitching module is used to read the lunar radar data to be processed, select and stitch the lunar radar data to obtain a radar data matrix. The matrix interpolation module is used to perform conformal piecewise cubic interpolation on the radar data matrix. The data alignment module is used to perform cross-correlation calculations between pairs of signals in the interpolated radar data matrix and align the data by combining the delay between the signals. The step of performing cross-correlation calculations between pairs of signals in the interpolated radar data matrix and aligning the data by combining the delay between signals specifically includes: Using columns as the processing unit, traverse all columns in the expanded radar data matrix, and use the cross-correlation of the sequences and the sequence cross-correlation formula to calculate the cross-correlation between the signal in the nth column and the signal in the 1st column respectively, to obtain the cross-correlation sequence, where n is the number of columns in the expanded radar data matrix, and n≥1; The cross-correlation sequence is used to determine the position of the maximum cross-correlation between any column of signal in the expanded radar data matrix and the first column of signal, and the delay τ of the nth column of signal relative to the first column of signal is calculated. Based on the delay τ, the nth column signal is aligned with the 1st column signal by truncating or padding with zeros.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, cause the processor to implement the method of any one of claims 1 to 6.

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