Radar-based adaptive polarization filtering method and system
By using radar adaptive polarization filtering, auxiliary channels are used to cancel clutter in the main channel, which improves the signal-to-noise ratio and target detection accuracy. This solves the problem of detection difficulties for traditional radar receivers in cluttered environments and achieves engineering friendliness of adaptive filtering.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING INST OF REMOTE SENSING EQUIP
- Filing Date
- 2025-12-16
- Publication Date
- 2026-05-08
AI Technical Summary
Traditional radar receiver filtering methods cannot be adaptively adjusted, resulting in reduced target detection accuracy under conditions of high clutter and noise. Clutter suppression is difficult, especially when the interference and signal directions are the same or close, the desired signal is easily canceled out.
An adaptive polarization filtering method based on radar is adopted. By acquiring the orthogonal polarization state data of the main channel and the auxiliary channel, the cancellation weight coefficient of the auxiliary channel is calculated, and the auxiliary channel is used to cancel clutter in the main channel, thereby suppressing clutter interference in the main channel and improving the signal-to-noise ratio.
It effectively improves the reception quality of useful signals, enhances target detection capabilities, and has the advantage of automatically compensating for amplitude and phase imbalances between channels. It is also relatively easy to implement in engineering.
Smart Images
Figure CN121995325A_ABST
Abstract
Description
Technical Field
[0001] This specification relates to the field of radar signal processing technology, and more specifically, to a radar-based adaptive polarization filtering method and system. Background Technology
[0002] Radar technology is widely used in meteorological monitoring, military defense, aerospace, and traffic monitoring. Traditional receivers often rely on preset models or parameters for filtering, making them unable to adaptively adjust to different clutter environments, resulting in poor filtering performance. In single-polarization adaptive receivers, when interference and signal directions are the same or similar, the desired signal is easily and severely canceled out. Under conditions of high clutter and noise, target detection accuracy decreases, and clutter suppression becomes difficult. Summary of the Invention
[0003] The purpose of this specification is to provide a radar-based adaptive polarization filtering method that can overcome the aforementioned shortcomings of traditional receiver filtering methods.
[0004] The embodiments described in this specification are implemented as follows:
[0005] On the one hand, this specification provides a radar-based adaptive polarization filtering method, which mainly includes:
[0006] Obtain discrete data from the main channel and discrete data from the auxiliary channel, wherein the polarization states of the main channel and the auxiliary channel are orthogonal.
[0007] The discrete data of the main channel is processed with preset weight coefficients to obtain the polarization data of the main channel;
[0008] Based on the discrete data of the auxiliary channel and the polarization data of the main channel, the cancellation weight coefficient of the auxiliary channel is obtained;
[0009] Based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, polarization cancellation is performed on the polarization data of the main channel to obtain the cancelled main channel signal data.
[0010] On the other hand, this specification provides a radar-based adaptive polarization filtering system, which mainly includes:
[0011] The acquisition module is used to acquire discrete data of the main channel and discrete data of the auxiliary channel, wherein the polarization states of the main channel and the auxiliary channel are orthogonal.
[0012] The processing module is used to process the discrete data of the main channel with preset weight coefficients to obtain the polarization data of the main channel;
[0013] The calculation module is used to obtain the cancellation weighting coefficient of the auxiliary channel based on the discrete data of the auxiliary channel and the polarization data of the main channel;
[0014] The cancellation module is used to perform polarization cancellation on the polarization data of the main channel based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, so as to obtain the cancelled main channel signal data.
[0015] The embodiments described in this specification have at least the following advantages or beneficial effects:
[0016] This radar-based adaptive polarization filtering method calculates the weighting coefficients of the two channels using orthogonally polarized channel signals. By using the auxiliary channel to cancel clutter in the main channel, it suppresses clutter interference in the main channel, thereby improving the signal-to-noise ratio, effectively enhancing the reception quality of useful signals, and ultimately strengthening target detection capabilities. Furthermore, this method has the advantage of automatically compensating for amplitude and phase inconsistencies between channels and is easy to implement in engineering. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this specification and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart illustrating the radar-based adaptive polarization filtering method provided in this specification.
[0019] Figure 2 This is another flowchart illustrating the radar-based adaptive polarization filtering method provided in this specification.
[0020] Figure 3 This is a schematic diagram of the signal spectrum before vertical S-path polarization provided in this specification.
[0021] Figure 4 This is a schematic diagram of the signal spectrum after vertical S-path polarization provided in this specification.
[0022] Figure 5 This is a schematic diagram of the signal spectrum before vertical Z-polarization provided in this specification.
[0023] Figure 6 This is a schematic diagram of the signal spectrum after vertical Z-polarization provided in this specification.
[0024] Figure 7 This is a schematic diagram of the radar-based adaptive polarization filtering system provided in this specification. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of the embodiments in this specification clearer, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Generally, the components of the embodiments of this specification described and shown in the accompanying drawings can be arranged and designed in various different configurations.
[0026] Please refer to Figures 1 to 6 One embodiment of this specification provides a radar-based adaptive polarization filtering method, which mainly includes:
[0027] Step 102: Obtain discrete data of the main channel and discrete data of the auxiliary channel, wherein the polarization states of the main channel and the auxiliary channel are orthogonal.
[0028] Step 104: Process the discrete data of the main channel with preset weight coefficients to obtain the polarization data of the main channel;
[0029] Step 106: Based on the discrete data of the auxiliary channel and the polarization data of the main channel, obtain the cancellation weighting coefficient of the auxiliary channel;
[0030] Step 108: Based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, polarization cancellation is performed on the polarization data of the main channel to obtain the cancelled main channel signal data.
[0031] In this embodiment, the vertical direction is the primary polarization direction, and the horizontal direction is the secondary polarization direction. That is, the channel with vertical polarization is the primary channel, and the channel with horizontal polarization is the secondary channel. The two polarization states are orthogonal (i.e., vertical).
[0032] In other embodiments, the polarization direction of the main channel (i.e., the main polarization direction) may be a horizontal polarization direction.
[0033] In this embodiment, the main channel polarization data can be obtained by multiplying the main channel discrete data by a preset weight coefficient.
[0034] In this embodiment, the preset weighting coefficients can be set according to requirements.
[0035] In this embodiment, the above method calculates the weighting coefficients of the two channels using orthogonal polarization channel signals, and suppresses clutter interference in the main channel by using the auxiliary channel to cancel clutter in the main channel, thereby improving the signal-to-noise ratio, effectively improving the reception quality of useful signals, and thus enhancing target detection capabilities. Moreover, the above method has the advantage of automatically compensating for amplitude and phase inconsistencies between channels, and is easy to implement in engineering.
[0036] In this embodiment, one implementation of step 102 is as follows:
[0037] Step 112: Acquire raw signal data from the main channel and the auxiliary channel;
[0038] Step 114: Perform analog-to-digital conversion on the original signal data of the main channel and the original signal data of the auxiliary channel respectively to obtain digital signal data;
[0039] Step 116: Perform down-conversion and filtering on the digital signal data to obtain the main channel discrete data and the auxiliary channel discrete data, respectively.
[0040] In this embodiment, the discrete data of the main channel and the discrete data of the auxiliary channel after down-conversion and filtering are represented by M-dimensional vectors as V=[v1,v2,v3,…v m ] T and H = [h1,h2,h3,…h m ] T .
[0041] In this embodiment, one implementation of step 106 is as follows:
[0042] Step 122: Obtain the autocorrelation matrix of the auxiliary channel signal based on the discrete data of the auxiliary channel;
[0043] Step 124: Based on the discrete data of the auxiliary channel and the polarization data of the main channel, obtain the cross-correlation matrix between the main channel signal and the auxiliary channel signal;
[0044] Step 126: Obtain the cancellation weighting coefficient of the auxiliary channel based on the autocorrelation matrix of the auxiliary channel signal and the cross-correlation matrix of the main channel signal and the auxiliary channel signal.
[0045] In this embodiment, the cancellation weight coefficient of the auxiliary channel is calculated based on the cross-correlation between the discrete data of the main channel and the discrete data of the auxiliary channel, which can effectively achieve the effect of canceling the clutter of the main channel.
[0046] In this embodiment, the autocorrelation matrix R of the auxiliary channel signal HH for:
[0047] R HH =H*H T
[0048] Where H = [h1, h2, ..., h i ,…,h m ] T h represents the discrete data of the auxiliary channel. i This represents the discrete data for the i-th auxiliary channel.
[0049] In this embodiment, the cross-correlation matrix R between the main channel signal and the auxiliary channel signalHV for:
[0050]
[0051] Among them, V dbf =[v S ,v Y ,v Z ,v D ] T For the main channel polarization data, v S v Y v Z and v D These are polarization data for the four main channels. It is V dbf The conjugate transpose of .
[0052] In this embodiment, the cancellation weighting coefficient of the auxiliary channel is:
[0053] W n =inv(R HH )*R HV
[0054] Among them, W n The cancellation weighting coefficient for the auxiliary channel is inv(R). HH ) is R HH The inverse matrix.
[0055] As can be seen, the above method calculates the cancellation weight coefficients of the auxiliary channel by specifically using the autocorrelation matrix and cross-correlation matrix, which has the advantages of simple logic and low computational cost.
[0056] In this embodiment, the cancellation weight coefficient of the auxiliary channel is calculated based on the double-precision floating-point method of the FPGA chip.
[0057] In this embodiment, to further reduce the complexity of matrix operations, the fixed-point calculation method of the FPGA chip is converted to the above-mentioned double-precision floating-point method for calculation, which can effectively reduce the complexity of the operation and meet the high precision requirements of the data.
[0058] In this embodiment, one implementation of step 108 is as follows:
[0059] Step 132: Determine the maximum value of the cancellation weight coefficient based on the cancellation weight coefficient of the auxiliary channel;
[0060] Step 134: Based on the maximum value of the cancellation weight coefficient, normalize the cancellation weight coefficient of the auxiliary channel to obtain the normalized weight coefficient of the main channel.
[0061] Step 136: Normalize the main channel polarization data using the normalized weighting coefficient of the main channel to obtain normalized main channel polarization data;
[0062] Step 138: Based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, perform polarization cancellation on the normalized polarization data of the main channel to obtain the cancelled main channel signal data.
[0063] In this embodiment, by normalizing the main channel polarization data, the main channel polarization data and the auxiliary channel polarization data can be calculated at the same scale, reducing the difficulty of calculation and improving the accuracy of the calculation results.
[0064] In this embodiment, based on the cancellation weight coefficient of the auxiliary channel, the maximum value of the cancellation weight coefficient is determined to be W. i With this maximum value W i The cancellation weight coefficients of the auxiliary channels are normalized to obtain the normalized weight coefficients w of the main channel. a The following example will illustrate this in detail:
[0065] According to the cancellation weight coefficient W of the auxiliary channel n The magnitudes of each element are determined, and a preset value is set. This preset value is then used as the cancellation weighting coefficient W for the auxiliary channel. n One element, finding the preset value and the canceling weight coefficient W of the auxiliary channel. n The maximum value among the elements is determined by the preset value. If the preset value is the maximum value, then the preset value is normalized to 1, and other elements are normalized proportionally. If an element is the maximum value, then the current maximum value is normalized to 1, and other elements (including the preset value) are normalized proportionally. The normalized preset value becomes the normalization weight coefficient w of the main channel. a .
[0066] With W n Taking a single channel as an example. Let's assume the cancellation weight coefficient for this channel is [0.5, 0.8, 0.9, 0.6]. The preset value is 1, so the maximum value is 1. The normalized W... n = [0.5, 0.8, 0.9, 0.6], then the normalized weight coefficient corresponding to this channel is 1.
[0067] With W n Taking one channel as an example. The cancellation weight coefficient W of this channel. n Taking [0.5, 0.8, 1.5, 0.6] as an example, with a preset value of 1, the maximum value is 1.5, and the normalized W is... n =[0.33,0.53,1,0.4], then the normalized weight coefficient corresponding to this channel is 1 / 3, which is 0.67.
[0068] In this embodiment, the above-mentioned W n It has four channels, and the above normalization process is performed on each of the four channels. That is, the preset value and the data of each channel are compared to determine the maximum value of the current channel, thereby normalizing the current channel.
[0069] In this embodiment, the cancellation weighting coefficient W of the auxiliary channel n Let w be the matrix composed of the channels. a This is a matrix consisting of the normalized weight coefficients corresponding to each channel.
[0070] In this embodiment, the above-described configuration allows the normalized weighting coefficient w of the main channel to be adjusted. a The value is always less than 1, thus ensuring that the signal bit width is within the preset range, thereby reducing the computational load and preventing the signal bit width from widening as the number of computations increases.
[0071] In this embodiment, the canceled main channel signal data is:
[0072] V O =w a V dbf -W n *H T
[0073] Among them, w a V is the normalized weighting coefficient of the main channel. dbf =[v S ,v Y ,v Z ,v D ] T For the main channel polarization data, v S v Y v Z and v D These are the polarization data for the four main channels, W n H represents the cancellation weighting coefficient of the auxiliary channel, where H = [h1, h2, ..., h i ,…,h m ] T h represents the discrete data of the auxiliary channel. i This represents the discrete data for the i-th auxiliary channel.
[0074] In this embodiment, the auxiliary channel polarization data H can be obtained by multiplying the cancellation weight coefficient of the auxiliary channel obtained in the current calculation with the discrete data of the auxiliary channel. dbf That is, H dbf =W n *H T =[h S ,h Y,h Z ,h D ] T , where H S H Y H Z and H D These are polarization data for the four auxiliary channels.
[0075] In this embodiment, the above cancellation method can cancel the clutter of the main channel polarized data, thereby achieving the effect of suppressing the clutter interference of the main channel polarized data.
[0076] For a detailed example of the engineering application of the above method in this embodiment, please refer to [link / reference needed]. Figures 3 to 6 Moreover, according to Figures 3 to 6 It can be seen that the vertical S-path and Z-path spectra after polarization cancellation have a significant suppression effect on signal clutter. It is evident that the above method effectively improves the signal-to-noise ratio of the main channel polarization data and enhances the calculation accuracy of subsequent signal velocity, position and other information, further demonstrating the reliability and effectiveness of the above method.
[0077] Please refer to Figure 7 Another embodiment of this specification provides a radar-based adaptive polarization filtering system, mainly comprising:
[0078] The acquisition module 202 is used to acquire discrete data of the main channel and discrete data of the auxiliary channel, wherein the polarization states of the main channel and the auxiliary channel are orthogonal.
[0079] Processing module 204 is used to process the main channel discrete data with preset weight coefficients to obtain main channel polarization data;
[0080] Calculation module 206 is used to obtain the cancellation weight coefficient of the auxiliary channel based on the discrete data of the auxiliary channel and the polarization data of the main channel;
[0081] The cancellation module 208 is used to perform polarization cancellation on the polarization data of the main channel according to the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, so as to obtain the cancelled main channel signal data.
[0082] In this embodiment, the above method calculates the weighting coefficients of the two channels using orthogonal polarization channel signals, and suppresses clutter interference in the main channel by using the auxiliary channel to cancel clutter in the main channel, thereby improving the signal-to-noise ratio, effectively improving the reception quality of useful signals, and thus enhancing target detection capabilities. Moreover, the above method has the advantage of automatically compensating for amplitude and phase inconsistencies between channels, and is easy to implement in engineering.
[0083] In this embodiment, the acquisition module 202 is used to acquire the original signal data of the main channel and the original signal data of the auxiliary channel; to perform analog-to-digital conversion on the original signal data of the main channel and the original signal data of the auxiliary channel respectively to obtain digital signal data; and to perform down-conversion and filtering processing on the digital signal data to obtain the discrete data of the main channel and the discrete data of the auxiliary channel respectively.
[0084] In this embodiment, the aforementioned digital signal data can be down-converted and filtered by the down-conversion filter module.
[0085] In this embodiment, the calculation module 206 is used to obtain the autocorrelation matrix of the auxiliary channel signal based on the auxiliary channel discrete data; to obtain the cross-correlation matrix of the main channel signal and the auxiliary channel signal based on the auxiliary channel discrete data and the main channel polarization data; and to obtain the cancellation weight coefficient of the auxiliary channel based on the autocorrelation matrix of the auxiliary channel signal and the cross-correlation matrix of the main channel signal and the auxiliary channel signal. Calculating the cancellation weight coefficient of the auxiliary channel using the cross-correlation of the main channel discrete data and the auxiliary channel discrete data can effectively achieve the effect of canceling main channel clutter. The above method calculates the cancellation weight coefficient of the auxiliary channel using specific autocorrelation and cross-correlation matrices, which has the advantages of simple logic and low computational complexity.
[0086] In this embodiment, the main channel polarization data and auxiliary channel polarization data obtained by calculating the weight coefficients or preset weight coefficients can be processed by the DBF module (beamforming module).
[0087] In this embodiment, the cancellation module 208 is used to determine the maximum value of the cancellation weight coefficient based on the cancellation weight coefficient of the auxiliary channel; normalize the cancellation weight coefficient of the auxiliary channel based on the maximum value of the cancellation weight coefficient to obtain the normalized weight coefficient of the main channel; normalize the polarization data of the main channel using the normalized weight coefficient of the main channel to obtain the normalized polarization data of the main channel; and perform polarization cancellation on the normalized polarization data of the main channel based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel to obtain the canceled main channel signal data. By normalizing the polarization data of the main channel, the polarization data of the main channel and the polarization data of the auxiliary channel can be calculated at the same scale, reducing the computational difficulty and improving the accuracy of the computation results. The above cancellation method can cancel the clutter of the polarization data of the main channel, thereby achieving the effect of suppressing the clutter interference of the polarization data of the main channel.
[0088] Based on the same inventive concept, another embodiment of this specification provides a computer-readable storage medium storing one or more programs, which, when executed by an electronic device including multiple application programs, cause the electronic device to perform... Figure 1 The corresponding embodiment provides a radar-based adaptive polarization filtering method.
[0089] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0090] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.
[0091] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0092] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 The device that provides the function specified in each box.
[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.
[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0095] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0096] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0097] The above description is merely an embodiment of this application and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims.
Claims
1. A radar-based adaptive polarization filtering method, characterized in that, include: Obtain discrete data from the main channel and discrete data from the auxiliary channel, wherein the polarization states of the main channel and the auxiliary channel are orthogonal. The discrete data of the main channel is processed with preset weight coefficients to obtain the polarization data of the main channel; Based on the discrete data of the auxiliary channel and the polarization data of the main channel, the cancellation weight coefficient of the auxiliary channel is obtained; Based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, polarization cancellation is performed on the polarization data of the main channel to obtain the cancelled main channel signal data.
2. The radar-based adaptive polarization filtering method according to claim 1, characterized in that, The acquisition of discrete data from the main channel and discrete data from the auxiliary channel includes: Collect raw signal data from the main channel and the auxiliary channel; The original signal data of the main channel and the original signal data of the auxiliary channel are respectively subjected to analog-to-digital conversion to obtain digital signal data; The digital signal data is down-converted and filtered to obtain the main channel discrete data and the auxiliary channel discrete data, respectively.
3. The radar-based adaptive polarization filtering method according to claim 1, characterized in that, The step of obtaining the cancellation weighting coefficients of the auxiliary channel based on the discrete data of the auxiliary channel and the polarization data of the main channel includes: Based on the discrete data of the auxiliary channel, the autocorrelation matrix of the auxiliary channel signal is obtained; Based on the discrete data of the auxiliary channel and the polarization data of the main channel, the cross-correlation matrix between the main channel signal and the auxiliary channel signal is obtained; The cancellation weighting coefficient of the auxiliary channel is obtained based on the autocorrelation matrix of the auxiliary channel signal and the cross-correlation matrix of the main channel signal and the auxiliary channel signal.
4. The radar-based adaptive polarization filtering method according to claim 3, characterized in that, The autocorrelation matrix R of the auxiliary channel signal HH for: R HH =H*H T Where H = [h1, h2, ..., h i ,…,h m ] T h represents the discrete data of the auxiliary channel. i This represents the discrete data for the i-th auxiliary channel.
5. The radar-based adaptive polarization filtering method according to claim 4, characterized in that, The cross-correlation matrix R between the main channel signal and the auxiliary channel signal HV for: Among them, V dbf =[v S ,v Y ,v Z ,v D ] T For the main channel polarization data, v S v Y v Z and v D These are polarization data for the four main channels. It is V dbf The conjugate transpose of .
6. The radar-based adaptive polarization filtering method according to claim 5, characterized in that, The cancellation weighting coefficient of the auxiliary channel is: W n =inv(R HH )*R HV Among them, W n The cancellation weighting coefficient for the auxiliary channel is inv(R). HH ) is R HH The inverse matrix.
7. The radar-based adaptive polarization filtering method according to claim 1, characterized in that, The step of performing polarization cancellation on the main channel polarization data based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel to obtain the cancelled main channel signal data includes: The maximum value of the cancellation weight coefficient is determined based on the cancellation weight coefficient of the auxiliary channel; Based on the maximum value of the cancellation weight coefficient, the cancellation weight coefficient of the auxiliary channel is normalized to obtain the normalized weight coefficient of the main channel. The polarization data of the main channel is normalized using the normalized weighting coefficient of the main channel to obtain the normalized polarization data of the main channel. Based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, polarization cancellation is performed on the normalized polarization data of the main channel to obtain the cancelled main channel signal data.
8. The radar-based adaptive polarization filtering method according to claim 7, characterized in that, The canceled main channel signal data is as follows: V O =w a V dbf -W n *H T Among them, w a V is the normalized weighting coefficient of the main channel. dbf =[v S ,v Y ,v Z ,v D ] T For the main channel polarization data, v S v Y v Z and v D These are the polarization data for the four main channels, W n H represents the cancellation weighting coefficient of the auxiliary channel, where H = [h1, h2, ..., h i ,…,h m ] T h represents the discrete data of the auxiliary channel. i This represents the discrete data for the i-th auxiliary channel.
9. The radar-based adaptive polarization filtering method according to claim 1, characterized in that, The cancellation weighting coefficient of the auxiliary channel is calculated based on the double-precision floating-point method of the FPGA chip.
10. A radar-based adaptive polarization filtering system, characterized in that, include: The acquisition module is used to acquire discrete data of the main channel and discrete data of the auxiliary channel, wherein the polarization states of the main channel and the auxiliary channel are orthogonal. The processing module is used to process the discrete data of the main channel with preset weight coefficients to obtain the polarization data of the main channel; The calculation module is used to obtain the cancellation weighting coefficient of the auxiliary channel based on the discrete data of the auxiliary channel and the polarization data of the main channel; The cancellation module is used to perform polarization cancellation on the polarization data of the main channel based on the cancellation weight coefficient of the auxiliary channel and the discrete data of the auxiliary channel, so as to obtain the cancelled main channel signal data.