A Multi-Target Sorting Method Based on Quadratic Fitting of Orientation and Parametric Overlap

Abstract

This invention provides a method for sorting multiple targets with overlapping parameters based on quadratic azimuth fitting. For full pulses with overlapping electromagnetic parameters and similar azimuths, the method first performs full pulse buffering to increase the number of full pulses and thus improve statistical effectiveness. Then, quadratic fitting is performed based on the azimuth of the full pulses, and the azimuth is flattened based on the fitted curve to remove the influence of azimuth changes caused by aircraft motion. Finally, histogram statistics are performed on the normalized azimuths, and target sorting is based on the histogram results, thereby meeting the sorting requirements for multiple targets with overlapping parameters.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and more specifically, to a method for sorting multiple targets based on quadratic azimuth fitting with overlapping parameters. Background Technology

[0002] Existing sorting processes are mainly based on changes in signal parameters, such as histogram methods and dynamic correlation algorithms. However, with the development of radar technology, modern radars use increasingly agile waveforms, leading to a decrease in the performance of traditional sorting methods or even their failure. Summary of the Invention

[0003] The present invention aims to provide a parameter overlap multi-target sorting method based on azimuth quadratic fitting, in order to solve the problem that the increasingly agile waveforms used in modern radars lead to a decrease in the performance or even failure of traditional sorting methods.

[0004] This invention provides a parameter overlap multi-target sorting method based on azimuth quadratic fitting, comprising the following steps:

[0005] Step 1: Buffer the input full pulse for a period of time, and perform histogram sorting on the buffered full pulse to achieve target clustering that can be distinguished by parameters or orientation.

[0006] Step 2: For each clustering result, a quadratic curve fitting algorithm is used to obtain the time-azimuth curve;

[0007] Step 3: Flatten the time-azimuth curve;

[0008] Step 4: Perform histogram statistics on the flattened time-azimuth curve;

[0009] Step 5: Search for peak values ​​in the histogram statistics;

[0010] Step 6: Based on the search results of the peak values, perform full pulse screening to obtain the multi-target sorting results with overlapping parameters.

[0011] Furthermore, step 2 specifically involves:

[0012] Let the arrival time of each full pulse be TOA. i (i = 1, 2, ..., N), with orientation DOA i (i = 1, 2, ..., N), where N represents the total number of pulses;

[0013] The time-azimuth curve is represented as The positions of all full pulses are combined into a column vector DOA. vec =[DOA1,DOA2,…,DOA] N ] TThe arrival times of all full pulses are combined into a column vector TOA. vec =[TOA1,TOA2,…,TOA N ] T ,in[·] T Indicates transpose;

[0014] The estimated values ​​of A, B, and C are obtained through a quadratic curve fitting algorithm:

[0015]

[0016] Where ε is a very small positive number, and eye(3) represents a diagonal matrix of order 3.

[0017] Furthermore, the flattened time-azimuth curve is represented as follows:

[0018]

[0019] At this point, the time-azimuth curve after flattening is... It appears as a flat straight line.

[0020] Furthermore, in step 4, based on the azimuth measurement resolution δ, the flattened time-azimuth curve is... Perform histogram analysis. Specifically:

[0021] The flattened time-azimuth curve minimum value To the maximum value Histogram intervals are divided according to an interval δ; assuming there are M histogram intervals in total, the range of the m-th histogram interval is... Traversal If If a value falls within a given histogram interval, the histogram value for that interval is incremented by 1, thus obtaining the histogram statistical result (Hist). m (m = 1, 2, ..., M).

[0022] Furthermore, in step 5, the formula for calculating the peak value of the histogram statistical results is as follows:

[0023] Hist m >Hist m-1 &Hist m >Hist m-2 &Hist m ≥Hist m+1 &Hist m ≥Hist m+2 &Hist m ≥Thres

[0024] Thres is the set threshold.

[0025] Furthermore, step 6 specifically involves:

[0026] Suppose there are N peaks in total, and the position of the nth (n = 1, 2, ..., N) peak is Peak(n), then the sorting result corresponds to... The range of values ​​is:

[0027] Based on this index, the original full pulse is used to achieve multi-target sorting results with overlapping parameters.

[0028] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0029] To improve the sorting performance of multiple targets with overlapping parameters, this invention provides a method for sorting multiple targets with overlapping parameters based on quadratic azimuth fitting. For full pulses with overlapping electromagnetic parameters and similar azimuths, full pulse buffering is first performed to increase the number of full pulses and thus improve the effectiveness of the statistics. Then, quadratic fitting is performed based on the azimuth of the full pulses, and the azimuth is flattened based on the fitted curve to remove the influence of azimuth changes caused by aircraft movement. Finally, histogram statistics are performed on the normalized azimuths, and target sorting is performed based on the histogram results, thereby meeting the sorting requirements of multiple targets with overlapping parameters. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of a multi-target sorting method based on quadratic fitting of orientation parameters in an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0033] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0034] Example

[0035] like Figure 1 As shown in the figure, this embodiment proposes a parameter overlap multi-target sorting method based on azimuth quadratic fitting, including the following steps:

[0036] Step 1: The input full pulse (PDW) is buffered for 100 seconds to increase the amount of data and improve statistical performance. The buffered full pulses are then sorted by histogram to achieve target clustering that can be distinguished by parameters or orientation. However, within the same clustering result, there may be dense targets with overlapping parameters.

[0037] Step 2: For each clustering result in Step 1, a quadratic curve fitting algorithm is used to obtain the time-azimuth curve; specifically:

[0038] Let the arrival time of each full pulse be TOA. i (i = 1, 2, ..., N), with orientation DOA i (i = 1, 2, ..., N), where N represents the total number of pulses;

[0039] The time-azimuth curve is represented as The positions of all full pulses are combined into a column vector DOA. vec =[DOA1,DOA2,…,DOA] N ] T The arrival times of all full pulses are combined into a column vector TOA. vec =[TOA1,TOA2,…,TOA N ] T ,in[·] T Indicates transpose;

[0040] The estimated values ​​of A, B, and C are obtained through a quadratic curve fitting algorithm:

[0041]

[0042] Here, ε is a very small positive number, usually taken as ε = 10. -4 Or ε = 10 -5 Or ε = 10 -6 eye(3) represents a diagonal matrix of order 3.

[0043] Step 3: Flatten the time-azimuth curve; the flattened time-azimuth curve is shown as follows:

[0044]

[0045] At this point, the time-azimuth curve after flattening is... It appears as a flat straight line.

[0046] Step 4: Based on the azimuth measurement resolution δ, analyze the flattened time-azimuth curve. Perform histogram statistics; specifically:

[0047] The flattened time-azimuth curve minimum value To the maximum value Histogram intervals are divided according to an interval δ; assuming there are M histogram intervals in total, the range of the m-th histogram interval is... Traversal If If a value falls within a given histogram interval, the histogram value for that interval is incremented by 1, thus obtaining the histogram statistical result (Hist). m (m = 1, 2, ..., M).

[0048] Step 5: Search for peak values ​​in the histogram statistics. The calculation formula is as follows:

[0049] Hist m >Hist m-1 &Hist m >Hist m-2 &Hist m ≥Hist m+1 &Hist m ≥Hist m+2 &Hist m ≥Thres

[0050] Thres is the set threshold.

[0051] Step 6: Based on the search results for peak values, perform full-pulse filtering to obtain the multi-target sorting results with overlapping parameters. Let there be N peak values ​​in total, and let the position of the nth (n = 1, 2, ..., N) peak be Peak(n). Then the sorting result corresponds to... The range of values ​​is:

[0052] Based on this index, the original full pulse is used to achieve multi-target sorting results with overlapping parameters.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-target sorting method based on quadratic orientation fitting with overlapping parameters, characterized in that, Includes the following steps: Step 1: Buffer the input full pulse for a period of time, and perform histogram sorting on the buffered full pulse to achieve target clustering that can be distinguished by parameters or orientation. Step 2: For each clustering result, a quadratic curve fitting algorithm is used to obtain the time-azimuth curve; Step 3: Flatten the time-azimuth curve; Step 4: Perform histogram statistics on the flattened time-azimuth curve; Step 5: Search for peak values ​​in the histogram statistics; Step 6: Based on the search results of the peak values, perform full pulse screening to obtain the multi-target sorting results with overlapping parameters; Step 2 is as follows: Let the arrival time of each full pulse be... The direction is ,in, N Indicates the total number of pulses; The time-azimuth curve is represented as The positions of all full pulses are arranged into a column vector. The arrival times of all full pulses are arranged into a column vector. ,in Indicates transpose; Obtained through quadratic curve fitting algorithm A , B , C The estimated value is: in, It is a very small positive number. This represents a diagonal matrix of order 3; The time-azimuth curve after flattening is expressed as follows: At this point, the time-azimuth curve after flattening is... It appears as a flat straight line.

2. The multi-target sorting method based on quadratic azimuth fitting with overlapping parameters according to claim 1, characterized in that, or or .

3. The multi-target sorting method based on quadratic orientation fitting with overlapping parameters according to claim 1, characterized in that, In step 4, based on the azimuth measurement resolution The time-azimuth curve after flattening Perform histogram statistics.

4. The multi-target sorting method based on quadratic azimuth fitting with overlapping parameters according to claim 3, characterized in that, Step 4 is as follows: The flattened time-azimuth curve minimum value To the maximum value According to interval Perform histogram interval segmentation; assuming a total of Then the nth histogram interval, The range of each histogram interval is ; Traversal If If a value falls within a given histogram interval, the histogram value for that interval is incremented by 1, thus obtaining the histogram statistical result. .

5. The multi-target sorting method based on quadratic azimuth fitting with overlapping parameters according to claim 4, characterized in that, In step 5, the formula for calculating the peak value of the histogram statistical results is as follows: in, It is a set threshold.

6. The parameter overlap multi-target sorting method based on azimuth quadratic fitting according to claim 5, characterized in that, Step 6 specifically involves: Assume there is a total The peak, and the first The peak positions are The sorting result corresponds to The range of values ​​is: Based on this index, the original full pulse is used to achieve the sorting results of multiple targets with overlapping parameters.

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