CN117493917A - Parameter overlapping multi-target sorting method based on azimuth quadratic fitting - Google Patents
Parameter overlapping multi-target sorting method based on azimuth quadratic fitting Download PDFInfo
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Abstract
The invention provides a parameter overlapping multi-target sorting method based on azimuth quadratic fitting, which aims at full pulse with electromagnetic parameter overlapping and azimuth similar, firstly carries out full pulse buffer storage processing, and improves statistical effectiveness by improving the number of full pulses. And then carrying out secondary fitting based on the omnidirectional pulse, and carrying out azimuth leveling processing on the omnidirectional pulse based on a fitting curve, so as to remove the influence of azimuth change caused by carrier movement. And finally, carrying out histogram statistics on the normalized azimuth, and carrying out target sorting according to the histogram statistics result, thereby meeting the sorting requirement of the parameter overlapping multi-target.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a parameter overlapping multi-target sorting method based on azimuth quadratic fitting.
Background
Existing sorting processes are based primarily on parameter variations of the signal, such as histogram methods, dynamic correlation algorithms. However, with the development of radar technology, modern radars adopt ever-changing waveforms, which results in reduced performance and even failure of traditional sorting methods.
Disclosure of Invention
The invention aims to provide a parameter overlapping multi-target sorting method based on azimuth quadratic fitting, which aims to solve the problem that the performance of the traditional sorting method is reduced or even fails due to the fact that a modern radar adopts a guaigible waveform.
The invention provides a parameter overlapping multi-target sorting method based on azimuth quadratic fitting, which comprises the following steps:
step 1, caching the input full pulse for a period of time, and performing histogram sorting treatment on the cached full pulse to realize target clustering with distinguishable parameters or orientations;
step 2, obtaining a time-azimuth curve by adopting a quadratic curve fitting algorithm for each clustering result;
step 3, leveling the time-azimuth curve;
step 4, carrying out histogram statistics on the time-azimuth curve after the leveling treatment;
step 5, searching peak values for the statistical results of the histogram;
and 6, carrying out full pulse screening based on the searching result of the peak value to obtain a parameter overlapping multi-target sorting result.
Further, the step 2 specifically includes:
let the arrival time of each full pulse be TOA i (i=1, 2, …, N) orientation is DOA i (i=1, 2, …, N), where N represents the total number of full pulses;
representing the time-azimuth curve asAll the full pulse directions are combined into one column vector DOA vec ＝[DOA 1 ,DOA 2 ,…,DOA N ] T Make up the arrival time of all full pulses into oneColumn vector TOA vec ＝[TOA 1 ,TOA 2 ,…,TOA N ] T Wherein [] T Representing a transpose;
the estimated value of A, B, C obtained by the quadratic curve fitting algorithm is:
where ε is a small positive number and eye (3) represents a diagonal array of order 3.
Further, the time-azimuth curve after the leveling process is expressed as:
time-azimuth curve after leveling treatment at this timeWhich appears as a straight line.
Further, in step 4, the time-azimuth curve after the leveling process is plotted based on the azimuth measurement resolution δAnd carrying out histogram statistics. The method comprises the following steps:
time-azimuth curve after leveling treatmentMinimum value +.>To maximum->Dividing a histogram interval according to the interval delta; assuming that there are M histogram bins in total, the range of the mth histogram bin isWalk->Treatment, if->If the histogram falls into the histogram interval, the histogram value of the histogram interval is added with 1, so as to obtain a histogram statistical result Hist m ,(m＝1,2,…,M)。
Further, in step 5, the calculation formula for searching the peak value for the histogram statistical result is:
Hist m >Hist m-1 &Hist m >Hist m-2 &Hist m ≥Hist m+1 &Hist m ≥Hist m+2 &Hist m ≥Thres
where Thres is the threshold set.
Further, the step 6 specifically includes:
let N peaks in total and N (n=1, 2, …, N) Peak positions be Peak (N), the sorting result corresponds toThe range of the value range is as follows:
and the original full pulse is indexed based on the index, so that a parameter overlapping multi-target sorting result is realized.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
in order to improve the sorting performance of the parameter overlapping multi-target, the invention provides a parameter overlapping multi-target sorting method based on azimuth quadratic fitting. Aiming at full pulses with overlapping electromagnetic parameters and similar orientations, full pulse caching is firstly carried out, and statistical effectiveness is improved by improving the number of full pulses. And then carrying out secondary fitting based on the omnidirectional pulse, and carrying out azimuth leveling processing on the omnidirectional pulse based on a fitting curve, so as to remove the influence of azimuth change caused by carrier movement. And finally, carrying out histogram statistics on the normalized azimuth, and carrying out target sorting according to the histogram statistics result, thereby meeting the sorting requirement of the parameter overlapping multi-target.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly describe the drawings in the embodiments, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for sorting multiple targets by overlapping parameters based on azimuth quadratic fitting in an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 1, this embodiment proposes a parameter overlapping multi-objective sorting method based on azimuth quadratic fitting, which includes the following steps:
step 1, caching input full Pulses (PDWs) for 100 seconds, improving statistical performance by increasing data quantity, and performing histogram sorting processing on the cached full pulses to realize target clustering with distinguishable parameters or orientations; but within the same clustering result there may be dense targets of parameter overlap.
Step 2, obtaining a time-azimuth curve by adopting a quadratic curve fitting algorithm for each clustering result in the step 1; the method comprises the following steps:
let the arrival time of each full pulse be TOA i (i=1, 2, …, N) orientation is DOA i (i=1, 2, …, N), where N represents the total number of full pulses;
representing the time-azimuth curve asAll the full pulse directions are combined into one column vector DOA vec ＝[DOA 1 ,DOA 2 ,…,DOA N ] T The arrival time of all full pulses is formed into a column vector TOA vec ＝[TOA 1 ,TOA 2 ,…,TOA N ] T Wherein [] T Representing a transpose;
the estimated value of A, B, C obtained by the quadratic curve fitting algorithm is:
where epsilon is a small positive number, usually epsilon=10 is preferable -4 Or epsilon=10 -5 Or epsilon=10 -6 Eye (3) represents a diagonal array of order 3.
Step 3, leveling the time-azimuth curve; the time-azimuth curve after the leveling process is expressed as:
time-azimuth curve after leveling treatment at this timeWhich appears as a straight line.
Step 4, based on azimuth measurement resolution delta, leveling the time-azimuth curve after the processingCarrying out histogram statistics; the method comprises the following steps:
time-azimuth curve after leveling treatmentMinimum value +.>To maximum->Dividing a histogram interval according to the interval delta; assuming that there are M histogram bins in total, the range of the mth histogram bin isWalk->Treatment, if->If the histogram falls into the histogram interval, the histogram value of the histogram interval is added with 1, so as to obtain a histogram statistical result Hist m ,(m＝1,2,…,M)。
Step 5, searching peak values for the statistical result of the histogram, wherein the calculation formula is as follows:
Hist m >Hist m-1 &Hist m >Hist m-2 &Hist m ≥Hist m+1 &Hist m ≥Hist m+2 &Hist m ≥Thres
where Thres is the threshold set.
Step 6, based on the searching result of the peak value, carrying out full pulse screening to obtain the ginsengThe numbers overlap the multi-target sorting results. Let N peaks in total and N (n=1, 2, …, N) Peak positions be Peak (N), the sorting result corresponds toThe range of the value range is as follows:
and the original full pulse is indexed based on the index, so that a parameter overlapping multi-target sorting result is realized.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The parameter overlapping multi-target sorting method based on azimuth quadratic fitting is characterized by comprising the following steps of:
step 1, caching the input full pulse for a period of time, and performing histogram sorting treatment on the cached full pulse to realize target clustering with distinguishable parameters or orientations;
step 2, obtaining a time-azimuth curve by adopting a quadratic curve fitting algorithm for each clustering result;
step 3, leveling the time-azimuth curve;
step 4, carrying out histogram statistics on the time-azimuth curve after the leveling treatment;
step 5, searching peak values for the statistical results of the histogram;
and 6, carrying out full pulse screening based on the searching result of the peak value to obtain a parameter overlapping multi-target sorting result.
2. The method for sorting the multiple targets by overlapping parameters based on azimuth quadratic fitting according to claim 1, wherein the step 2 is specifically:
let the arrival time of each full pulse be TOA i (i=1, 2, …, N) orientation is DOA i (i=1, 2, …, N), where N represents the total number of full pulses;
representing the time-azimuth curve asAll the full pulse directions are combined into one column vector DOA vec ＝[DOA 1 ,DOA 2 ,…,DOA N ] T The arrival time of all full pulses is formed into a column vector TOA vec ＝[TOA 1 ,TOA 2 ,…,TOA N ] T Wherein [] T Representing a transpose;
the estimated value of A, B, C obtained by the quadratic curve fitting algorithm is:
where ε is a small positive number and eye (3) represents a diagonal array of order 3.
3. The method for sorting multiple targets by overlapping parameters based on azimuthal quadratic fitting according to claim 2, wherein ε=10 -4 Or epsilon=10 -5 Or epsilon=10 -6 。
4. A method of multi-objective sorting based on parametric overlap of azimuthal quadratic fitting according to claim 3, wherein the time-azimuthal plot after the flattening process is expressed as:
time-azimuth curve after leveling treatment at this timeWhich appears as a straight line.
5. The method for sorting multiple targets by parameter overlap based on azimuth quadratic fit according to claim 4, wherein in step 4, the time-azimuth curve after leveling is processed based on azimuth measurement resolution δAnd carrying out histogram statistics.
6. The method for sorting multiple targets by overlapping parameters based on azimuth quadratic fitting according to claim 5, wherein step 4 is specifically:
time-azimuth curve after leveling treatmentMinimum value +.>To maximum->Dividing a histogram interval according to the interval delta; assuming that there are M histogram bins in total, the range of the mth histogram bin isWalk->Treatment, if->If the histogram falls into the histogram interval, the histogram value of the histogram interval is added with 1, so as to obtain a histogram statistical result Hist m ,(m＝1,2,…,M)。
7. The method for sorting multiple targets by overlapping parameters based on azimuth quadratic fit according to claim 6, wherein in step 5, the calculation formula for searching peak values for histogram statistics is:
Hist m >Hist m-1 &Hist m >Hist m-2 &Hist m ≥Hist m+1 &Hist m ≥Hist m+2 &Hist m ≥Thres
where Thres is the threshold set.
8. The method for sorting multiple targets by overlapping parameters based on azimuth quadratic fitting according to claim 7, wherein step 6 is specifically:
let N peaks in total and N (n=1, 2, …, N) Peak positions be Peak (N), the sorting result corresponds toThe range of the value range is as follows:
and the original full pulse is indexed based on the index, so that a parameter overlapping multi-target sorting result is realized.
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