CN107290724B - High-dynamic signal parameter estimation method based on delay correlation function - Google Patents
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Abstract
The invention aims to provide a parameter estimation method based on a delay correlation function, which utilizes KT transformation to eliminate residual distance migration after delay correlation; in the iteration process, a high-order dynamic parameter estimation result is utilized, and a frequency domain compensation mode is adopted to simultaneously compensate the range migration and the Doppler frequency migration caused by high-order dynamics; by designing delay variables related to delay, the parameter estimation precision is improved. The method is suitable for estimating the dynamic parameters of the radar signals.
Description
Technical Field
The invention relates to a parameter estimation method of a dynamic signal, which is particularly suitable for signal parameter estimation under a low carrier-to-noise ratio and high dynamic environment and belongs to the field of radar signal processing.
Background
The dynamic parameter estimation precision of the moving target influences the radar moving target detection and high-resolution radar imaging effects. In order to improve the parameter estimation accuracy of the target, the signal-to-noise ratio is usually improved by means of coherent accumulation. Within the coherent accumulation time, the high dynamic of the target motion can cause the range migration and Doppler frequency migration of radar echo signals, thereby causing serious signal-to-noise ratio loss and influencing the parameter estimation performance.
The parameter estimation method based on Generalized Radon Fourier Transform (GRFT) can eliminate the influence of range migration and Doppler frequency migration through parameter multidimensional joint search, but the calculation amount is too large, so that the parameter estimation method is not suitable for parameter estimation under a multi-order dynamic model; the methods such as Polynomial Phase Transformation (PPT), integral generalized fuzzy function (IGAF) and the like convert parameter multidimensional joint search into a plurality of one-dimensional searches through iterative delay correlation processing, the computation amount is greatly reduced, but the methods only correct Doppler frequency migration and cannot correct the influence of range migration, so the methods need to be used together with a range migration correction method. The Keystone transformation (Keystone transformation) can realize blind correction of range migration by using a sampling rate changing mode, but only can correct dynamic influence of a certain order, and is not applicable to a multi-order dynamic model. In the article "A FastManeuverting TargetMotionParameters EstimationAlgorithm Based on ACCF" published by Xiaoolong Li et al, IEEE Signal Processing Letters 22 volume 3, pages 270 to 274, a parameter estimation method Based on the adjacent delay correlation function (ACCF) was proposed. The method realizes dynamic joint reduction of distance dimension and frequency dimension by delaying relevant processing without performing multidimensional joint search of parameters. But the method does not compensate the residual range migration after delay correlation when estimating the dynamic parameters; and when the low-order dynamic parameters are estimated in a recursive mode, only the Doppler frequency migration of the high-order dynamics is corrected by using the estimation result of the high-order dynamic parameters, but the range migration of the high-order dynamics is not corrected.
The influence of residual distance migration after delay correlation is not considered in the processing of the method, so that the size of the dynamic parameter is restricted. And the method adopts a processing mode related to adjacent delay, so that the delay time is short, and the estimation precision of the dynamic parameters is low. Therefore, in order to eliminate the constraint on the dynamic parameters and improve the estimation accuracy of the ACCF, the method needs to be improved, and the content of the invention is not reported at home and abroad.
Disclosure of Invention
The invention aims to provide a high dynamic signal parameter estimation method based on a delay correlation function, which utilizes Keystone transformation to eliminate residual distance migration after delay correlation; in the iteration process, a high-order dynamic parameter estimation result is utilized, and a frequency domain compensation mode is adopted to simultaneously compensate the range migration and the Doppler frequency migration caused by high-order dynamics; by designing delay variables related to delay, the parameter estimation precision is improved. The method is suitable for dynamic parameter estimation of radar signals.
The invention is realized by the following technical scheme:
a high dynamic signal parameter estimation method based on a delay correlation function comprises the following steps:
(1) the logic control module sets an initial value of a dynamic order k to be N-1, wherein N is the highest dynamic order, and the compensation function construction module sets an initial value of a compensation function to be 1;
(2) sampling a received signal to construct a received signal matrix;
(3) after receiving the received signal matrix, the delay correlation module calculates each order of delay correlation function and outputs the delay correlation function to the fast time dimension Fourier transform module;
(4) the fast time dimension Fourier transform module receives the delay correlation function, carries out fast time dimension Fourier transform on the delay correlation function to obtain a fast time dimension frequency domain value matrix, outputs the matrix to the frequency domain value matrix storage module, and the frequency domain value matrix storage module stores the fast time dimension frequency domain value matrix of each order of delay correlation function;
(5) the logic control module outputs the dynamic order k to the frequency domain value matrix storage module and the compensation function construction module;
(6) the frequency domain value matrix storage module outputs a k-order delay correlation function to the dynamic compensation module according to the dynamic order k output by the logic control module;
(7) the compensation function construction module outputs a compensation function to the dynamic compensation module;
(8) the dynamic compensation module receives the compensation function and the k-order delay correlation function, multiplies corresponding elements of the two matrixes to obtain a modified frequency domain value matrix, and outputs the modified frequency domain value matrix to the Keystone conversion module;
(9) the Keystone conversion module receives the modified frequency-domain value matrix and generates a frequency-domain variable f in each frequency domainτPerforming Keystone transformation under the value of the adjacent cross-correlation function to obtain a modified adjacent cross-correlation function fast time dimension frequency domain matrix, and outputting the matrix to a fast time dimension inverse Fourier transform module;
(10) the fast time dimension inverse Fourier transform module receives the corrected fast time dimension frequency domain matrix of the adjacent cross-correlation function, carries out fast time dimension inverse Fourier transform on the matrix and outputs a time domain value matrix after residual distance migration correction;
(11) the slow time dimension Fourier transform module receives the time domain value matrix after the residual distance migration correction, performs slow time dimension Fourier transform on the matrix, and outputs the modified adjacent cross-correlation function slow time dimension frequency domain matrix to the parameter estimation module;
(12) the parameter estimation module receives the corrected adjacent cross-correlation function slow time dimension frequency domain matrix, selects the position of the maximum value of the absolute value of the matrix, and calculates the corresponding fast time delay and slow time frequency domain value to obtain
Is an estimated value of 0, 1 dynamic parameter of the k-order correlation function and is obtained according to the estimated value and a round of iterationk,2，bk,3，…，bk,N-kTo obtain an estimated value of bk-1,2，bk-1,3，…，bk-1,N-k+1(ii) a After parameter estimation is finished, the parameter estimation module sends a trigger pulse signal, outputs the trigger pulse signal to the logic control module and outputs a parameter estimation result to the compensation function construction module;
(13) after the logic control module detects the trigger pulse signal, making k equal to k-1, and judging the value of k, and if k equal to 0, jumping to step (15); otherwise, outputting k to a frequency domain value matrix storage module and a compensation function construction module;
(14) after receiving the parameter estimation result, the compensation function construction module reconstructs the compensation function and skips to the step (5);
(15) and the logic control module outputs the result of the estimated parameters and ends the parameter estimation.
The method for estimating the radar signal parameters under the low signal-to-noise ratio and high dynamic scene has the following beneficial effects:
1. the method for estimating the radar signal parameters based on the delay correlation can solve the problem of high-order distance dimension migration without multi-dimensional parameter search, so compared with the GRFT and other methods, the method has the advantages of low computation amount, small memory amount and high real-time property.
2. The invention uses the ACCF method for reference to carry out delay correlation processing, and can realize the combined correction of distance dimension and frequency dimension dynamic migration compared with the PPT method, the IGAF method and the like.
3. The invention is improved on the basis of the ACCF method: eliminating residual range migration after delay correlation by using Keystone transformation; in the iteration process, a high-order dynamic parameter estimation result is utilized, and a frequency domain compensation mode is adopted to simultaneously compensate the range migration and the Doppler frequency migration caused by high-order dynamics; by designing delay variables related to delay, the parameter estimation precision is improved. Through the processing, the limitation of the distance migration quantity parameter caused by the limitation of high-order dynamics in the ACCF method is eliminated, and the dynamic parameter estimation resolution is improved.
4. Compared with the ACCF method, the method has better distance dimension focusing performance and better noise resistance, and is more suitable for parameter estimation of radar signals in low signal-to-noise ratio and high dynamic scenes.
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FIG. 1 is a block diagram of a method for estimating parameters of a high dynamic signal based on a delay correlation function according to the present invention;
Detailed Description
Firstly, the principle of the parameter estimation method based on the delay correlation function is as follows:
under multi-order dynamics, the received baseband signal can be expressed as
Wherein,
Tpis the pulse width, fcFor the carrier frequency, gamma is the frequency modulation rate, TrFor pulse repetition interval, tm＝mTrIs a slow time, ts＝t-tmFor fast time, N is the highest order dynamic order. Distance R (T)k) Comprises the following steps:
wherein, a0Is the initial distance between the radar and the target, a1，a2，…，aNAnd respectively carrying out target dynamic parameters of each order.
Calculating a first order delay correlation function R of a received signal1
Wherein,
τ1for fast time dimension delay variables, q1Is a slow time dimension delay variable.
k order delay correlation function Rk(τk,tm；qk) Can be prepared from Rk-1(τk-1,tm；qk-1) To obtain
Wherein, taukFor fast time-dimensional delay variables of order k, qkIs a slow time dimension delay variable of order k. bk,iIs the ith dynamic parameter of the k-order correlation function and has
The delay correlation function of the N-1 th order is
Elimination of R by Keystone transformN-1(τN-1,tm；qN-1) Residual range migration
Where KT denotes the Keystone transform and FT denotes the fourier transform.
It can be seen that the position of the envelope peak after Keystone transformation is tauN-1＝-2bN-1,0C and tm'Irrelevance, namely Keystone transformation, eliminates the influence of residual range migration after delay correlation.
Further, R is paired in the slow time dimensionN-1(τN-1,tm'；qN-1) Fourier transform to obtain
The position of the envelope peak is (tau)N-1,fm')＝(-2bN-1,0/c,-2fcbN-1,1C), i.e. the estimated value is obtained by peak search
And
further, the estimated value can be obtained by the equation (6)
And
using estimated values
Constructing a compensation function
Wherein f isτIs a fast time dimension frequency domain value.
Delay correlation function R of order N-2N-2(τN-2,tm') Performing fast time dimension Fourier transform with compensation function H (f)τ,tm) After multiplication, fast time dimension inverse Fourier transform operation is carried out to obtain the corrected adjacent cross-correlation function
Through the processing, a compensation function is constructed by utilizing a high-order dynamic estimation result, the low-order delay correlation function is compensated, and the dynamic order of the low-order delay correlation function can be reduced. Furthermore, Keystone transform and Fourier transform operation (refer to formulas (8) to (9)) are carried out on the compensated result, energy can be jointly focused in a distance dimension and a Doppler dimension, and an N-2 order parameter estimation result is obtained.
Through the recursion processing for N-1 times, the estimation of the dynamic parameters from high order to low order can be realized.
It should be noted that the delay variable q is a slow time dimensionkGreater than or equal to 1, if the sample length of the sampling signal is fixed, qkThe larger the sample point number after the correlation is, the smaller the sample point number; if the sample length of the sampling signal is not limited, qkThe larger the required sample length of the sampled signal, the more the total calculation is, and therefore a compromise is made to select qkThe value of (c).
The structure block diagram of the invention is shown in figure 1, and comprises a received signal matrix module, a delay correlation module, a fast time dimension Fourier transform module, a frequency domain value matrix storage module, a logic control module, a compensation function construction module, a dynamic compensation module, a Keystone transform module, a fast time dimension Fourier transform module, a slow time dimension Fourier transform and a parameter estimation module.
The following examples describe the specific steps of the present invention as follows:
(1) the logic control module sets the initial value of the dynamic order k to be N-1, and the compensation function building module sets the initial value of the compensation function to be H (f)τ,tm)＝1。
(2) And sampling the received signals to construct a received signal matrix.
(3) Delay correlationAfter the module receives the received signal matrix, the module calculates delay correlation function R of each order according to formula (3) and formula (5)1(τ1,tm；q1)、R2(τ2,tm；q2)、…、RN-1(τN-1,tm；qN-1) And outputting the data to a fast time dimension Fourier transform module.
(4) The fast time dimension Fourier transform module receives the delay correlation function and carries out fast time dimension Fourier transform on the delay correlation function to obtain R1(fτ,tm)、R2(fτ,tm)，…、RN-1(fτ,tm) And outputting the data to a frequency domain value matrix storage module.
(5) And the frequency domain value matrix storage module stores the frequency domain value matrix of the fast time dimension of each order of delay correlation function.
(6) And the logic control module outputs the dynamic order k to the frequency domain value matrix storage module and the compensation function construction module.
(7) The frequency domain value matrix storage module outputs a k-order delay correlation function R according to the dynamic order k output by the logic control modulek(fτ,tm) To the dynamic compensation module.
(8) The compensation function construction module outputs a compensation function H (f)τ,tm) To the dynamic compensation module.
(9) The dynamic compensation module receives H (f)τ,tm) And Rk(fτ,tm) Multiplying corresponding elements of the two matrixes to obtain a corrected frequency domain value matrix
And outputting the data to a Keystone transformation module.
(10) Keystone conversion module receiving matrix
At each fτPerforming Keystone transformation under the value of (a) to obtain a matrix
And outputting the data to a fast time dimension inverse Fourier transform module.
(11) Fast time dimension inverse Fourier transform module receiving matrix
Inverse Fourier transform is carried out on the fast time dimension of the matrix, and a time domain value matrix after residual distance migration correction is output
(12) Receiving matrix of slow time dimension Fourier transform module
Fourier transform is carried out on the matrix in the slow time dimension, and a matrix is output
To the parameter estimation module.
(13) Parameter estimation module receiving matrix
Selecting
Position (X) of maximumindex，Yindex) Calculating its corresponding fast time delay taukAnd a slow time-frequency domain value fm'To obtain bk,0，bk,1Is calculated by the formula
(τk,fm')＝(-2bk,0/c,-2fcbk,1/c) (12)
Using b obtained herek,0，bk,1And b obtained in the previous iterationk,2，bk,3，…，bk,N-kFurther obtaining b from the formula (6)k-1,2，bk-1,3，…，bk-1,N-k+1And | represents a modulo operation. After the parameter estimation is completed, the parameter estimation module sends out a trigger pulse signal flagk. Parameter estimationThe meter module outputs the trigger pulse signal to the logic control module and outputs the parameter estimation result to the compensation function construction module.
(14) After detecting the trigger pulse signal flagk, the logic control module makes k equal to k-1, judges the value of k, and jumps to step (16) if k equal to 0; otherwise, outputting the k to a frequency domain value matrix storage module and a compensation function construction module.
(15) After receiving the parameter estimation result, the compensation function construction module reconstructs the compensation function H (f)τ,tm)。H(fτ,tm) Can be expressed as
And (6) jumping to the step.
(16) And the logic control module outputs the result of the estimated parameters and ends the parameter estimation.
Claims (1)
1. A high dynamic signal parameter estimation method based on a delay correlation function is characterized by comprising the following steps:
(1) the logic control module sets an initial value of a dynamic order k to be N-1, wherein N is the highest dynamic order, and the compensation function construction module sets an initial value of a compensation function to be 1;
(2) sampling a received signal to construct a received signal matrix;
(3) after receiving the received signal matrix, the delay correlation module calculates each order of delay correlation function and outputs the delay correlation function to the fast time dimension Fourier transform module;
(4) the fast time dimension Fourier transform module receives the delay correlation function, carries out fast time dimension Fourier transform on the delay correlation function to obtain a fast time dimension frequency domain value matrix, outputs the matrix to the frequency domain value matrix storage module, and the frequency domain value matrix storage module stores the fast time dimension frequency domain value matrix of each order of delay correlation function;
(5) the logic control module outputs the dynamic order k to the frequency domain value matrix storage module and the compensation function construction module;
(6) the frequency domain value matrix storage module outputs a k-order delay correlation function fast time dimension frequency domain value matrix to the dynamic compensation module according to the dynamic order k output by the logic control module;
(7) the compensation function construction module outputs a compensation function to the dynamic compensation module;
(8) the dynamic compensation module receives the compensation function and the fast time dimension frequency domain value matrix of the k-order delay correlation function, multiplies corresponding elements of the two matrixes to obtain a modified frequency domain value matrix, and outputs the modified frequency domain value matrix to the Keystone conversion module;
(9) the Keystone conversion module receives the modified frequency-domain value matrix and generates a frequency-domain variable f in each frequency domainτPerforming Keystone transformation under the value of the adjacent cross-correlation function to obtain a modified adjacent cross-correlation function fast time dimension frequency domain matrix, and outputting the matrix to a fast time dimension inverse Fourier transform module;
(10) the fast time dimension inverse Fourier transform module receives the corrected fast time dimension frequency domain matrix of the adjacent cross-correlation function, carries out fast time dimension inverse Fourier transform on the matrix and outputs a time domain value matrix after residual distance migration correction;
(11) the slow time dimension Fourier transform module receives the time domain value matrix after the residual distance migration correction, performs slow time dimension Fourier transform on the matrix, and outputs the modified adjacent cross-correlation function slow time dimension frequency domain matrix to the parameter estimation module;
(12) the parameter estimation module receives the corrected adjacent cross-correlation function slow time dimension frequency domain matrix, selects the position of the maximum value of the absolute value of the matrix, and calculates the corresponding fast time delay and slow time frequency domain value to obtain
Is an estimated value of 0, 1 dynamic parameter of the k-order correlation function and is obtained according to the estimated value and a round of iterationk,2，bk,3，…，bk,N-kTo obtain an estimated value of bk-1,2，bk-1,3，…，bk-1,N-k+1(ii) a After parameter estimation is finished, the parameter estimation module sends out a trigger pulse signal, outputs the trigger pulse signal to the logic control module and sends the parameter to the logic control moduleThe number estimation result is output to a compensation function construction module;
(13) after the logic control module detects the trigger pulse signal, making k equal to k-1, and judging the value of k, and if k equal to 0, jumping to step (15); otherwise, outputting k to a frequency domain value matrix storage module and a compensation function construction module;
(14) after receiving the parameter estimation result, the compensation function construction module reconstructs the compensation function and skips to the step (5);
(15) and the logic control module outputs the result of the estimated parameters and ends the parameter estimation.
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