Approximately optimal radar target detection method against K-distributed clutter plus noise

A radar target and detection method technology, which is applied to radio wave measurement systems, instruments, etc., can solve problems such as poor detection performance, complex detector expressions, and reduced detection performance, and achieve the effect of improving detection performance

Active Publication Date: 2017-03-01

XIDIAN UNIV

4 Cites 18 Cited by

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Problems solved by technology

Literature. F.Gini, "Suboptimal coherent radar detection in a mixture of K-distributed and Gaussian clutter," IEE Proc.-Radar, Sonar, Navig., 144(1):39-47, 1997. and literature F.Gini , M.V.Greco, A.Farina, P.Lombardo, "Optimum and mismatched detection against K-distributed clutter plus Gaussian clutter," IEEE Trans.Aerospace Electron.Systems 34(3):860-876, 1998. This model is discussed in the context of The optimal detector is the optimal K distribution plus Gaussian white noise detector OKGD, but because the d...

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Abstract

The invention discloses an approximately optimal radar target detection method against K-distributed clutter plus noise and aims to solve the problem that the prior art is not completely suitable for target detection against K-distributed clutter plus Gaussian white noise. The method comprises steps of: 1) acquiring an echo data matrix, and blocking the echo data matrix; 2) selecting a to-be-detected distance unit zk of the bth echo data block, and calculating the covariance matrix estimator R<k> of the to-be-detected distance unit zk; 3) calculating the carrier noise ratio CNR of the echo data by use of the covariance matrix estimator R<k>; 4) calculating the test statistics xik of the to-be-detected distance unit by use of the CNR and the R<k>; 5) calculating the detection threshold T<xi> according to the false alarm probability; and 6) judging whether the a target exists or not by comparing the size of the test statistics xik and the detection threshold T<xi>. In this way, the target detection performance is improved and the approximately optimal radar target detection method can be used for radar motion detection under the back ground of sea clutter.

Application Domain

Wave based measurement systems

Technology Topic

White noiseDetection performance +8

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Examples

Experimental program(1)

Example Embodiment

[0024] The present invention will be further explained below in conjunction with the drawings:

[0025] Reference figure 1 , The implementation steps of the present invention are as follows:

[0026] Step 1. Obtain the echo data matrix X.

[0027] The radar transmitter emits a continuous pulse signal, and the pulse signal irradiates the surface of the object to produce an echo. The radar receiver receives the echo data matrix X. The echo data matrix X is a matrix of Q×M dimensions, where Q represents the echo The number of accumulated pulses of data, and M represents the number of distance units of echo data.

[0028] Step 2: Block processing of the echo data matrix.

[0029] The echo data matrix X is equally divided into B N×M-dimensional echo data blocks along the pulse dimension, where N represents the number of pulses of each echo data block, and B echo data blocks are respectively denoted as X 1 ,X 2 …,X b ,...,X B , X b Represents the b-th echo data block, b=1, 2,...,B, and the value of B is a natural number greater than 1 and satisfies B×N≤Q. In the example of the present invention, B=10 4.

[0030] Step 3. Select the distance unit to be detected according to the echo data block, and calculate the covariance matrix estimation of the distance unit to be detected

[0031] 3.1) Select the b-th echo data block X b The kth distance unit is used as the distance unit z to be detected of the echo data block k , K=1,2,...,M;

[0032] 3.2) Remove the distance unit z to be detected k And its adjacent two distance units, using echo data block X b The remaining L distance units are used as distance units to be detected z k The reference distance unit of, where L is a natural number greater than 1;

[0033] 3.3) Calculate the distance unit z to be detected k Covariance matrix estimation

[0034] Calculate the distance unit z to be detected k Covariance matrix estimation There are three main methods:

[0035] The first is the sample covariance matrix estimation method, and its calculation formula is:

[0036]

[0037] Where z q Indicates the qth reference distance unit, the superscript H represents the conjugate transpose, and L represents the number of reference distance units;

[0038] The second is the normalized sample covariance matrix estimation method, and its calculation formula is:

[0039]

[0040] The third is the power median normalized covariance matrix estimation method, its calculation formula is:

[0041]

[0042] Among them, meadia{} means the median value.

[0043] This example uses but is not limited to using the first method to calculate the distance unit z to be detected k Covariance matrix estimation

[0044] Step 4. Use the distance unit z to be detected k Covariance matrix estimation Calculate the noise to noise ratio CNR of the echo data.

[0045] 4.1) According to the Doppler frequency f of the given target s , Calculate the normalized Doppler frequency f d :

[0046]

[0047] Among them, t represents the radar pulse transmission period;

[0048] 4.2) Using normalized Doppler frequency f d , Calculate the Doppler steering vector p of the target:

[0049]

[0050] Among them, the superscript T means transpose;

[0051] 4.3) Using the target's Doppler steering vector p and the distance unit z to be detected k Covariance matrix estimation Calculate the noise to noise ratio CNR of echo data:

[0052]

[0053] Among them, μ represents the scale parameter of the texture component, ν represents the shape parameter of the texture component, and σ 2 Represents the power of Gaussian white noise, Represents the power ratio of K distribution clutter to Gaussian white noise in the echo data.

[0054] Step 5, use the noise-to-noise ratio CNR of the echo data and the distance unit z to be detected k Covariance matrix estimation Calculate the distance unit z to be detected k Test statistic ξ k :

[0055]

[0056] Among them, the superscript -1 indicates the inversion, |·| indicates the modulus value, the superscript γ indicates the exponential factor, and γ is any real number greater than 0. The value of γ=2 in this example.

[0057] Step 6. According to the false alarm probability f given by the system, the detection threshold T is calculated through Monte Carlo experiment ξ.

[0058] 6.1) Let C be the set natural number greater than 100/f, the value is C=10 6 , Simulate C distance units without the target, and calculate the test statistics of each distance unit:

[0059]

[0060] Where z w Represents the wth distance unit, ξ w Indicates the test statistic of the w-th distance unit;

[0061] 6.2) Arrange the obtained C test statistics in descending order, and take the [Cf]th test statistic after the arrangement as the detection threshold T ξ , Where [Cf] represents the largest integer that does not exceed the real number Cf.

[0062] Step 7. According to the distance unit z to be detected k Test statistic ξ k And detection threshold T ξ Determine whether the target exists.

[0063] Set the distance unit to be detected z k Test statistic ξ k And detection threshold T ξ Compare: If ξ k ≥T ξ , It indicates that the distance unit to be detected has a target, if ξ k ξ , It means that the distance unit to be detected has no target.

[0064] The effect of the present invention will be further explained below in conjunction with simulation experiments.

[0065] 1. Simulation parameters

[0066] The echo data used in the simulation experiment is the K distribution clutter plus Gaussian white noise data generated by Matlab software.

[0067] Parameter 1. Use Matlab software to simulate white K-distribution clutter plus Gaussian white noise data. The parameters of the simulation data are set as: pulse number N=16, texture component shape parameter ν=0.5, Gaussian white noise power σ 2 =1, normalized Doppler frequency f d It is a random number between 0 and 0.5, and the false alarm probability is f=10 -4 , Change the scale parameter μ of the texture component to make the signal-to-noise ratio SCNR=9dB and the noise-to-noise ratio CNR from -20dB to 20dB.

[0068] Parameter 2. Use Matlab software to simulate colored K-distribution clutter plus Gaussian white noise data. The parameters of the simulation data are set as: pulse number N=8, texture component shape parameter ν=1, texture component scale parameter μ=2, Gaussian white noise power σ 2 =1, speckle covariance matrix R=[m ij ] 1≤i,j≤N ,m ij =ρ |i-j| ,0 <1, where |·| represents the modulus value, m ij Represents the element in the i-th row and j-th column of the speckle covariance matrix R, ρ represents the correlation coefficient, ρ=0.5, the signal-to-noise ratio SCNR=3dB, the number of reference units L=32, the false alarm probability f=10 -4 , The normalized Doppler frequency value is from 0 to 0.5.

[0069] 2. Simulation experiment content

[0070] The simulation experiment analyzes the detection performance by comparing the detection probabilities of different methods under the same background. The greater the detection probability, the better the detection performance of the detector.

[0071] Simulation experiment 1

[0072] Given a normalized Doppler frequency, the speckle covariance matrix R=I, when the noise-to-noise ratio CNR changes from -20dB to 20dB, the matched filter MF is used, which depends on the shape parameter detector α-MF, optimal K-distribution plus Gaussian white noise detector OKGD and the present invention perform target detection under parameter 1, and the detection result is as follows figure 2 As shown, figure 2 The horizontal axis represents the CNR change of the noise-to-noise ratio, and the vertical axis represents the detection probability. figure 2 The solid line represents the detection probability curve of the detector α-MF which depends on the shape parameter, the dashed line represents the detection probability curve of the matched filter MF, the framed line represents the detection probability curve of the present invention, and the dotted line represents the optimal K distribution plus The detection probability curve of the Gaussian white noise detector OKGD.

[0073] by figure 2 It can be seen that under the background of K-distribution clutter and noise, the performance of the method proposed by the present invention is close to the optimal K-distribution plus Gaussian white noise detector OKGD, which is better than the matched filter MF and the shape parameter-dependent detector α-MF, That is, the present invention can reduce the performance loss to a very low level.

[0074] Simulation experiment 2

[0075] When the normalized Doppler frequency value changes from 0 to 0.5, the noise-to-noise ratio that depends on the Doppler frequency changes with the normalized Doppler frequency value, using the adaptive matched filter AMF, which depends on the shape parameter The adaptive detector α-AMF and the present invention perform target detection under parameter 2. The detection results are as follows image 3 As shown, image 3 The horizontal axis represents the normalized Doppler frequency change, the vertical axis represents the detection probability, image 3 The solid line in the figure represents the detection probability curve of the detector α-AMF that depends on the shape parameter, the dotted line represents the detection probability curve of the adaptive matched filter AMF, and the framed line represents the detection probability curve of the present invention.

[0076] image 3 It shows that when the normalized Doppler frequency value is small, that is, when the target is in the clutter-dominated area, the present invention has the same performance as the approximately optimal shape parameter-dependent adaptive detector α-AMF, and the performance is better than adaptive Matched filter AMF. When the normalized Doppler frequency value is large, that is, when the target is outside the clutter-dominated area, the performance of the present invention is close to or even slightly better than the approximately optimal adaptive matched filter AMF, and the performance ratio depends on the adaptation of the shape parameter The detector α-AMF is much better. In general, the detection performance of the present invention is better than the existing methods.

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Classification and recommendation of technical efficacy words

Improve detection performance

Approximately optimal radar target detection method against K-distributed clutter plus noise

A radar target and detection method technology, which is applied to radio wave measurement systems, instruments, etc., can solve problems such as poor detection performance, complex detector expressions, and reduced detection performance, and achieve the effect of improving detection performance

AI-Extracted Technical Summary

Problems solved by technology

Abstract

Image

Examples

Example Embodiment

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Description & Claims & Application Information

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