Generalized Pareto distribution parameter explicit two-quantile estimation method
A technology for distributing parameters and quantiles, applied in radio wave measurement systems, instruments, etc., can solve the problems of not obtaining explicit solutions for shape parameters and scale parameters, decreasing estimation accuracy, and insufficient estimation result accuracy, and improving flexibility. Performance and accuracy, high accuracy, the effect of expanding the selection range
Active Publication Date: 2017-10-17
XIDIAN UNIV
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Such outlier samples can lead to a sharp drop in the estimation accuracy of the three methods
The document "P-L.Shui and M.Liu,"Subband adaptive GLRT-LTD for weak moving targets insea clutter,"IEEE Trans.Aerosp.Electron.Syst.,52(1):423-437,2016."proposes biquantile Point estimation method, which is robust to abnormal samples, but the literature only clearly gives the parameter estimation method when the sample cumulative probability is 0.5 and 0.75, when the sample cumulative probability takes other values, the shape parameter and scale parameter are not obtained The explicit solution of , and the accuracy of the parameter estimation method when the sample cumulative probability is 0.5 and 0.75 is not enough
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[0037] The present invention will be further described below in conjunction with accompanying drawing:
[0038] refer to figure 1 , the implementation steps of the present invention are as follows:
[0039] Step 1, obtain the sequence of increasing clutter amplitude.
[0040] The radar transmitter transmits a continuous pulse signal, and the pulse signal is irradiated on the surface of the object to generate an echo. The radar receiver receives the echo data. Among the echo data, N clutter data are selected, and the N clutter data are modeled and Arranged in ascending order, get the clutter amplitude increasing sequence z 1 ,z 2 ,...,z t ,...,z N , where z t Indicates the tth clutter amplitude in the clutter amplitude increasing sequence, t=1,2,...,N, this example takes N=10 4 .
[0041] Step 2, determine the probability density function f(r) of the generalized Pareto distribution:
[0042]
[0043] Among them, r represents the magnitude of the clutter, which is th...
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The invention discloses a generalized Pareto distribution parameter explicit two-quantile estimation method, aiming to solve the problems that the prior art is bad in applicability and is bad in robustness on abnormal samples. The implementation steps are 1) acquiring the clutter amplitude increasing sequence; 2) according to the generalized Pareto distribution probability density function, determining the cumulative distribution function of the generalized Pareto distribution; 3) giving a positive number q greater than 1, and selecting the sample cumulative probabilities alpha and beta; 4) according to the value of positive number q, obtaining the relation between the quantiles r alpha and r beta and the generalized Pareto distribution parameter; 5) obtaining the estimate value of each quantile according to the clutter amplitude increasing sequence; and 6) replacing the quantiles in the relation with the estimate values of the quantiles to obtain the shape parameter estimate value (as shown in the description) and the scale parameter estimate value (as shown in the description). The method can reduce the interference of the abnormal scattering unit to the sample, improve the parameter estimation performance, and can be used for the target detection in the sea clutter background.
Description
technical field [0001] The invention belongs to the technical field of radar target detection, and in particular relates to a biquantile point estimation method, which can be used to determine the shape and scale parameters of a sea clutter amplitude distribution model under the background of sea clutter. Background technique [0002] Object detection in sea clutter background is an important application field of radar. [0003] The composite Gaussian model is a sea clutter model widely recognized by scholars at present. It is the product of a slowly varying positive random variable texture component and a rapidly varying complex Gaussian random vector speckle component. When the texture component of the sea clutter obeys the inverse gamma distribution, the sea clutter amplitude obeys the generalized Pareto distribution. The structure of the optimal detector under the generalized Pareto distribution model has been obtained. The structure of the optimal detector depends on ...
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IPC IPC(8): G01S7/292
CPCG01S7/292
Inventor 水鹏朗杨春娇于涵史利香
Owner XIDIAN UNIV