Logarithmic moment-based generalized Pareto distribution parameter estimation method

Abstract

The invention discloses a logarithmic moment-based generalized Pareto distribution parameter estimation method. The invention mainly aims to solve the problems of poor estimation accuracy and low execution efficiency of an existing parameter estimation method. According to the technical schemes of the invention, the method includes the following steps that: 1, sea clutter data samples are obtained through a sea monitoring radar; 2, the obtained clutter data samples are normalized according to clutter power; 3, the check estimator of data in the log domain is calculated through using the normalized clutter data samples; and 4, a distribution parameter is calculated through using the check estimator. With the method of the invention adopted, the estimation accuracy of a traditional Pareto distribution parameter estimation method can be improved. The method has high computation speed and can meet a requirement for the real-time processing of the signals of a radar system and can be applied to target detection under sea clutter background.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a generalized Pareto distribution parameter estimation method, which can be used for target detection under the sea clutter background. Background technique [0002] Target detection technology under sea clutter background is a crucial research direction in radar application technology, and has been widely used in military and civilian fields. The accurate analysis of the statistical characteristics of sea clutter is an important factor for the target detection technology to achieve good results in the background of sea clutter. Therefore, giving a suitable model and accurately estimating its model parameters has become an important problem that we need to solve. [0003] With the improvement of the distance resolution of modern radar systems, radar echoes have statistical characteristics that were not found in previous low-resolution radar systems, usually...

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Problems solved by technology

[0005] The literature "Castillo, E., Hadi, A.S., 1997. Fitting the generalized Pareto distribution to data. J. Amer. Statist. Assoc. 92, 1609–1620." gives the moment estimation and maximum likelihood of the generalized Pareto distribution The estimation method estimates the parameters according to the sample moment and the likelihood function respectively, but because the moment estimation itself is easily affected by the sample size and abnormal data, its estimation accuracy is difficult to guarantee

Although the estimation accuracy of maximum likelihood estimation can meet the requirements, the time complexity of the algorithm is high, so the engineering implementation is more difficult.

[0006] The literature "Arnold, B.C., Press, S.J., 1989. Bayesian estimation and prediction for Pareto data. J. Amer. Statist. Assoc. 84, 1079–1084." gives a generalized Pareto distribution parameter estimation method based on prior information , but its calculation is relatively complicated, and the estimation effect is affected by the accuracy of prior information, so it is inconvenient to apply

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