Circular Error Probable Estimation Method Based on Gaussian Mixture Model and Expectation Maximum Algorithm for Non-GaussianDistribution

doi:10.3969/j.issn.1000-1093.2019.02.017

Abstract

Abstract: For the situation when the ordnance attacking and/or observing points do not obey the Gaussian distribution, the traditional circular error probability (CEP) computation method could not effectively deal with the experimental data. In order to resolve this problem, one new CEP estimation method based on Gaussian mixture model (GMM) and expectation maximum (EM) algorithm is proposed. In the proposed method, GMM is used to depict the ordnance attacking and/or observing points probability density function（PDF）, the EM algorithm is used to solve the model parameters, and the bisection method is used to compute CEP. A lot of scenes are used to generate the ordnance attacking and/or observing points, and the traditional method and the proposed method are used to estimate the CEP. Experimental results show that the mean square error of CEP computed by the proposed method is about 1/10 of that computed by the traditional method. This illustrates that the performance of the proposed method is better than that of the traditional method. The proposed method could be used effectively to estimate CEP when the ordnance attacking and/or observing points do not obey the Gaussian distribution. Key

Key words: circularerrorprobability, non-Gaussiandistribution, Gaussianmixturemodel, expectationmaximumalgorithm

CLC Number:

E920.8

JING Peiliang， DUAN Yu， HAN Chao， GUO Ronghua， NING Xiaolei， LIU Yu. Circular Error Probable Estimation Method Based on Gaussian Mixture Model and Expectation Maximum Algorithm for Non-GaussianDistribution[J]. Acta Armamentarii, 2019, 40(2): 369-376.

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第40卷第2期
2019 年2月兵工学报ACTA
ARMAMENTARIIVol.40No.2Feb. 2019

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