Gaussian Sum PHD Filtering Algorithm for Nonlinear Non-Gaussian Models

A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density (GSPHD) filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussiaa sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaassian mixture probability hypothesis density (GMPHD) filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special ease of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.

作 者： Yin Jianjun Zhang Jianqiu Zhuang Zesen   作者單位： Department of Electronic Engineering, Fudan University, Shanghai 200433, China  刊 名： 中國(guó)航空學(xué)報(bào)(英文版)  ISTIC 英文刊名： CHINESE JOURNAL OF AERONAUTICS  年，卷(期)： 2008 21(4)  分類(lèi)號(hào)： V2  關(guān)鍵詞： signal processing   Gaussian sum probability hypothesis density   simulation   nonlinear non-Gaussian   tracking  

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