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： 中國航空學報(英文版)  ISTIC 英文刊名： CHINESE JOURNAL OF AERONAUTICS  年，卷(期)： 2008 21(4)  分類號： V2  關(guān)鍵詞： signal processing   Gaussian sum probability hypothesis density   simulation   nonlinear non-Gaussian   tracking  

【Gaussian Sum PHD Filtering Algorithm】相關(guān)文章：

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION ON-04-27

Rapid Optimal Generation Algorithm for Terrain Following Trajectory Based on Optimal Control04-27

STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS04-27

A scheme to implement the Deutsch-Josza algorithm on a superconducting charge-qubit quantum computer04-26

A Bayesian based quasi-analytical algorithm for retrieval of inherent optical properties from ocean color04-27

Global and local contrast enhancement algorithm for image using wavelet neural network and stationary wavelet transform04-26