Convolution PHD Filtering for Nonlinear Non-Gaussian Models

摘要：

A novel probability hypothesis density (PHD) filter, called the Gaussian mixture convolution PHD (GMCPHD) filter was proposed. The PHD within the filter is approximated by a Gaussian sum, as in the Gaussian mixture PHD (GMPHD) filter, but the model may be nonGaussian and nonlinear. This is implemented by a bank of convolution filters with Gaussian approximations to the predicted and posterior densities. The analysis results show the lower complexity, more amenable for parallel implementation of the GMCPHD filter than the convolution PHD (CPHD) filter and the ability to deal with complex observation model, small observation noise and non-Gaussian noise of the proposed filter over the existing Gaussian mixture particle PHD (GMPPHD) filter. The multi-target tracking simulation results verify the effectiveness of the proposed method.

关键词： Probability hypothesis density (PHD) filter Nonlinear Convolution Tracking

作者: Jianjun Yin Jianqiu Zhang

作者单位: Room 133, Wuli Building, Electronic Engineering Department, Fudan University, 220 Han Dan Rd.,Shanghai, 200433, China

会议类型: 国际会议

会议名称: 2011 International Conference on Advanced Material Research(ICAMR 2011)(2011年先进材料研究国际会议)

会议地点: 重庆

会议语种:英文

页码: 344-348

在线出版日期: 2011-01-21（万方平台首次上网日期，不代表论文的发表时间）