Cubature Kalman Particle Filters

摘要：

　　To resolve the tracking problem of nonlinear/non-Gaussian systems effectively,this paper proposes a novel combination of the cubature kalman filter(CKF) with the particle filters(PF),which is called cubature kalman particle filters(CPF).In this algorithm,CKF is used to generate the importance density function for particle filter.It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian cubature points.It need not compute the Jacobian matrix and is easy to be implemented.Moreover,it makes efficient use of the latest observation information into system state transition density,thus greatly improving the filter performance.The simulation results are compared against the widely used unscented particle filter(UPF),and have demonstrated that CPF has higher estimation accuracy and less computational load.

关键词： particle filter cubature kalman filter importance density function

作者: Shah Ganlin Chen Hai Ji Bing Zhang Kai

作者单位: Department of Optics and Electronics Engineering Shijiazhuang Mechanical Engineering College Shijiazhuang,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 456-460

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）