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Vol. 172, Issue 6, June 2014, pp. 196-201




An Improved Particle Filter with Applications in Ballistic Target Tracking

1 Chun-Ling Wu, 1 Yong-Feng Ju, 2 Chong-Zhao Han

1 Chang’an University, Middle Section of Nan Erhuan Road, Xi'an City, Shaanxi, 710064, P. R. China
2 Xi’an Jiaotong University, No.28 Xianning West Road, Xi'an, Shaanxi, 710049, P. R. China
1 Tel.: +86 13991383895, fax: +86 2988230909

1 E-mail: informationway@163.com


Received: 30 April 2014 /Accepted: 30 May 2014 /Published: 30 June 2014

Digital Sensors and Sensor Sysstems


Abstract: In this paper, we present an improved particle filter algorithm for ballistic target tracking, the quadrature Kalman particle filter (QKPF). The proposed algorithm uses quadrature Kalman filter (QKF) for generating the proposal distribution. The QKF is a recursive, nonlinear filtering algorithm developed in the Kalman filtering framework. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian-Hermite quadrature points, need not to compute the Jacobian matrix and is easy to implement. Moreover, the generated proposal distribution integrates the latest measurements into system state transition density, so it can match the posterior density well. The simulation results show that QKPF is a viable alternative for sequential estimation in nonlinear dynamic models.


Keywords: Ballistic target tracking, Quadrature Kalman filter, Importance density function, Particle filter.


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