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Li Wei
Xuzhou Institute of Technology

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A New Fusion Tracking Method with Unknown Noise Sun Tao; Qin Lu-Fang; Li Wei; Li Jun; Cao Jie
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 10: October 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i10.pp7262-7273

Abstract

In order to sovle the problem of video target tracking problem, this paper proposed a new kind of particle filter with unknown statistical characteristics of noises. This paper derivates the distribution function of statistical properties in detailed with correlative noise by the establishment model of related noise, and gives the real-time estimation equation of the noise statistical characteristics and the system state. The new algorithm reduced the estimation error effectively and improved the anti-noise ability of the system. Under the improving particle filter framework, we used color and motion edge character as observation model and fused multi features weights through the D-S evidence theory. The experimental results showed that the method proposed in this paper has high precision and strong robustness to target tracking under the complicated conditions.
A New Particle Filter Algorithm with Correlative Noises Qin Lu-Fang; Li Wei; Sun Tao; Li Jun; Cao Jie
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp6164-6172

Abstract

The standard particle filter (SPF) requirements system noise and measurement noise must be independent. In order to overcome this limit, a new kind of correlative noise particle filter (CN-PF) algorithm is proposed. In this new algorithm, system state model with correlative noise is established, and the noise related proposal distribution function characteristics were analyzed in detail. At last, the concrete form of the best proposal distribution function is derived based on the condition of the minimum variance of importance weight with the assumption of gaussian noise. Theoretical analysis and experimental results show the effectiveness of the proposed new algorithm.
Co-Authors Cao Jie Li Jun Qin Lu-Fang Sun Tao