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Di Wu
Lanzhou University of Technology

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Acoustic Source Localization Based on Iterative Unscented Particle Filter Jie cao; Jia-qi Liu; Di Wu; Jin-hua Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 5: May 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

To solve the problem of tracking an acoustic source in noise and reverberation environment, a new method is proposed in pursuit of higher accuracy. First, this paper improves the unscented particle filter, which can add the latest measurement information to optimize the proposal distribution. Then, the likelihood function is constructed by calculating the microphone arrays’ output energy in the framework of the improved algorithm. Finally, the experiment results indicate that the proposed localization method can not only improves the accuracy of location estimation, but also can enhance the ability to resist noise and reverberation in the acoustic source localization system. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4816
Object Tracking Method Based on a New Multi-Feature Fusion Strategy Jie Cao; Lei-lei Guo; Jin-hua Wang; Di Wu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 9: September 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i9.pp6811-6818

Abstract

In order to solve the poor robustness problem due to partial occlusionof target, we propose an adaptive particle filter tracking method based on thefusion of the multiple features. First, we regard information fusion as aprocess of information loss, loss coefficient is defined based on this idea.Then the credibility of feature can be obtained by weighted distance and themean of particle filter, and we use the features’ credibility to reallocate theloss coefficient. An extensive number of comparative experiments show theproposed algorithm is more stable and robust than multiplicative fusion andadditive fusion tracking algorithms.
Multiple-feature Tracking Based on the Improved Dempster-Shafer Theory Jie Cao; Leilei Guo; Xing Meng; Di Wu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: January 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Dempster-Shafer evidence theory is widely used in the fields of decision level information fusion. In order to overcome the problem of the counter-intuitive results encountered when using Dempster’s combination rule to combine the evidences which exist high conflict, a modified sequential weighted evidence combination is proposed. Firstly, the credibility of each evidence can obtained based on K-L distance, besides, the uncertainty of each evidence can obtained based on information entropy. Simultaneously, using the uncertainty of each evidence to improve the credibility of each evidence, then the weights of the bodies of evidence are obtained based on the improved credibility of each evidence, the weights generated are used to modify the bodies of evidence including the previous combination result, the previous evidence and the new arriving body of evidence at current step. Finally, according to the Dempster’s combination rule, the weighted average combination results can be obtained. In the experimental part, the improved method is used to fuse video multiple features in target tracking system and compared the results with the standard D-S theory. The simulation results show that the proposed method has better performance. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3364
Object Tracking Based on Multiple Features Adaptive Fusion Jie Cao; Leilei Guo; Jinhua Wang; Di Wu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5621-5628

Abstract

Multiple features fusion based tracking is one of the most active research in tracking literature, In this paper, a novel adaptive fusion strategy is proposed for multiple features fusion, based on two common used fusion rules: product rule and weighted sum rule. This strategy employs particle filtering technique; product rule and weighted sum rule are unified into an adaptive framework through defined features distance. In practice, the new fusion strategy shows more robustness than product fusion and weighted sun rule.
Co-Authors Jia-qi Liu Jie CAO Jin-hua Wang Jinhua Wang Lei-lei Guo Leilei Guo Xing Meng