A novel algorithm, termed a Boosted Adaptive Particle Filter (AAPF), for integrated face detection and face tr...
Jianfang Dou 1 , Jianxun Li 1,2 , Zhi Zhang 2 , Shan Han 2 1. Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 2002402. Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240
Since the features of low energy consumption and limited power supply are very important for wireless sensor networks(WSNs), the problems of distributed state estimation with quantized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function(PDF) with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algorithms for WSNs, the posterior Crame′r–Rao lower bound(CRLB) with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.
For wireless sensor networks, target tracking is an important application areas, but the communication consump...
Zhi Zhang 1 , Jianxun Li 2 , Shan Han 3 , Qiang Zhu 4 Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240
