State estimation with quantized innovations in wireless sensor networks: Gaussian mixture estimator and posterior Cramér-Rao lower bound

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• 作者：Zhi Zhang^a ; ^b ; ^{mark1896bc@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jianxun Li^aAuthor Vitae ; Liu Liu^cAuthor Vitae ; Zhaolei Liu^bAuthor Vitae ; Shan Han^a
• 关键词：Posterior Cramé ; r&ndash ; Rao lower bounds ; Quantization ; State estimation ; Target tracking ; Wireless sensor networks
• 刊名：Chinese Journal of Aeronautics
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：28
• 期：6
• 页码：1735-1746
• 全文大小：1926 K

文摘

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 Cramér–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.