A Non-uniform Quantization Filter Based on Adaptive Quantization Interval in WSNs

Wireless sensor networks (WSNs) fusion systems are usually faced with the communication bandwidth constraints, so it is necessary to adopt quantization. The existing quantization is usually under the assumption of known distribution function of data. However, obtaining the accurate distribution function of data is impossible. Therefore, in this paper, based on the obtained measurements, a kind of non-uniform quantization strategy under the principle of least square sum of quantization error is proposed firstly. Then, the solving method of non-uniform quantizing points based on adaptive quantization interval is presented. Next, we further provide a recursive updating method of quantizing points which include semi real-time update and real-time update. Finally, on the basis of the new method, we establish the Kalman Filter based on the new idea of Non-uniform Quantization and give some analysis of performance, which includes the theoretical part and experiment simulation.

[1]  Alejandro Ribeiro,et al.  Bandwidth-constrained distributed estimation for wireless sensor networks-part II: unknown probability density function , 2006, IEEE Transactions on Signal Processing.

[2]  Lihua Xie,et al.  Quantized Kalman Filtering , 2007, 2007 IEEE 22nd International Symposium on Intelligent Control.

[3]  Stergios I. Roumeliotis,et al.  Decentralized Quantized Kalman Filtering With Scalable Communication Cost , 2008, IEEE Transactions on Signal Processing.

[4]  Yunmin Zhu,et al.  An efficient sensor quantization algorithm for decentralized estimation fusion , 2011, Autom..

[5]  John A. Gubner,et al.  Distributed estimation and quantization , 1993, IEEE Trans. Inf. Theory.

[6]  Fuad E. Alsaadi,et al.  Distributed Kalman Filtering With Quantized Sensing State , 2015, IEEE Trans. Signal Process..

[7]  Subhrakanti Dey,et al.  Quantized Filtering Schemes for Multi-Sensor Linear State Estimation: Stability and Performance Under High Rate Quantization , 2013, IEEE Transactions on Signal Processing.

[8]  Lihua Xie,et al.  The sector bound approach to quantized feedback control , 2005, IEEE Transactions on Automatic Control.

[9]  Zhi-Quan Luo,et al.  Minimum Energy Decentralized Estimation in a Wireless Sensor Network with Correlated Sensor Noises , 2005, EURASIP J. Wirel. Commun. Netw..

[10]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[11]  S. Mitter,et al.  Quantization of linear systems , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[12]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[13]  Peter Willett,et al.  Distributed Estimation in Large Wireless Sensor Networks via a Locally Optimum Approach , 2008, IEEE Transactions on Signal Processing.

[14]  Nicola Elia,et al.  Stabilization of linear systems with limited information , 2001, IEEE Trans. Autom. Control..

[15]  Qi Tian.  Distributed quantized Kalman filtering for wireless sensor networks , 2011 .

[16]  Guoqiang Hu,et al.  Distributed Fusion Estimation With Communication Bandwidth Constraints , 2015, IEEE Transactions on Automatic Control.

[17]  Alejandro Ribeiro,et al.  Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case , 2006, IEEE Transactions on Signal Processing.

[18]  Mohamed-Slim Alouini,et al.  Distributed Estimation Based on Observations Prediction in Wireless Sensor Networks , 2015, IEEE Signal Processing Letters.

[19]  Wen-an Zhang,et al.  Distributed Fusion Estimation With Missing Measurements, Random Transmission Delays and Packet Dropouts , 2014, IEEE Transactions on Automatic Control.