Decentralized Kalman Filtering with Multilevel Quantized Innovation in Wireless Sensor Networks

Because of low power consumption and limited power supply significance in wireless sensor networks (WSNs), this paper studies the multilevel quantized innovation Kalman filtering (MQI-KF) for decentralized state estimation in WSNs since the MQI-KF can help to save power. In the first place, the common features of the practical low energy consumption WSNs are explored. On this basis, the new quantization scheme is presented. Besides, this paper explores the quantization state estimation by adopting the Bayesian method rather than the traditional iterated conditional expectation method. After that, this paper proposes a new decentralized state estimation algorithm (MQI-KF) for WSNs. Information entropy is analyzed to evaluate the performance of the quantization scheme. Performance analysis and simulations show that the MQI-KF is more efficient than the other decentralized Kalman filtering (KF) algorithms, and the accuracy of its estimation is close to that of the standard KF based on nonquantized measurements. Since the new quantization scheme and algorithm take into consideration the features of real WSNs which are based on the universal network protocol IEEE 802.15.4 standard, they can almost be applied into all practical WSNs with low energy consumption.