Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation

Abstract

Low energy consumption and limited power supply are significant factors for wireless sensor networks (WSNs); thus, distributed state estimation and data fusion with quantized innovation are explored. The universal features of practical WSNs are investigated, and a dynamic transmission strategy is introduced. Furthermore, quantization state estimation based on Bayesian theory is derived. Unlike previous algorithms suitable for processing scalar measurement, the proposed distributed data fusion algorithm is applicable to general vector measurement. Furthermore, the efficiency of the proposed dynamic transmission strategy is analyzed. It is concluded that the proposed algorithm is more efficient than previous methods, and its estimation accuracy comparable to that of the standard Kalman filtering, which is based on analog-amplitude vector measurement.

创新点

低能耗和有限供能是无线传感器网络的重要特点;因此,基于此特点,本文研究了基于量化新息的分布式状态估计和数据融合。首先,针对实际无线传感器网络的普遍特点进行了研究,并介绍了一个动态传输策略。进一步,推导出了基于贝叶斯理论的量化状态估计方法。不同于已有的方法适合处理标量量化测量,本文进一步给出了能够应用于向量量化测量的数据融合算法。并且,对于本文给出的动态传输策略进行了性能分析。通过性能分析和仿真实验可以看出,本文给出的量化估计算法比已有的方法更为有效,并且算法的估计精度接近基于非量化测量向量的标准卡尔曼滤波方法。