带相关噪声多传感器系统的事件触发贯序和分布式融合估计

对带相关噪声的多传感器系统,研究了事件触发的贯序和分布式融合估计算法.不同传感器之间的观测噪声同时刻相关,并与过程噪声一步相关.为了节省通信能耗,采用了事件触发传输机制,该机制依赖于每个传感器当前的观测值和上一个触发时刻的观测值.在事件触发条件下,提出了在线性最小方差意义上的最优贯序融合和分布式融合估计算法.所提出的贯序融合算法可以根据传感器观测数据到达滤波器的顺序进行实时处理,具有较小的计算负担.所提出的分布式融合算法可以对传感器观测数据进行并行处理,具有更好的可靠性.两种算法与事件触发集中融合算法具有相同的估计精度.仿真结果验证了算法的有效性.

Event-Triggered Sequential and Distributed Fusion Estimation for Multi-Sensor Systems with Correlated Noises

As for multi-sensor systems with correlated noises, event triggered sequential and distributed fusion state estimation algorithms are studied, whereas measurement noises of different sensors are correlated with each other at the same time step and also correlated with the process noise at the previous time step. In order to save communication energy, an event triggered transmission mechanism is adopted based on observations at current instant and the latest triggered instant of each sensor. Sequential fusion and distributed fusion estimation algorithms are proposed in the sense of linear minimum variance under condition of event triggered situation. The data processing can be made by the proposed sequential fusion algorithm in real time in accordance with the order of measurements of sensors arriving at the fusion center, and the algorithms have a little burden to computation. The proposed distributed fusion algorithm can process in parallel measurements of sensors, and has a good reliability. The two kinds of algorithms are as good in estimation accuracy as the event triggered centralized fusion estimator. The simulation result shows that the proposed algorithms are valid.