Flexible optimal Kalman filtering in wireless sensor networks with intermittent observations

This paper is concerned with the distributed Kalman filtering over the wireless sensor networks (WSNs) in the presence of intermittent observations and different sensing states, where only task nodes are required to estimate the state of a linear time-invariant discrete-time system. A class of flexible binary values is used to develop the adaptability of flexible optimal Kalman filtering (FOKF) for variable sensing states. Based on the minimum error covariance trace principle, two classes of FOKFs have optimal collaborative estimation via their own and community observations, including the original FOKF and the FOKF with uncertain noise variance. The performance analysis of these two types of filters show that they have high estimation accuracy, strong robustness, low energy consumption and user-friendliness. The proposed algorithms are applied to estimate and track the position of a moving target in WSNs. The simulation illustrates that the proposed filters have superior performance, compared with the existing algorithms. (c) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.