This paper focuses on the design of a privacy-preserving distributed Kalman filtering algorithm for a class of linear time-varying systems in signal-to-interference-plus-noise ratio (SINR)-based sensor networks, where packet dropouts may occur in information transmission between neighboring sensor nodes. Considering the potential occurrence of eavesdropping attacks during information transmission, which is common due to the inherent vulnerability of SINR-based sensor networks, a new class of distributed secure Kalman filtering algorithm has been developed. The presented algorithm incorporates a modified ElGamal cryptosystem and adaptive fusion weights to significantly enhance security, resist privacy leakage, and bolster robustness against packet dropping. Then, a detailed performance analysis for the presented distributed secure Kalman filtering algorithm is conducted, where the security and unbiasedness of the designed algorithm are discussed. Sufficient conditions for the stability of the estimation error are further established to ensure that the estimation error is ultimately bounded in the almost sure sense. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithm.

This paper focuses on the event-triggered distributed hierarchical filtering over wireless networks consisting of local wireless sensor networks in the first layer and wireless cluster networks in the second layer. To reduce communication consumption over local wireless sensor networks, recursive event-triggered sequential local estimators are first proposed in each cluster head by the co-design of an event-triggered scheduling mechanism and sequential local estimators, with only necessary measurements being transmitted to the cluster head in an individual cluster. A distributed hierarchical estimator is further derived by fusing local estimates over unreliable wireless cluster networks, where interacted estimates may be unavailable due to potential random link failures. It is noted that the designed hierarchical estimator has significant advantages in reducing communication bandwidth and improving execution efficiency. Finally, a target tracking example is shown to verify the proposed results.