Locally Distributed Estimation With Binary Sensors: A Dissipativity-Based Approach

In this paper, a distributed estimation algorithm is proposed to address the $\boldsymbol{H}_{{\infty}}$-consensus state estimation challenges over binary sensor networks. The algorithm extracts richer information from limited binary data by modeling the time-varying threshold of binary sensors as a linear combination of switching measurements at two consecutive time instants. By incorporating the constraint induced by the switching events, a novel distributed estimator is designed, which integrates data from both the sensor itself and neighboring nodes within its sensing range to enhance estimation accuracy. In addition, a local performance analysis framework is developed based on the vector dissipativity theory such that the proposed distributed estimation algorithm can be independently executed at each node, significantly reducing the computational complexity. Furthermore, sufficient conditions for the desired $\boldsymbol{H}_{{\infty}}$-consensus performance are derived, and a systematic procedure for directly calculating the estimator gains is formulated as linear matrix inequalities. Both theoretical analysis and simulation results demonstrate the effectiveness of the proposed algorithm.