Encoding-Based Fusion Estimation for Multi-Sensor Systems: Applications to Target Tracking

This paper is concerned with the robust set-membership fusion estimation problem for multi-sensor systems under the influence of uncertain parameters and unknown-but-bounded noises. The sensor nodes and the remote fusion estimator are connected through bandwidth-constrained communication networks. To address this challenge, a dynamic encoding-based data compression mechanism is introduced, enabling the data to be efficiently encoded using a reduced number of bits. By thoroughly analyzing the impact of uncertain parameters, unknown-but-bounded noises, and data distortions on estimation performance, sufficient conditions are derived to ensure that the estimation error remains within the bounded ellipsoidal set. Moreover, the weight parameters are calculated by solving constrained optimization problems. Finally, the effectiveness of the proposed estimation algorithm is verified through a maneuvering target tracking scenario.