Distributed State Estimation for Stochastic Linear Hybrid Systems With Finite-Time Fusion

Abstract

Building on the classical interacting multiple model algorithm, this article develops a novel distributed approach for solving the problem of distributed state estimation for stochastic linear hybrid systems. In our distributed framework, a network of sensors is employed and each of them measures only a portion of the system outputs, thus, the system may not necessarily be observable for any individual sensor. To tackle with this, we develop an effective data fusion scheme that enables the sensor network to collectively measure the output of the hybrid system, by leveraging the communication between neighboring sensors. Consequently, each sensor only needs to process a relatively small set of data and is able to locally and identically observe the states (both the continuous states and discrete modes) of stochastic linear hybrid systems. Stability of the proposed distributed algorithm is proved theoretically, in the sense that the covariance of estimation error is lower and upper bounded. Finally, the performance of the proposed algorithm is demonstrated via illustrative simulations on a maneuvering aircraft tracking problem.