Distributed Estimation and Smoothing for Linear Dynamic Systems Over Sensor Networks

This paper focuses on distributed estimation and smoothing for linear dynamic systems monitored by sensors that are organized as a network. To achieve low power consumption of sensors, the system is decomposed into several subsystems based on the observable canonical decomposition method. Each sensor in the network performs state estimation for one subsystem at most, while estimations for the majority of subsystems are obtained from the neighbors of each sensor. The optimal gains of the estimator and the smoother are pre-calculated offline, and a numerical solution is obtained by taking advantage of optimization algorithms. Furthermore, the boundedness of the prediction error covariance is guaranteed resulting from the bounded solution of a Riccati equation, and the performance of the smoother is analyzed. A numerical example is presented to validate the feasibility of the algorithm even if the sensor network suffers from network interruptions. The proposed algorithm is more robust against partial loss of predictions and recovers more rapidly, compared to some consensus-based distributed estimation algorithms.

Automated summary

This paper focuses on distributed estimation and smoothing for linear dynamic systems monitored by a sensor network. The system is decomposed into subsystems, and each sensor estimates the state for one subsystem at most. The estimations for most subsystems are obtained from the neighbors of each sensor. Optimal gains are pre-calculated offline, and a numerical solution is obtained using optimization algorithms. The algorithm is more robust and recovers faster compared to consensus-based estimation algorithms.