Scheduling Parallel Kalman Filters for Multiple Processes

Abstract

In this paper, we investigate the problem of scheduling parallel Kalman filters for multiple processes, where each process is observed by a Kalman filter and at each time step only one Kalman filter could obtain observation due to practical constraints. To solve the problem, two novel notions, permissible consecutive observation loss (PCOL) and least consecutive observation (LCO), are introduced as criteria to describe feasible observation sequences for a process ensuring desired estimation qualities. Then two methods, namely, threshold method and periodic method, are proposed to calculate PCOL and LCO for each process. Based on the derived PCOL and LCO requirements, we develop two algorithms that are applicable to different situations: Sxy algorithm from the pinwheel problem for the case of LCO = 1 and tree search algorithm for general cases. Also, to reduce the computational complexity of tree search algorithm, several useful pruning conditions are obtained. Both rigorous analysis and simulation results are provided to validate the approaches.