Scheduling parallel Kalman filters for multiple processes

• Published in: Automatica (Oxford) Vol. 49; no. 1; pp. 9 - 16
• Main Authors: Lin, Zhiyun, Wang, Chen
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2013; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control theory. Systems; Criteria; Exact sciences and technology; Kalman filtering; Kalman filters; Mathematical analysis; Modelling and identification; Operational research and scientific management; Operational research. Management science; Parallel estimation; Periodic observation; Pruning; Scheduling; Scheduling algorithms; Scheduling, sequencing; Search algorithms; Sensor networks; Software; Theoretical computing; Trees; Sensor networks; Parallel estimation; Periodic observation; Kalman filtering; Scheduling algorithms; Kalman filter; Scheduling; Computational complexity; Pruning(tree); Search tree; System identification; Sensor array; Limit cycle
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2012.09.011

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, the threshold method and the 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.