Scheduling parallel Kalman filters with quantized deadlines

In this paper we explore the problem of scheduling parallel processes of Kalman filters to meet individual estimation error requirements. It is assumed that at each time-step measurements of only one process are received. We define real-time deadlines of transmissions and convert the problem into arranging sequence of tasks with corresponding deadlines. To reduce computations, cycles of transmissions are calculated and virtual processes are introduced into scheduling. A sliding window method is then designed to adjust the processes against real-time disturbances in applications. Compared with algorithms proposed in Lin and Wang (2013), the proposed algorithm is able to schedule a feasible sequence adaptively within a short scheduling window and requires little computation.