Bayesian filtering with unknown sensor measurement losses

This paper studies the state estimation problem of a stochastic nonlinear system with unknown sensor measurement losses. If the estimator knows the sensor measurement losses of a linear Gaussian system, the minimum variance estimate is easily computed by the celebrated intermittent Kalman filter (IKF). However, this will no longer be the case when the measurement losses are unknown and/or the system is nonlinear or non-Gaussian. By exploiting the binary property of the measurement loss process and the IKF, we design three suboptimal filters for the state estimation, that is, BKF-I, BKF-II, and RBPF. The BKF-I is based on the MAP estimator of the measurement loss process and the BKF-II is derived by estimating the conditional loss probability. The RBPF is a particle filter-based algorithm that marginalizes out the loss process to increase the efficiency of particles. All of the proposed filters can be easily implemented in recursive forms. Finally, a linear system, a target tracking system, and a quadrotor's path control problem are included to illustrate their effectiveness, and show the tradeoff between computational complexity and estimation accuracy of the proposed filters.Ministry of Education (MOE)Accepted versionThis work was supported in partby the National Key Research and Development Program of China un-der Grant 2017YFC0805310, in part by the National Natural ScienceFoundation of China under Grant 61722308 and Grant 41427806, andin part by the Ministry of Education of Singapore under Grant MoE Tier RG78/15