Kalman Filtering with Unknown Sensor Measurement Losses*

This work studies the state estimation problem of a networked linear system where a sensor and an estimator are connected via a lossy network. If the measurement loss is known to the estimator, the minimum variance estimate is easily computed by the intermittent Kalman filter (IKF). However, this does not hold for the case of unknown measurement losses, and we have to address the non-Gaussianity/non-linearity of the networked system. By exploiting the measurement loss process and the IKF, we design three recursive suboptimal filters for state estimation, i.e., BKF-I, BKF-II and RBPF. The BKF-I is based on the MAP estimator of the loss process and the BKF-II is derived by an estimate of the conditional loss probability. The RBPF is an effective sequential importance sampling algorithm by marginalizing out the loss process. A target tracking example is included to illustrate their effectiveness and shows the tradeoff between computation complexity and estimation accuracy of the proposed filters.