Unscented Kalman filtering for target tracking systems with packet dropout compensation

Abstract

The unscented Kalman filtering problem is investigated for a class of non-linear discrete-time stochastic systems subject to random packet dropout. Here, a random variable, which obeys Bernoulli distribution with known conditional probability, is introduced to depict the phenomenon of packet dropout occurring in a stochastic way. Based on the compensation technology of packet dropout, the one-step prediction value of observer instead of zero-input is used as a compensator when packet dropout occurs. Owing to taking the phenomenon of random packet dropout into account, the authors need to compute parameters to reduce the effects of the compensator. Then, based on the minimum mean square error principle, a new unscented Kalman filtering algorithm is proposed such that, for the random packet dropout, the filtering error is minimised. By solving the recursive matrix equations, the filter gain matrices and error covariance matrices can be obtained and the proposed results can be easily verified by using the standard numerical software. They finally provide two examples, one is a numerical example to show the performance of the proposed approach, and the other is to solve the problem of target estimation for a tracking system with random packet dropout.