Adaptive Kalman Estimation in Target Tracking Mixed with Random One-Step Delays, Stochastic-Bias Measurements, and Missing Measurements

Abstract

The objective of this paper is concerned with the estimation problem for linear discrete-time stochastic systems with mixed uncertainties involving random one-step sensor delay, stochastic-bias measurements, and missing measurements. Three Bernoulli distributed random variables are employed to describe the uncertainties. All the three uncertainties in the measurement have certain probability of occurrence in the target tracking system. And then, an adaptive Kalman estimation is proposed to deal with this problem. The adaptive filter gains can be obtained in terms of solutions to a set of recursive discrete-time Riccati equations. Examples in three scenarios of target tracking are exploited to show the effectiveness of the proposed design approach.

An Improved Unscented Kalman Filter for Discrete Nonlinear Systems with Random Parameters

“…However, the linearity of the dynamic system, as one of the basic requirements of the Kalman filter, is hard to satisfy in actual implementation. On the other hand, discrete dynamic systems with random parameters arise in many applications such as missile track estimation, satellite navigation, maneuvering target tracking, and economic forecast [4][5][6][7][8].…”