An adaptive nonlinear filter with missing measurements compensation for manoeuvring target tracking

Abstract To solve the problem of missing measurements in highly manoeuvring target tracking, an expected‐mode‐augmentation‐based unscented Kalman filter with missing measurements compensation (EMA‐MMCUKF) is designed based on the variable‐structure multiple model method. In the proposed EMA‐MMCUKF, the random missing measurements are described by the Bernoulli distribution, and the one‐step prediction is used as the compensation. Based on the proposed EMA‐MMCUKF, a Bayesian estimation‐based expected‐mode‐augmentation unscented Kalman filter with missing measurements compensation (BE‐EMA‐MMCUKF) is proposed for adaptive estimation of the sensor measurement reception rate, where the unknown measurement reception rate can be estimated by fully utilising prior information. Simulation results demonstrate that the proposed EMA‐MMCUKF can effectively track the manoeuvring target at different measurement reception rates. Moreover, when the sensor prior information differs significantly from the true measurement reception rate, the proposed BE‐EMA‐MMCUKF can effectively estimate the unknown sensor measurement reception rate and improve the accuracy of manoeuvring target tracking compared with non‐estimation of the sensor measurement reception rate.