Abstract
In most practical applications, the tracking process needs to update the data constantly. However, outliers may occur frequently in the process of sensors’ data collection and sending, which affects the performance of the system state estimate. In order to suppress the impact of observation outliers in the process of target tracking, a novel filtering algorithm, namely a robust adaptive unscented Kalman filter, is proposed. The cost function of the proposed filtering algorithm is derived based on fading factor and maximum correntropy criterion. In this paper, the derivations of cost function and fading factor are given in detail, which enables the proposed algorithm to be robust. Finally, the simulation results show that the presented algorithm has good performance, and it improves the robustness of a general unscented Kalman filter and solves the problem of outliers in system.
Highlights
In real-world applications, target tracking problems have attracted much attention such as maneuvering target tracking [1], ballistic target tracking [2] and multiple target tracking [3], etc.For getting better accuracy, efficiency and performance of tracking problems, the effect of noise needs to be reduced, especially of measurement noise
Motivated by the above discussion, this work proposes a new adaptive robust unscented Kalman filter (UKF) scheme based on both fading factor and maximum correntropy criterion (MCC) to focus on the state estimation problems with measurement outliers
The state estimate of Kalman filter depends on the ratio of new measurements and the ones which are based on predicted state vector, dynamics model, and all previous measurements
Summary
In real-world applications, target tracking problems have attracted much attention such as maneuvering target tracking [1], ballistic target tracking [2] and multiple target tracking [3], etc. Based on Bayesian estimation and a sequential Monte Carlo approach, Du et al utilized PF to handle nonlinear and non-Gaussian problems, and the PF is applied in small target tracking in an optimal image sequence [16] These nonlinear filters were susceptible to outliers and did not have robust property. Karlgaard [20] proposed an adaptive robust nonlinear filtering algorithm to resist the effects of outliers For both the state and measurement outliers, Gandhi and Mili [21] introduced a generalized maximum likelihood type KF. Motivated by the above discussion, this work proposes a new adaptive robust UKF scheme based on both fading factor and maximum correntropy criterion (MCC) to focus on the state estimation problems with measurement outliers.
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