Abstract
Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a Gaussian density by using the statistical linear regression (SLR) to estimate the mean and covariance. In order to track bearings-only maneuvering target, the posterior PDF is jointly estimated based on the prior probability density function and the modified prior probability density function, and a practical algorithm is developed. Finally, compared with other nonlinear filtering approaches, the experimental results of the proposed algorithm show a significant improvement for both the univariate nonstationary growth model (UNGM) case and bearings-only target tracking case.
Highlights
Bearings-only maneuvering target tracking has been widely researched for decades
Unlike the truncated unscented Kalman filtering (TUKF) algorithm, to overcome the modeling uncertainty, a modified prior probability density function (PDF) is defined based on several auxiliary target characteristics and current measurements, which can effectively minimize the variance of the prior distribution
In the maneuvering target tracking scenario, only a constant velocity model is used for the auxiliary truncated unscented Kalman filtering (ATUKF) algorithm and truncated quadrature Kalman filtering (TQKF) algorithm
Summary
Bearings-only maneuvering target tracking has been widely researched for decades. It is important for many applications such as maritime surveillance, navigation and aerospace, wireless sensor networks (WSN), and infrared search and track (IRST) systems [1,2,3,4,5,6]. For the maneuvering target tracking problem in bearings-only wireless sensor networks (WSNs), Atiyeh et al [20] proposed a interacting multiple model particle filter to estimate the state variables of the moving target. Li et al [21] proposed a Rao–Blackwellized particle filter based on multiple model algorithm for maneuvering target tracking in a cluttered environment. Yu et al [22] proposed a distributed particle filter by incorporating the curvature of the sensing region in the measurement model for bearings-only tracking of a moving target. Unlike the TUKF algorithm, to overcome the modeling uncertainty, a modified prior probability density function (PDF) is defined based on several auxiliary target characteristics and current measurements, which can effectively minimize the variance of the prior distribution.
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