Abstract
Conventional spherical simplex-radial cubature Kalman filter (SSRCKF) for maneuvering target tracking may decline in accuracy and even diverge when a target makes abrupt state changes. To overcome this problem, a novel algorithm named strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF) is proposed in this paper. The proposed algorithm uses the spherical simplex-radial (SSR) rule to obtain a higher accuracy than cubature Kalman filter (CKF) algorithm. Meanwhile, by introducing strong tracking filter (STF) into SSRCKF and modifying the predicted states’ error covariance with a time-varying fading factor, the gain matrix is adjusted on line so that the robustness of the filter and the capability of dealing with uncertainty factors is improved. In this way, the proposed algorithm has the advantages of both STF’s strong robustness and SSRCKF’s high accuracy. Finally, a maneuvering target tracking problem with abrupt state changes is used to test the performance of the proposed filter. Simulation results show that the STSSRCKF algorithm can get better estimation accuracy and greater robustness for maneuvering target tracking.
Highlights
Maneuvering target tracking has drawn increasing attention because of its widespread application in areas such as radar tracking, aircrafts surveillance, and spacecraft orbit control [1,2].For maneuvering target tracking, many algorithms are developed and grouped into two types.One type is to improve the accuracy of the motion model, such as multiple-model (MM) methods [3], optimization of multiple model neural filter [4], current statistical (CS) model [5,6], and so on
The effectiveness of the proposed algorithm will be illustrated through two examples of maneuvering target tracking
The constant acceleration (CA) model is a common tool for tracking target modeling
Summary
Maneuvering target tracking has drawn increasing attention because of its widespread application in areas such as radar tracking, aircrafts surveillance, and spacecraft orbit control [1,2]. The other type is to detect the target maneuverability and to cope with it effectively, such as strong tracking filter (STF) [7], tracking algorithm based on maneuvering detection [8], and so on. To further improve estimation accuracy with low complexity, a new nonlinear filter named spherical simplex-radial cubature filter (SSRCKF) is developed in [20]. To tackle the problem mentioned above, a new algorithm called strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF) is proposed in this paper. Compared with the STF, strong tracking unscented Kalman filter (STUKF) [23], strong tracking cubature Kalman filter (STCKF) [24] and SSRCKF, the proposed algorithm has a good accuracy and robust advantage over a wide range of maneuver.
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