Abstract
High-maneuvering target tracking is a focused application area in radar positioning and military defense systems, especially in three-dimensional space. However, using a traditional motion model and techniques expanded from general two-dimensional maneuvering target tracking may be inaccurate and impractical in some mission-critical systems. This paper proposes an adaptive sample-size unscented particle filter with partitioned sampling (PS-AUPF), which is used to track a three-dimensional, high-maneuvering target, combined with the CS-jerk model. In PS-AUPF, the partitioned sampling is introduced to improve the resampling and predicting process by decomposing motion space. At the same time, the adaptive sample size strategy is used to adjust the sample size adaptively in the tracking process, according to the initial parameters and the estimated state variance of each time step. Finally, the effectiveness of this method is validated by simulations, in which the sample size of each algorithm is set to the minimum required for the optimal accuracy, thus ensuring the reliability of the tracking results. The results have shown that the proposed PS-AUPF, with higher accuracy and lower computational complexity, performs better than other existing tracking methods in three-dimensional high-maneuvering target tracking scenarios.
Highlights
Maneuvering target tracking is a fundamental and critical task in many practical applications with a wide range of military and civil backgrounds [1,2]
We have proposed a novel adaptive sample-size unscented particle filter with partitioned sampling to track a high-maneuvering target in three-dimensional motion space
Aiming at the high maneuver of a target, the CS-jerk model was introduced as the motion model to match the target-changing motion by its self-adaptive modification, and to facilitate subsequent processing, the measurement model with coordinate transformation was further introduced to convert the coordinate of measurements obtained by sensors
Summary
Maneuvering target tracking is a fundamental and critical task in many practical applications with a wide range of military and civil backgrounds [1,2]. Due to the inconsistency of maneuverability in all directions in three-dimensional space, the target motion cannot be accurately described by the expansion of a general two-dimensional model and method, especially for the high-speed and high-maneuvering targets. For high-maneuvering target tracking, the establishment of a motion model is the primary task. The multiple-model (MM) methods [7,8,9,10] are usually adopted because of their fine modeling. Such methods need to add as many motion models as possible to ensure the completeness of the model set, which will lead to a significant increase in computational complexity and unnecessary competition among similar models Three-dimensional high-maneuvering target tracking has become one of the difficulties in this kind of problem [3,4,5,6].
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