Adaptive Sample-Size Unscented Particle Filter with Partitioned Sampling for Three-Dimensional High-Maneuvering Target Tracking

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.

Tracking of Multiple Closely Spaced Extended Targets Based on Prediction-Driven Measurement Sub-Partitioning Algorithm

“…Let c i 1 be the first alternative point p 1 . The other n i − 1 alternative points P n i −1 = {p 2 , p 3 , • • •, p n i } can be considered as the vertexes of a regular polygon, and their locations are calculated by Equation (3). Then, all alternative points…”

“…Target tracking techniques have been studied extensively with fruitful results and widely developed in both military and civil fields [1][2][3][4][5][6][7][8][9][10]. In traditional tracking applications, it is generally assumed that at most one measurement can be received by sensors per time and the target is modeled as a single point mass.…”

Maneuvering Star-Convex Extended Target Tracking Based on Modified ExpectedMode Augmentation Algorithm

“…Existing single motion models encompass constant acceleration (CA), constant velocity (CV) (Yunita et al 2020), constant turn (CT) (Amrouche et al 2018), Singer model (Zhu et al 2019), Jerk model (Deng et al 2019), and others. Nonetheless, a single model is often insufficient to describe the complex and variable kinematic behavior of targets in maneuvering scenarios.…”