In this letter, we propose a new approach to tracking a target that maneuvers based on the multiple-constant-turns model. Usually, the interactive-multiple-model (IMM) algorithm based on the extended Kalman filter (IMM-EKF) is employed for this problem with successful tracking performance. Recently proposed IMM-particle filtering (IMM-PF) showed outperforming results over IMM-EKF for this nonlinear problem. The proposed approach in this letter is a new framework of PF that adopts the minimax strategy to IMM-PF. The minimax strategy results in the decreased variance of the weights of particles that provides the robustness against the degeneracy phenomenon (a common problem of generic PF). In this letter, we show outperforming results by IMM-minimax-PF over IMM-PF besides the IMM-EKF in terms of estimation accuracy and computational complexity.

中文翻译：

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基于极小极大粒子滤波的交互多模型算法

在这封信中，我们提出了一种基于多恒定转弯模型来跟踪机动目标的新方法。通常，基于扩展卡尔曼滤波器 (IMM-EKF) 的交互式多模型 (IMM) 算法用于解决此问题，并具有成功的跟踪性能。最近提出的 IMM 粒子滤波 (IMM-PF) 在这个非线性问题上表现出优于 IMM-EKF 的结果。这封信中提出的方法是一种新的PF框架，它对IMM-PF采用了minimax策略。极小极大策略导致粒子权重的方差减小，从而提供了对简并现象（通用 PF 的常见问题）的鲁棒性。在这封信中，除了 IMM-EKF 之外，我们还展示了 IMM-minimax-PF 在估计精度和计算复杂度方面优于 IMM-PF 的结果。