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Improved Interactive Multiple Models Based on Self-Adaptive Turn Model for Maneuvering Target Tracking

机译：基于自适应转向模型的改进的交互式多模型进行机动目标跟踪

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摘要

Tracking maneuvering target is a challenging problem and Interactive Multiple Model (IMM) is proved an effective solution for it. In multiple model, the constant turn model (CT) is usually used to describe the target's turning motion. However, fixed or partially adaptive turn angular rate μ is usually adopted in CT which leads to tracking accuracy decrease. In this paper, an improved interactive multiple model set based on self-adaptive CT model is proposed. In self-adaptive CT model, the value of the turn angular rate ω is calculated based on both x and y velocity instead of only one of them or fixed value. To verify the improvement, particle filter, which is proved an effective way to solve non Gaussian nonlinear problem, is used to track maneuvering target. The performance of the proposed multiple model set is verified in two different scenarios and compared to two widely used multiple model sets. Simulation results show that the proposed model set has better performance both in tracking accuracy and computational cost.
机译：跟踪机动目标是一个具有挑战性的问题，并证明了交互式多模型（IMM）是有效的解决方案。在多种模型中，常量转向模型（CT）通常用于描述目标的转向运动。然而，固定或部分自适应转弯角速率μ通常在CT中采用，这导致跟踪精度降低。本文提出了一种基于自适应CT模型的改进的交互式多模型组。在自适应CT模型中，基于X和Y速度计算旋转角速率ω的值而不是其中一个或固定值。为了验证改进，粒子滤波器被证明是解决非高斯非线性问题的有效方法，用于跟踪机动目标。所提出的多模型集的性能在两个不同的场景中验证，并与两个广泛使用的多模型集进行比较。仿真结果表明，所提出的模型集以跟踪精度和计算成本均具有更好的性能。

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