Maneuvering Target Tracking Algorithm Based on Improved Truncation Gaussian Probability Model
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摘要: 机动目标模型是水下目标跟踪的关键,在分析截断正态概率密度模型的基础上,利用前2个时刻的观测值与状态估计偏差,对该模型机动加速度与其方差的自适应关系重新进行修正,给出了一种基于改进截断正态概率密度模型的机动目标跟踪算法。该算法避免了加速度极限值的预先设定问题,从而提高了对机动目标状态估计的精度。仿真结果表明,该算法在跟踪水下机动目标时具有良好的跟踪性能。
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关键词:
- 目标跟踪算法 /
- 自适应滤波 /
- 截断正态概率密度模型
Abstract: Maneuvering target model is a key element in underwater target tracking. Based on truncation Gaussian probability model for underwater target tracking, an improved maneuvering target tracking algorithm is presented in this paper. The algorithm utilizes the difference between observation value and state estimation of previous two sample positions to update the model′s self-adaptation of maneuvering acceleration and its variance, and avoids adverse influence of acceleration limit presupposition. Simulation result shows that this algorithm has good performance of tracking underwater maneuvering target with higher accuracy. -
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