模型转移概率自适应的交互式多模型跟踪算法
Interacting Multiple Model Algorithm with Adaptive Markov Transition Probabilities
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摘要: 该文利用量测中所包含的当前模式信息，实现了马尔可夫转移概率的实时估计，并将估计结果用于交互式多模型跟踪算法(IMM)的设计中，构造出参数自适应的交互多模型跟踪算法(PAIMM)，有效降低了人为因素的影响。通过一个跟踪机动目标的仿真实例，说明PAIMM算法的有效性。Abstract: A estimator of the time-varying Markov state transition probabilities is presented , which is based on the measurements. Then the Parameter Adaptive Interacting Multiple Model (PAIMM) is designed by adopting the above estimator. In comparison with that of the conventional IMM algorithm , the tracking performance of PAIMM is better in the simulation of tracking a maneuvering target.
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