A Multiple Model Particle Filter for Maneuvering Target Tracking Based on Composite Sampling
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摘要: 提出了一种新型的基于混合采样的多模型粒子滤波算法,该算法能够有效降低多模型粒子滤波器的采样粒子数. 文中证明了这种基于混合采样的粒子滤波算法是一种多模型粒子滤波算法. 该算法的计算复杂度与单模型粒子滤波算法相当. 仿真实验表明,与已有的多模型粒子滤波算法相比,算法的计算复杂度大幅降低.Abstract: A novel multiple model particle filter based on composite sampling is presented, which can decrease the number of particles required in the multiple model particle filter. It is proved that the algorithm based on composite sampling is a kind of multiple model particle filter. The computational complexity of the algorithm is similar to that of single model particle filter. Simulation shows that compared with the existing multiple model particle filter, the computational complexity of the proposed filter is decreased greatly.
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