【摘要】 粒子滤波算法由于其处理非线性非高斯的能力优势,目前应用领域非常广泛。然而粒子滤波中存在的粒子退化、样贫等问题同样不容忽视,针对这些问题提出了一种改进的重采样粒子滤波算法。该方法借鉴了部分分层重采样和残差重采样的思路,通过对粒子权值大中小分类,在兼顾粒子多样性的情况下用不同策略分层次复制三个集合样本,从而优化了重采样算法。最后通过与经典粒子滤波重采样算法和其他部分重采样(PR)算法相比,以一维非线性跟踪模(UNG)和二维纯角度跟踪模型(BOT)两个模型的仿真结果验证了所提算法的滤波性能和有效性。更多还原

【Abstract】 Particle filter is widely applied in many fields due to its ability of dealing with nonlinear and non-Gaussian problems. However, concerning some serious problems such as particle degradation and poverty in particle filtering, an improved resampling algorithm was proposed in the paper. The idea of method was based on partial stratified resampling and residual resampling, to classify particles by large, medium and small weights and replicate samples from three hierarchical sets with different strategies. The efficiency of algorithm was improved while maintaining diversity of particles. Finally through comparison with classic sequential importance sampling and resamplings and other partial resamplings, simulation results of UNG( Univariate Non-stationary Growth) and BOT( Bearings Only Tracking) models also verify the filtering performance and validity of the proposed algorithm in this paper.更多还原