【摘要】：提出了一種簡化的擬蒙特卡洛-高斯粒子濾波(QMC-GPF)算法(SQMC-GPF),以解決將QMC方法應用于GPF時計算復雜度高、運算量大的問題。該算法中,在連續的迭代濾波過程開始之前,首先利用QMC采樣產生單位擬高斯分布粒子集,然后用其線性變換產生GPF算法中需要的高斯分布粒子集,省去了重新進行QMC采樣步驟。該算法簡化了新粒子集的產生過程,減少了運算量和濾波時間,增強了算法的實時性。將粒子濾波算法(PF)、GPF算法、QMC-GPF算法和SQMCGPF算法用于單變量非靜態增長模型(UNGM)和二維純角度跟蹤模型(BOT)的仿真結果表明,SQMC-GPF算法的濾波性能與QMC-GPF算法的濾波性能相近,但有更為明顯的速度優勢,具有重要的實際應用價值。
[Abstract]:A simplified quasi-Monte Carlo Gao Si particle filter (SQMC-GPF) algorithm is proposed to solve the problem of high computational complexity and large computational complexity when the QMC method is applied to GPF. In this algorithm, before the continuous iterative filtering process begins, the unit quasi-Gaussian distribution particle subset is generated by QMC sampling, and then the Gao Si distribution particle subset needed in GPF algorithm is generated by its linear transformation. The QMC sampling step is omitted. The algorithm simplifies the generation process of new particle subset, reduces the amount of computation and filtering time, and enhances the real-time performance of the algorithm. The particle filter algorithm (PF), GPF algorithm, QMC-GPF algorithm and SQMCGPF algorithm are applied to the simulation results of single variable non-static growth model (UNGM) and two-dimensional pure angle tracking model (BOT). The filtering performance of SQMC-GPF algorithm is similar to that of QMC-GPF algorithm, but it has more obvious speed advantages and has important practical application value.
【作者單位】： 江蘇科技大學電子信息學院;
【基金】：國家自然科學基金資助項目(61401179)
【分類號】：TN713

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1 萬某峰;趙長勝;;UKF濾波中蒙特卡洛采樣策略比較分析[J];測繪通報;2012年12期

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本文編號：2483895