【摘要】：提出了一種簡(jiǎn)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(yīng)用于GPF時(shí)計(jì)算復(fù)雜度高、運(yùn)算量大的問題。該算法中,在連續(xù)的迭代濾波過程開始之前,首先利用QMC采樣產(chǎn)生單位擬高斯分布粒子集,然后用其線性變換產(chǎn)生GPF算法中需要的高斯分布粒子集,省去了重新進(jìn)行QMC采樣步驟。該算法簡(jiǎn)化了新粒子集的產(chǎn)生過程,減少了運(yùn)算量和濾波時(shí)間,增強(qiáng)了算法的實(shí)時(shí)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(tài)增長(zhǎng)模型(UNGM)和二維純角度跟蹤模型(BOT)的仿真結(jié)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(yōu)勢(shì),具有重要的實(shí)際應(yīng)用價(jià)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(xué)電子信息學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61401179)
【分類號(hào)】：TN713

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