本文關(guān)鍵詞： 粒子濾波 重要性函數(shù) 粒子蛻化 自適應(yīng)重采樣 電路設(shè)計(jì)　出處：《電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：近幾十年發(fā)展起來的一種基于蒙特卡羅思想實(shí)現(xiàn)非線性、非高斯系統(tǒng)濾波的粒子濾波算法,完全突破了傳統(tǒng)Kalman濾波理論框架,適用于任何能用狀態(tài)空間模型表示的非線性系統(tǒng),且精度可以逼近最優(yōu)估計(jì)。粒子濾波是統(tǒng)計(jì)模擬理論學(xué)科和現(xiàn)代信號(hào)處理之間的交叉學(xué)科,因此對(duì)其研究有著重要的理論意義和深遠(yuǎn)的實(shí)踐價(jià)值。其在雷達(dá)目標(biāo)跟蹤、語音信號(hào)增強(qiáng)、傳感器故障診斷、倒立擺動(dòng)控制系統(tǒng)、衛(wèi)星導(dǎo)航、經(jīng)濟(jì)學(xué)以及生物控制等領(lǐng)域都有著廣泛應(yīng)用前景。本文對(duì)粒子濾波的發(fā)展背景和意義做了概述,由狀態(tài)方程出發(fā),通過貝葉斯理論和蒙特卡洛采樣推導(dǎo)出粒子濾波的基本原理。根據(jù)標(biāo)準(zhǔn)粒子濾波分析現(xiàn)有粒子濾波存在著估計(jì)精度不高、粒子蛻化、重要函數(shù)難選擇、計(jì)算量大以及實(shí)時(shí)性差等缺點(diǎn)。本文結(jié)合擴(kuò)展卡爾曼粒子濾波和不敏卡爾曼粒子濾波結(jié)構(gòu),利用Gauss-Hermite卡爾曼作為重要函數(shù)的Gauss-Hermite卡爾曼粒子濾波算法。其算法復(fù)雜度低于不敏卡爾曼粒子濾波,而其性能則高于擴(kuò)展卡爾曼粒子濾波,它為粒子濾波的重要性函數(shù)提供了多一種選擇。研究了四種基本重采樣算法并把四種算法仿真比較,并對(duì)其不能完全拋棄較小粒子提出了改進(jìn)思路。根據(jù)自適應(yīng)重采樣算法,改進(jìn)得到了線性自適應(yīng)重采樣算法,這種算法雖然要犧牲估計(jì)精度卻非常高效的在硬件上實(shí)現(xiàn)。通過權(quán)值優(yōu)選思想,結(jié)合自適應(yīng)重采樣算法對(duì)其算法時(shí)間上進(jìn)行改進(jìn),避免了大量數(shù)據(jù)的排序的問題,降低其算法復(fù)雜度,但其性能接近原來的權(quán)值優(yōu)選算法。最后研究了粒子濾波的電路設(shè)計(jì),分析了粒子濾波電路設(shè)計(jì)存在著較大的計(jì)算量和實(shí)時(shí)性差的問題。通過避免權(quán)值歸一化處理,從而大大減少除法運(yùn)算。并通過電路的并行設(shè)計(jì)來解決實(shí)時(shí)性差的問題。電路設(shè)計(jì)中有效地把粒子電路設(shè)計(jì)分為獨(dú)立的采樣電路、權(quán)值計(jì)算電路設(shè)計(jì)和重采樣電路設(shè)計(jì)。通過粒子濾波電路設(shè)計(jì)得到電路設(shè)計(jì)主要誤差存在于指數(shù)運(yùn)算,實(shí)時(shí)性差的問題主要存在于重采樣算法中。對(duì)于如何提高粒子濾波硬件設(shè)計(jì)的性能提供了依據(jù)。
[Abstract]:In recent decades, a kind of particle filter algorithm based on Monte Carlo theory to realize nonlinear, non-#china_person0# system filter has completely broken through the traditional Kalman filter theoretical framework. It is suitable for any nonlinear system which can be represented by a state space model, and the precision can approach the optimal estimation. Particle filter is an interdisciplinary subject between statistical simulation theory and modern signal processing. Therefore, its research has important theoretical significance and far-reaching practical value. It is used in radar target tracking, speech signal enhancement, sensor fault diagnosis, inverted swing control system, satellite navigation. In this paper, the development background and significance of particle filter are summarized, starting from the equation of state. The basic principle of particle filter is deduced by Bayesian theory and Monte Carlo sampling. According to the standard particle filter analysis, the existing particle filter has low estimation accuracy, particle disintegration, and important functions are difficult to choose. This paper combines the extended Kalman particle filter and the unsensitive Kalman particle filter structure. The complexity of Gauss-Hermite Kalman particle filter algorithm is lower than that of unsensitive Kalman particle filter algorithm. Its performance is better than that of extended Kalman particle filter, which provides an additional choice for the importance function of particle filter. Four basic resampling algorithms are studied and compared with each other. An improved method is put forward for it can not completely abandon smaller particles. According to the adaptive resampling algorithm, the linear adaptive resampling algorithm is obtained. This algorithm is implemented in hardware efficiently at the expense of estimation accuracy. Through the idea of weight optimization, the algorithm is improved in time by combining with adaptive resampling algorithm. The algorithm complexity is reduced, but its performance is close to the original weight optimization algorithm. Finally, the circuit design of particle filter is studied. In this paper, the problems of large computational complexity and poor real-time performance in the design of particle filter circuit are analyzed, and the normalized processing of weight value is avoided. Thus greatly reduce the division operation, and through the parallel design of the circuit to solve the problem of poor real-time. In the circuit design, the particle circuit design is effectively divided into independent sampling circuit. Weight calculation circuit design and resampling circuit design. Through particle filter circuit design, the main error of circuit design exists in exponential operation. The problem of poor real-time performance is mainly found in the resampling algorithm, which provides a basis for improving the performance of particle filter hardware design.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN713

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 趙梅;張三同;朱剛;;輔助粒子濾波算法及仿真舉例[J];北京交通大學(xué)學(xué)報(bào);2006年02期

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本文編號(hào)：1481605