本文選題：參數(shù)估計(jì) + 粒子濾波��； 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：全球定位系統(tǒng)(Global Position System,GPS)在人們的生活中被廣泛應(yīng)用,而隨著人們對(duì)導(dǎo)航系統(tǒng)中高精度定位需求的日益增長(zhǎng),干擾抑制成為了研究熱點(diǎn)。在眾多影響定位精度的因素中,多徑干擾是降低定位精度的主要誤差源之一。抑制多徑干擾造成的誤差,其難點(diǎn)在于多徑干擾具有位置上的不相關(guān)性、時(shí)間上的不確定性,不能通過(guò)現(xiàn)有的差分技術(shù)來(lái)消除。而基于數(shù)據(jù)處理的多徑誤差抑制方法符合目前軟件接收機(jī)的發(fā)展趨勢(shì),因此,本文重點(diǎn)研究了基于參數(shù)估計(jì)的多徑誤差抑制算法,旨在通過(guò)數(shù)據(jù)處理的方法估計(jì)多徑參數(shù),并依據(jù)所估參數(shù)重構(gòu)多徑信號(hào),進(jìn)而消除多徑干擾的影響,以此來(lái)達(dá)到抑制多徑誤差的目的。本文重點(diǎn)研究了高斯噪聲和非高斯噪聲下的多徑誤差抑制算法。擴(kuò)展卡爾曼濾波(Extended Kalman Filter,EKF)和粒子濾波(Particle Filter,PF)分別是高斯噪聲和非高斯噪聲下用于多徑估計(jì)的兩種典型算法,取得了很多研究者的關(guān)注。但在高斯噪聲下,基于EKF的多徑估計(jì)算法仍存在如下問(wèn)題:對(duì)初值敏感、在對(duì)非線性方程進(jìn)行線性化過(guò)程中會(huì)產(chǎn)生截?cái)嗾`差,致使估計(jì)結(jié)果在真值附近具有較大的波動(dòng)。在非高斯噪聲下,PF算法雖然應(yīng)用廣泛,濾波效果較好,但標(biāo)準(zhǔn)的粒子濾波在進(jìn)行參數(shù)估計(jì)時(shí)存在粒子枯竭的問(wèn)題,致使新產(chǎn)生粒子的多樣性減少,降低了PF的參數(shù)估計(jì)精度。針對(duì)EKF存在對(duì)初值敏感、濾波結(jié)果波動(dòng)較大的問(wèn)題,本文提出一種基于PF和滑動(dòng)平均EKF的多徑估計(jì)算法。該算法在運(yùn)行的初始階段,首先利用PF估計(jì)多徑參數(shù),然后將得到的多徑參數(shù)的粗略估計(jì)值作為EKF的初始估計(jì)值,以解決EKF對(duì)初值敏感的問(wèn)題。接著利用EKF進(jìn)行算法的后續(xù)估計(jì),并對(duì)EKF的估計(jì)結(jié)果進(jìn)行滑動(dòng)平均,最后將滑動(dòng)平均后的濾波結(jié)果作為多徑參數(shù)的估計(jì)結(jié)果。仿真結(jié)果表明,改進(jìn)后的多徑估計(jì)算法相比EKF和PF具有更優(yōu)的估計(jì)性能,可有效降低估計(jì)結(jié)果的波動(dòng)幅度,同時(shí)克服了EKF對(duì)初值敏感的問(wèn)題。針對(duì)標(biāo)準(zhǔn)粒子濾波存在的粒子枯竭問(wèn)題,本文提出一種基于自適應(yīng)差分進(jìn)化的粒子濾波(Adaptive Differential Evolution Particle Filter,ADE-PF)算法,該算法利用自適應(yīng)差分進(jìn)化算法代替PF中的重采樣策略來(lái)產(chǎn)生新粒子,使粒子朝著狀態(tài)后驗(yàn)概率密度函數(shù)的高似然區(qū)移動(dòng),同時(shí)提高了粒子的多樣性。所采用的ADE算法,通過(guò)一種非線性自適應(yīng)調(diào)節(jié)策略來(lái)自適應(yīng)地調(diào)整縮放因子和交叉因子,以提高改進(jìn)PF中DE(Differential Evolution)優(yōu)化部分的尋優(yōu)能力。為了驗(yàn)證該算法的有效性,分別在高斯噪聲環(huán)境和非高斯噪聲環(huán)境下,將所提出的ADE-PF算法應(yīng)用于多徑估計(jì)。通過(guò)仿真驗(yàn)證了ADE-PF算法可克服標(biāo)準(zhǔn)PF存在的粒子枯竭問(wèn)題;與PF、EKF和DE-PF相比,ADE-PF算法具有更優(yōu)的多徑估計(jì)性能。本文研究?jī)?nèi)容為山西省自然科學(xué)基金(No.2014021022-7)的重要組成部分,為高斯噪聲和非高斯噪聲下的多徑誤差抑制算法研究提供了參考,對(duì)提高導(dǎo)航系統(tǒng)的定位精度有重要的理論意義和廣泛的應(yīng)用前景。
[Abstract]:Global Position System (GPS) is widely used in the people's life. With the increasing demand for high precision positioning in the navigation system, interference suppression has become a hot topic. Among the many factors affecting the positioning accuracy, multipath interference is one of the main error sources to reduce the positioning accuracy. The difficulty of the error caused by interference is that the multipath interference is not related to the position, the time is uncertain and can not be eliminated by the existing differential technology. And the method of multipath error suppression based on data processing is in line with the development trend of the current software receiver. Therefore, this paper focuses on the multipath error based on parameter estimation. The difference suppression algorithm is designed to estimate multipath parameters by data processing, and to reconstruct multipath signals based on the estimated parameters and eliminate the influence of multipath interference in order to suppress multipath error. This paper focuses on the multi-path error suppression algorithm under Gauss noise and non Gauss noise. Extended Calman filter (Extended K). Alman Filter, EKF) and particle filter (Particle Filter, PF) are two typical algorithms for multipath estimation under Gauss noise and non Gauss noise, which have obtained many researchers' attention. But under Gauss noise, the following problems still exist in the multipath estimation algorithm based on EKF, which is sensitive to the initial value and linearized the nonlinear equation. There will be a truncation error in the process, which leads to the larger fluctuation of the estimated results near the true value. Under the non Gauss noise, the PF algorithm is widely used and the filtering effect is good, but the standard particle filter has the problem of particle exhaustion in the parameter estimation, which reduces the diversity of the newly generated particles and reduces the estimation accuracy of the parameters of the PF. In this paper, a multipath estimation algorithm based on PF and sliding mean EKF is proposed to solve the problem that the EKF is sensitive to the initial value and the filtering results are very volatile. In the initial stage of the operation, the algorithm first uses PF to estimate the multipath parameters and then the rough estimation value of the obtained multipath parameters as the initial value of the EKF, so as to solve the initial value sensitivity of the EKF. Then the EKF is used to carry out the subsequent estimation of the algorithm, and the estimation results of the EKF are sliding averaging. Finally, the filtering results after the sliding average are used as the estimation results of the multipath parameters. The simulation results show that the improved multipath estimation algorithm has a better estimation performance than the EKF and the PF, and can effectively reduce the fluctuation of the estimated results. At the same time, the problem of EKF sensitivity to the initial value is overcome. Aiming at the problem of particle exhaustion in the standard particle filter, an adaptive differential evolution based particle filter (Adaptive Differential Evolution Particle Filter, ADE-PF) algorithm is proposed. The algorithm uses the self adaptive differential evolution algorithm to replace the resampling strategy in PF. New particles are generated to move the particle to the high likelihood region of the posterior probability density function and improve the diversity of the particles. The ADE algorithm used to adjust the scaling factor and the cross factor adaptively by a nonlinear adaptive adjustment strategy to improve the optimization of the optimized part of the improved PF (DE) (Differential Evolution). Ability. In order to verify the effectiveness of the algorithm, the proposed ADE-PF algorithm is applied to multipath estimation in Gauss noise environment and non Gauss noise environment. It is verified by simulation that the ADE-PF algorithm can overcome the problem of particle exhaustion in standard PF; compared with PF, EKF and DE-PF, the ADE-PF algorithm has a better performance of multipath estimation. The research content is an important part of the natural science foundation of Shanxi (No.2014021022-7). It provides a reference for the study of the multipath error suppression algorithm under Gauss noise and non Gauss noise. It has important theoretical significance and wide application foreground for improving the positioning accuracy of the navigation system.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P228.4

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王林;林雪原;孫煒瑋;王萌;;改進(jìn)粒子濾波算法及其在GPS/SINS組合導(dǎo)航中的應(yīng)用[J];海軍航空工程學(xué)院學(xué)報(bào);2016年01期

2 呂銘晟;沈洪遠(yuǎn);李志高;王汐;龔明;王俊年;;多變異策略差分進(jìn)化算法的研究與應(yīng)用[J];計(jì)算機(jī)工程;2014年12期

3 張才千;葛磊;韓東;;基于目標(biāo)跟蹤的粒子群粒子濾波算法研究[J];計(jì)算機(jī)仿真;2014年08期

4 楊鵬生;吳曉軍;張玉梅;;改進(jìn)擴(kuò)展卡爾曼濾波算法的目標(biāo)跟蹤算法[J];計(jì)算機(jī)工程與應(yīng)用;2016年05期

5 CHENG Lan;CHEN Jie;XIE Gang;;Model and Simulation of Multipath Error in DLL for GPS Receiver[J];Chinese Journal of Electronics;2014年03期

6 鄭南山;蔡良師;卞和方;林聰;;GPS多路徑減輕的混合粒子濾波算法(英文)[J];Transactions of Nonferrous Metals Society of China;2014年05期

7 高源;張磊;龍騰;;衛(wèi)星導(dǎo)航信號(hào)可變間距采樣的多徑估計(jì)方法[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年04期

8 席志紅;付存利;;一種基于UPF的改進(jìn)粒子濾波算法[J];計(jì)算機(jī)仿真;2014年02期

9 程蘭;陳杰;謝剛;;軟件接收機(jī)中基于數(shù)據(jù)處理的多徑估計(jì)方法[J];系統(tǒng)工程與電子技術(shù);2013年10期

10 陳杰;程蘭;甘明剛;;基于高斯和近似的擴(kuò)展切片高斯混合濾波器及其在多徑估計(jì)中的應(yīng)用[J];自動(dòng)化學(xué)報(bào);2013年01期

相關(guān)會(huì)議論文 前1條

1 程蘭;陳杰;甘明剛;;GPS接收機(jī)載波跟蹤多徑誤差分析[A];第二十九屆中國(guó)控制會(huì)議論文集[C];2010年

相關(guān)博士學(xué)位論文 前1條

1 李敏;衛(wèi)星導(dǎo)航接收機(jī)數(shù)字波束形成關(guān)鍵技術(shù)研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2011年

相關(guān)碩士學(xué)位論文 前1條

1 蔡?hào)|亮;基于最小二乘法的狀態(tài)估計(jì)算法研究[D];山東大學(xué);2006年

，

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