發(fā)布時間：2018-05-19 22:47

  本文選題：參數(shù)估計 + 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,而隨著人們對導(dǎo)航系統(tǒng)中高精度定位需求的日益增長,干擾抑制成為了研究熱點。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,其難點在于多徑干擾具有位置上的不相關(guān)性、時間上的不確定性,不能通過現(xiàn)有的差分技術(shù)來消除。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的發(fā)展趨勢,因此,本文重點研究了基于參數(shù)估計的多徑誤差抑制算法,旨在通過數(shù)據(jù)處理的方法估計多徑參數(shù),并依據(jù)所估參數(shù)重構(gòu)多徑信號,進而消除多徑干擾的影響,以此來達到抑制多徑誤差的目的。本文重點研究了高斯噪聲和非高斯噪聲下的多徑誤差抑制算法。擴展卡爾曼濾波(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的多徑估計算法仍存在如下問題:對初值敏感、在對非線性方程進行線性化過程中會產(chǎn)生截斷誤差,致使估計結(jié)果在真值附近具有較大的波動。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)的粒子濾波在進行參數(shù)估計時存在粒子枯竭的問題,致使新產(chǎn)生粒子的多樣性減少,降低了PF的參數(shù)估計精度。針對EKF存在對初值敏感、濾波結(jié)果波動較大的問題,本文提出一種基于PF和滑動平均EKF的多徑估計算法。該算法在運行的初始階段,首先利用PF估計多徑參數(shù),然后將得到的多徑參數(shù)的粗略估計值作為EKF的初始估計值,以解決EKF對初值敏感的問題。接著利用EKF進行算法的后續(xù)估計,并對EKF的估計結(jié)果進行滑動平均,最后將滑動平均后的濾波結(jié)果作為多徑參數(shù)的估計結(jié)果。仿真結(jié)果表明,改進后的多徑估計算法相比EKF和PF具有更優(yōu)的估計性能,可有效降低估計結(jié)果的波動幅度,同時克服了EKF對初值敏感的問題。針對標(biāo)準(zhǔn)粒子濾波存在的粒子枯竭問題,本文提出一種基于自適應(yīng)差分進化的粒子濾波(Adaptive Differential Evolution Particle Filter,ADE-PF)算法,該算法利用自適應(yīng)差分進化算法代替PF中的重采樣策略來產(chǎn)生新粒子,使粒子朝著狀態(tài)后驗概率密度函數(shù)的高似然區(qū)移動,同時提高了粒子的多樣性。所采用的ADE算法,通過一種非線性自適應(yīng)調(diào)節(jié)策略來自適應(yīng)地調(diào)整縮放因子和交叉因子,以提高改進PF中DE(Differential Evolution)優(yōu)化部分的尋優(yōu)能力。為了驗證該算法的有效性,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。通過仿真驗證了ADE-PF算法可克服標(biāo)準(zhǔn)PF存在的粒子枯竭問題;與PF、EKF和DE-PF相比,ADE-PF算法具有更優(yōu)的多徑估計性能。本文研究內(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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P228.4

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