【摘要】：隨著系統(tǒng)規(guī)模變大,系統(tǒng)的復(fù)雜度不斷增強(qiáng),原有的粒子濾波算法在系統(tǒng)參數(shù)、狀態(tài)估計(jì)以及目標(biāo)跟蹤方面存在不足,因此,設(shè)計(jì)新的粒子濾波算法,提高估計(jì)精度,減少計(jì)算復(fù)雜度顯得至關(guān)重要。 本文針對(duì)粒子濾波算法進(jìn)行改進(jìn),主要工作如下： 1.介紹了粒子濾波算法的基本原理以及標(biāo)準(zhǔn)粒子濾波算法的計(jì)算流程,同時(shí)分析了其存在的主要問(wèn)題,對(duì)并本文改進(jìn)算法中要用到的一些智能算法如：禁忌搜索、人工免疫以及權(quán)值選擇算法予以說(shuō)明。 2.在粒子濾波算法基礎(chǔ)上,針對(duì)粒子退化,樣本集多樣性低的問(wèn)題,設(shè)計(jì)出基于禁忌免疫的粒子濾波算法。該算法利用人工免疫算法的尋優(yōu)能力從眾多粒子中挑選好的粒子,提高了樣本集的多樣性,并且通過(guò)禁忌搜索回避搜索陷入局部最優(yōu)。利用該算法估計(jì)系統(tǒng)的參數(shù)和狀態(tài),并與人工免疫粒子濾波、標(biāo)準(zhǔn)粒子濾波算法進(jìn)行對(duì)比,驗(yàn)證算法的估計(jì)性能。 3.針對(duì)粒子濾波算法計(jì)算復(fù)雜度高的問(wèn)題,提出了基于權(quán)值選擇的邊緣化粒子濾波算法。該算法通過(guò)利其模型中的線(xiàn)性子結(jié)構(gòu)降低從標(biāo)準(zhǔn)粒子濾波算法中得到的估計(jì)方差,并能邊緣化處理相應(yīng)的線(xiàn)性狀態(tài)變量,同時(shí)能利用最優(yōu)線(xiàn)性濾波進(jìn)行估計(jì),從而降低了計(jì)算量。并且,粒子間的相互獨(dú)立性使得粒子集包含更多相異的粒子路徑,提升粒子集的多樣性,具有較好的優(yōu)化效果。 4.以城市軌道列車(chē)制動(dòng)模型為背景,將提出的兩種改進(jìn)算法用于列車(chē)制動(dòng)率以及列車(chē)運(yùn)行狀態(tài)的聯(lián)合估計(jì),對(duì)兩種改進(jìn)算法進(jìn)行了仿真對(duì)比。
[Abstract]:With the scale of the system becoming larger and the complexity of the system increasing, the original particle filter algorithm has some shortcomings in system parameters, state estimation and target tracking. Therefore, a new particle filter algorithm is designed to improve the estimation accuracy. It is very important to reduce computational complexity. In this paper, the particle filter algorithm is improved, the main work is as follows: 1. This paper introduces the basic principle of particle filter algorithm and the calculation flow of standard particle filter algorithm, analyzes its main problems, and improves some intelligent algorithms used in the algorithm, such as Tabu search, Tabu search, etc. Artificial immune and weight selection algorithm to explain. 2. On the basis of particle filter algorithm, aiming at the problem of particle degradation and low diversity of sample set, a particle filter algorithm based on Tabu immunity is designed. The algorithm uses the optimization ability of artificial immune algorithm to select good particles from many particles, which improves the diversity of sample set and falls into local optimum through Tabu search avoidance search. The algorithm is used to estimate the parameters and states of the system, and compared with the artificial immune particle filter and the standard particle filter algorithm, the estimation performance of the algorithm is verified. Aiming at the problem of high computational complexity of particle filter algorithm, an edge particle filter algorithm based on weight selection is proposed. The algorithm can reduce the estimated variance obtained from the standard particle filter algorithm by using the linear substructure in the model, and can marginalize the corresponding linear state variables, and at the same time, it can use the optimal linear filter to estimate. Thus, the calculation amount is reduced. Moreover, the mutual independence of particles makes the particle set contain more different particle paths, improve the diversity of particle sets, and have a better optimization effect. 4. Based on the braking model of urban rail train, the two improved algorithms are applied to the joint estimation of train braking rate and train running state, and the two improved algorithms are simulated and compared.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN713;TP18

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