【摘要】：隨著系統(tǒng)規(guī)模變大,系統(tǒng)的復(fù)雜度不斷增強,原有的粒子濾波算法在系統(tǒng)參數(shù)、狀態(tài)估計以及目標(biāo)跟蹤方面存在不足,因此,設(shè)計新的粒子濾波算法,提高估計精度,減少計算復(fù)雜度顯得至關(guān)重要。 本文針對粒子濾波算法進行改進,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)粒子濾波算法的計算流程,同時分析了其存在的主要問題,對并本文改進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)值選擇算法予以說明。 2.在粒子濾波算法基礎(chǔ)上,針對粒子退化,樣本集多樣性低的問題,設(shè)計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,并且通過禁忌搜索回避搜索陷入局部最優(yōu)。利用該算法估計系統(tǒng)的參數(shù)和狀態(tài),并與人工免疫粒子濾波、標(biāo)準(zhǔn)粒子濾波算法進行對比,驗證算法的估計性能。 3.針對粒子濾波算法計算復(fù)雜度高的問題,提出了基于權(quán)值選擇的邊緣化粒子濾波算法。該算法通過利其模型中的線性子結(jié)構(gòu)降低從標(biāo)準(zhǔn)粒子濾波算法中得到的估計方差,并能邊緣化處理相應(yīng)的線性狀態(tài)變量,同時能利用最優(yōu)線性濾波進行估計,從而降低了計算量。并且,粒子間的相互獨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.以城市軌道列車制動模型為背景,將提出的兩種改進算法用于列車制動率以及列車運行狀態(tài)的聯(lián)合估計,對兩種改進算法進行了仿真對比。
[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)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713;TP18

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