【摘要】：隨著計算機技術(shù)和人工智能的快速發(fā)展,基于室內(nèi)定位服務的應用不斷增多。其中,基于地磁場的室內(nèi)定位技術(shù)因為無需外在設施、定位精度高等獨特的優(yōu)點逐漸成為研究熱點,粒子濾波算法被認為是基于地磁場的各種室內(nèi)定位算法中最有前景的算法之一。但是,現(xiàn)有的基于粒子濾波的室內(nèi)定位算法一方面普遍存在著重采樣之后粒子貧乏的問題,另一方面又受到行為模型的誤差影響而定位誤差大,系統(tǒng)可靠性不高。本文針對粒子濾波定位算法中存在的問題進行分析與研究,通過在重采樣算法、觀測模型以及行為模型等多個環(huán)節(jié)進行改進,提出了一個改進的粒子濾波算法,并且通過仿真實驗證明本文提出的定位算法誤差在1米左右。本文主要工作是:通過分析智能手機傳感器的測量特性,在利用不同手機測量構(gòu)建地磁場模型的實驗基礎(chǔ)上,提出以地磁變化率改進算法執(zhí)行過程中對地磁數(shù)據(jù)的處理;通過分析粒子權(quán)重對重采用過程的重要性,提出基于觀測路徑相似性的采樣方法,并采用相對誤差計算相似度;針對粒子濾波算法在重采樣之后粒子豐富性降低的問題,提出了一種改進的進化重采樣粒子濾波算法;通過分析行為模型誤差,提出了以粒子加權(quán)步長代替運動模型中的固定步長;通過分析定位目標朝向發(fā)生改變增加水平方向磁場噪聲的情況,提出了一種融合三維分量匹配模型和HV匹配模型的混合匹配模型;最后針對算法定位丟失定位目標的情況,提出了一種地磁匹配的解決方法。
[Abstract]:With the rapid development of computer technology and artificial intelligence, the application of indoor positioning service is increasing. Among them, indoor positioning technology based on geomagnetic field has gradually become a research hotspot because of its unique advantages such as high positioning accuracy and no need for external facilities. Particle filter algorithm is considered as one of the most promising indoor localization algorithms based on geomagnetic field. However, the existing indoor localization algorithms based on particle filter have the problem of poor particles after resampling, on the other hand, because of the error of behavior model, the localization error is large and the system reliability is not high. In this paper, the problems in particle filter localization algorithm are analyzed and studied. An improved particle filter algorithm is proposed by improving the resampling algorithm, observation model and behavior model. The simulation results show that the error of the proposed algorithm is about 1 meter. The main work of this paper is as follows: by analyzing the measurement characteristics of smart phone sensors and on the basis of the experiment of using different mobile phone measurements to construct geomagnetic field model, this paper puts forward the processing of geomagnetic data in the execution process of the improved algorithm of geomagnetic variation rate; By analyzing the importance of particle weight to the re-adoption process, a sampling method based on the similarity of observation path is proposed, and the similarity is calculated by using relative error. An improved evolutionary resampling particle filter algorithm is proposed to solve the problem of decreasing particle richness after resampling. By analyzing the behavior model error, the particle weighted step size is proposed to replace the fixed step size in the motion model. By analyzing the situation that the orientation of the target is changed to increase the horizontal magnetic field noise, a hybrid matching model is proposed, which combines the 3D component matching model and the HV matching model. Finally, a method of geomagnetic matching is proposed for locating lost targets.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN713

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