【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)行業(yè)的高速發(fā)展,人們對(duì)于室內(nèi)定位的需求日益迫切,作為位置信息服務(wù)最重要的組成部分,室內(nèi)定位技術(shù)逐漸受到越來越多的商業(yè)關(guān)注。由于技術(shù)的進(jìn)步,人們對(duì)于室內(nèi)定位的精度要求也越來越高,但是由于室內(nèi)環(huán)境的反射和多徑傳播,高精度室內(nèi)定位問題一直是定位領(lǐng)域的最大難題。論文以信息柔性融合技術(shù)為基礎(chǔ),主要研究如何在復(fù)雜室內(nèi)環(huán)境下得到良好的室內(nèi)定位結(jié)果。論文所做的主要工作如下:(1)論文對(duì)室內(nèi)定位技術(shù)的研究現(xiàn)狀進(jìn)行了總結(jié),剖析了各種室內(nèi)定位技術(shù)的優(yōu)缺點(diǎn)和技術(shù)難點(diǎn)�；诳死懒_界,分析了TOA定位算法的理論極限,并推導(dǎo)了基站噪聲特性不一致時(shí)TOA算法的CRLB。針對(duì)制約室內(nèi)定位精度提高的瓶頸問題,設(shè)計(jì)了基于信息柔性融合的室內(nèi)定位系統(tǒng)框架與結(jié)構(gòu)。(2)針對(duì)信息柔性融合的數(shù)據(jù)層融合算法,分別研究了單/多傳感器條件下的定位參數(shù)估計(jì)、多定位參數(shù)融合定位等問題。在此基礎(chǔ)上提出了一種基于RSSI/AOA的新型室內(nèi)精確定位方法,仿真和測(cè)試結(jié)果顯示本文所給算法可以有效濾除參數(shù)測(cè)量波動(dòng)造成定位結(jié)果誤差,有效提高了室內(nèi)復(fù)雜環(huán)境下的目標(biāo)定位精度。(3)建立了基于強(qiáng)跟蹤Kalman濾波器的決策層融合算法。解決Kalman濾波器在運(yùn)動(dòng)目標(biāo)定位跟蹤方面的缺陷:對(duì)突變狀態(tài)的跟蹤能力差,對(duì)于大噪聲的濾除能力弱。改進(jìn)強(qiáng)跟蹤算法中的次優(yōu)漸消因子,根據(jù)前次結(jié)果對(duì)強(qiáng)跟蹤結(jié)果做反饋,形成了基于指數(shù)漸消因子的融合跟蹤算法(EFF-STF)。詳細(xì)說明了算法的實(shí)現(xiàn)步驟,建立系統(tǒng)實(shí)際采集數(shù)據(jù)驗(yàn)證了算法的有效性,并和相同條件下和卡爾曼濾波性能對(duì)比驗(yàn)證了算法在大噪聲濾除方面的優(yōu)越性,通過跟蹤前后的定位誤差CDF圖驗(yàn)證了跟蹤性能對(duì)定位誤差的良好修正作用。(4)論文利用MATLAB和VS2010聯(lián)合編程,實(shí)現(xiàn)了基于低功耗藍(lán)牙的室內(nèi)定位系統(tǒng),將定位結(jié)果可視化,驗(yàn)證了算法的性能,實(shí)驗(yàn)測(cè)試表明經(jīng)過信息柔性融合和跟蹤處理之后,可以將85%的定位誤差約束在1m以內(nèi),實(shí)現(xiàn)了室內(nèi)實(shí)時(shí)精確定位的需求。
[Abstract]:With the rapid development of mobile Internet industry, the demand for indoor positioning becomes more and more urgent. As the most important part of location information service, indoor positioning technology has attracted more and more commercial attention. Due to the progress of technology, the accuracy of indoor positioning is more and more demanding. However, due to the reflection and multipath propagation of indoor environment, the problem of high-precision indoor positioning has always been the biggest problem in the field of positioning. Based on information flexible fusion technology, this paper mainly studies how to obtain good indoor positioning results in complex indoor environment. The main work of this paper is as follows: (1) the research status of indoor positioning technology is summarized, and the advantages and disadvantages and technical difficulties of various indoor positioning techniques are analyzed. Based on the Caramello bound, the theoretical limit of the TOA localization algorithm is analyzed, and the CRLB. of the TOA algorithm is derived when the noise characteristics of the base station are inconsistent. In order to solve the bottleneck problem which restricts the improvement of indoor positioning accuracy, the frame and structure of indoor positioning system based on information flexible fusion are designed. (2) the data layer fusion algorithm for information flexible fusion is proposed. The estimation of location parameters and fusion localization of multi-location parameters under single / multi-sensor conditions are studied respectively. On this basis, a new precise indoor location method based on RSSI/AOA is proposed. The simulation and test results show that the proposed algorithm can effectively filter the error caused by parameter measurement fluctuation. The accuracy of target location in complex indoor environment is improved effectively. (3) A decision-level fusion algorithm based on strong tracking Kalman filter is proposed. The defects of Kalman filter in moving target location and tracking are solved: poor tracking ability for abrupt state and weak filtering ability for large noise. A fusion tracking algorithm based on exponential fading factor (EFF-STF) is developed by improving the suboptimal fading factor in the strong tracking algorithm and feedback the strong tracking results according to the previous results. The implementation steps of the algorithm are described in detail, and the validity of the algorithm is verified by the establishment of the system's actual data collection, and the superiority of the algorithm in large noise filtering is verified by comparing the performance of the algorithm with the Kalman filter under the same conditions. The positioning error CDF diagram before and after tracking verifies the good correction effect of tracking performance on the positioning error. (4) in this paper, the indoor positioning system based on low power Bluetooth is realized by using MATLAB and VS2010 joint programming, and the positioning results are visualized. The performance of the algorithm is verified. The experimental results show that after information flexible fusion and tracking processing, 85% of the positioning error can be limited to less than 1m, and the requirement of real-time and accurate indoor positioning can be realized.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN713;TN925

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