基于信息熵的WLAN室內(nèi)定位算法研究
發(fā)布時間：2018-12-13 11:14
【摘要】：無線局域網(wǎng)作為寬帶有線接入網(wǎng)的補充應(yīng)用越來越廣泛，同時也催生了以無線局域網(wǎng)為基礎(chǔ)的各類服務(wù)如WLAN室內(nèi)定位服務(wù)等。而基于位置指紋的WLAN室內(nèi)定位系統(tǒng)以其操作及設(shè)備簡單等特點而成為研究熱點。因而本文將基于位置指紋的WLAN室內(nèi)定位方法作為主要研究內(nèi)容，并通過改進該方法提高定位準確度和定位所需時間。 基于位置指紋的WLAN室內(nèi)定位一般分兩個階段：離線階段Radio Map的建立和在線定位階段。在離線階段，通過實測得到參考點的位置信息及相應(yīng)的RSS值形成Radio Map；在線階段使用特征匹配算法計算出在線測得數(shù)據(jù)的物理位置�；谖恢弥讣y的定位算法需要解決兩個問題：定位的準確性和時效性。因而本文研究了聚類算法、AP選擇算法及Radio Map更新算法。 首先，本文分析了現(xiàn)有的基于Radio Map的WLAN室內(nèi)定位的特點，根據(jù)其關(guān)鍵的兩個環(huán)節(jié)即Radio Map的建立及特征匹配算法進行分析。位置指紋的創(chuàng)建方法有兩種，即自由空間傳播模型法和接收到RSS值的特征值法，本文選用RSS特征值法。RSS值隨著時間，天線朝向，參考點位置變化而變化，因而需要選用合理的方法建立Radio Map。特征匹配算法中，包括最簡單的最近鄰算法、經(jīng)典的K近鄰算法和加權(quán)K近鄰算法。 其次，本文通過分析Radio Map，研究如何對Radio Map進行化簡及更新操作。為了定位的時效性，本文首先對Radio Map進行聚類處理，將RadioMap劃分為幾個小類，然后在每一個小類中使用AP選擇算法選擇出合適的AP組合用于定位。在聚類算法中，研究了最簡單的K均值聚類算法、引入隸屬度概念的模糊K均值聚類算法和無需指定初始聚類數(shù)的仿射傳播聚類算法；在AP選擇算法中，研究了隨機選擇及均值最大選擇AP方法、信息熵增益方法和互信息熵方法。最后，為定位的準確性，研究了基于隱馬爾科夫模型的Radio Map更新方法，，并使用EM算法對隱馬爾科夫模型進行求解。 最后，通過在真實環(huán)境下的實驗仿真，利用特征匹配算法進行定位。對聚類算法、AP選擇算法及Radio Map更新算法進行了性能分析，并基于實驗環(huán)境選擇了合適的參數(shù)以期達到定位準確度高及定位時間短的特點。
[Abstract]:WLAN is more and more widely used as a supplement to broadband wired access network. At the same time, WLAN services such as WLAN indoor positioning services are given birth to. The WLAN indoor positioning system based on position fingerprint has become a research hotspot because of its simple operation and equipment. Therefore, the WLAN indoor location method based on location fingerprint is taken as the main research content in this paper, and the accuracy and time of location are improved by improving the method. WLAN indoor location based on position fingerprint is generally divided into two stages: the establishment of Radio Map and the online location. In the off-line phase, the position information of the reference point and the corresponding RSS value are measured to form the Radio Map; online phase. The physical position of the on-line measured data is calculated by using the feature matching algorithm. The localization algorithm based on location fingerprint needs to solve two problems: accuracy and timeliness. Therefore, clustering algorithm, AP selection algorithm and Radio Map update algorithm are studied in this paper. Firstly, this paper analyzes the characteristics of existing WLAN indoor location based on Radio Map, and analyzes its two key links, namely, the establishment of Radio Map and the feature matching algorithm. There are two methods to create position fingerprint, that is, free space propagation model method and eigenvalue method that receives RSS value. In this paper, RSS eigenvalue method is used. The RSS value changes with time, antenna orientation and reference point position. Therefore, it is necessary to select a reasonable method to establish Radio Map.. The feature matching algorithms include the simplest nearest neighbor algorithm, the classical K nearest neighbor algorithm and the weighted K nearest neighbor algorithm. Secondly, this paper studies how to simplify and update Radio Map by analyzing Radio Map,. In order to get the timeliness of the localization, the Radio Map is first clustered, the RadioMap is divided into several subclasses, and then the appropriate AP combination is selected by using the AP selection algorithm in each subclass. In the clustering algorithm, the simplest K-means clustering algorithm is studied, the fuzzy K-means clustering algorithm based on membership degree and the affine propagation clustering algorithm without specifying the initial clustering number are introduced. In the AP selection algorithm, the random selection and the mean maximum selection AP method, the information entropy gain method and the mutual information entropy method are studied. Finally, for the accuracy of location, the Radio Map updating method based on Hidden Markov Model is studied, and the EM algorithm is used to solve the Hidden Markov Model. Finally, through the real-time simulation, the feature matching algorithm is used to locate the location. The performance of clustering algorithm, AP selection algorithm and Radio Map update algorithm are analyzed, and the suitable parameters are selected based on the experimental environment in order to achieve the characteristics of high localization accuracy and short localization time.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN925.93
本文編號：2376444
[Abstract]:WLAN is more and more widely used as a supplement to broadband wired access network. At the same time, WLAN services such as WLAN indoor positioning services are given birth to. The WLAN indoor positioning system based on position fingerprint has become a research hotspot because of its simple operation and equipment. Therefore, the WLAN indoor location method based on location fingerprint is taken as the main research content in this paper, and the accuracy and time of location are improved by improving the method. WLAN indoor location based on position fingerprint is generally divided into two stages: the establishment of Radio Map and the online location. In the off-line phase, the position information of the reference point and the corresponding RSS value are measured to form the Radio Map; online phase. The physical position of the on-line measured data is calculated by using the feature matching algorithm. The localization algorithm based on location fingerprint needs to solve two problems: accuracy and timeliness. Therefore, clustering algorithm, AP selection algorithm and Radio Map update algorithm are studied in this paper. Firstly, this paper analyzes the characteristics of existing WLAN indoor location based on Radio Map, and analyzes its two key links, namely, the establishment of Radio Map and the feature matching algorithm. There are two methods to create position fingerprint, that is, free space propagation model method and eigenvalue method that receives RSS value. In this paper, RSS eigenvalue method is used. The RSS value changes with time, antenna orientation and reference point position. Therefore, it is necessary to select a reasonable method to establish Radio Map.. The feature matching algorithms include the simplest nearest neighbor algorithm, the classical K nearest neighbor algorithm and the weighted K nearest neighbor algorithm. Secondly, this paper studies how to simplify and update Radio Map by analyzing Radio Map,. In order to get the timeliness of the localization, the Radio Map is first clustered, the RadioMap is divided into several subclasses, and then the appropriate AP combination is selected by using the AP selection algorithm in each subclass. In the clustering algorithm, the simplest K-means clustering algorithm is studied, the fuzzy K-means clustering algorithm based on membership degree and the affine propagation clustering algorithm without specifying the initial clustering number are introduced. In the AP selection algorithm, the random selection and the mean maximum selection AP method, the information entropy gain method and the mutual information entropy method are studied. Finally, for the accuracy of location, the Radio Map updating method based on Hidden Markov Model is studied, and the EM algorithm is used to solve the Hidden Markov Model. Finally, through the real-time simulation, the feature matching algorithm is used to locate the location. The performance of clustering algorithm, AP selection algorithm and Radio Map update algorithm are analyzed, and the suitable parameters are selected based on the experimental environment in order to achieve the characteristics of high localization accuracy and short localization time.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN925.93
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