WLAN位置指紋室內定位關鍵技術研究
發(fā)布時間：2018-09-09 09:00
【摘要】：移動智能終端的廣泛應用和無線網絡的快速普及和大量應用,使得基于位置服務(Location Based Services, LBS)的應用需求呈現(xiàn)出快速、大幅增長趨勢,LBS迅速發(fā)展和普及到了社會生活和生產的各個領域,并逐漸顯示出了良好的技術發(fā)展前景和巨大的應用市場空間。借助位置信息需求,定位技術與基于位置的服務的發(fā)展緊密地聯(lián)系在一起了。其中,可靠而高效的室內定位技術是實現(xiàn)LBS的前提和關鍵所在。 在已有的室內定位技術中大多需要額外的專用硬件設施,定位成本高,定位精度和覆蓋范圍受硬件條件的限制,不利于LBS在室內環(huán)境的應用和推廣�；跓o線局域網(Wireless Local Area Network, WLAN)和接收信號強度(Received Signal Strength, RSS)的室內定位技術,充分利用現(xiàn)有的WLAN公共基礎設施,無需任何其它專用設備,只需特定的定位軟件,即可通過移動智能終端實現(xiàn)定位。WLAN的室內定位技術因其較低的定位成本、且能滿足大多數(shù)室內應用對定位精度的需求,已經成為室內定位技術的首選。但是,隨著室內無線接入點的廣泛部署和智能終端設備的不斷增減,室內無線電傳播環(huán)境越來越復雜,RSS表現(xiàn)出高度的多變性和復雜性,這嚴重影響了基于RSS的WLAN指紋定位系統(tǒng)的定位精度,給基于WLAN的位置指紋室內定位技術帶來了全新的研究內容,也給研究工作者提出了更為艱難的挑戰(zhàn)。 本文對WLAN的基于RSS的位置指紋定位技術開展了較為深入地調查和研究。著眼實用,以LBS為研究應用背景,圍繞提高RSS信號的可信度這一關鍵問題,以提高室內定位的可靠性和有效性為研究目標,以基礎理論研究為主,采用軟件與硬件結合、仿真和實驗并重的研究方法,對WLAN指紋定位的定位區(qū)域聚類、AP選擇、RSS信號定位特征提取等主要技術環(huán)節(jié)進行了研究。主要貢獻歸納為以下幾點： ·調查研究了室內RSS信號的分布特點。為了更好地描述RSS信號分布,本文選取了四種典型的室內環(huán)境(普通住宅、辦公樓、教學樓和商場)進行信號收集,分析了人員、接收器方向以及樣本數(shù)量對RSS信號的影響。提出了一種基于改進的雙峰高斯模型(Improved Double-peak Gaussian Distribution, IDGD)定位算法。實驗證明,與傳統(tǒng)的基于直方圖和高斯模型的定位技術相比,在保證相同定位精度前提下,基于IDGD的定位算法可減少大約70%的樣本數(shù)量。因此IDGD算法可以大幅度地減少樣本數(shù)量,減少數(shù)據采集工作量,節(jié)約定位成本,提高系統(tǒng)的定位精度。 ·研究了大定位目標區(qū)域的聚類問題。在較大范圍的室內定位環(huán)境,RSS的統(tǒng)計特性變化更大,對于基于學習型定位算法來說,對整個定位區(qū)域進行學習,將增加算法復雜度,建立的定位模型不是最優(yōu)的,從而不利于提高系統(tǒng)的定位精度。若采用聚類算法,將大的定位目標區(qū)域劃分為若干個較小的定位子區(qū)域,然后在每個定位子區(qū)域建立區(qū)域定位模型,將降低計算復雜度,提高定位精度。本文針對已有的聚類分塊問題沒有考慮信號的相關性,從而導致分類精度不夠高的問題,提出了一種將RSS信號白化后再進行k-means聚類的算法。實驗表明,與k-means聚類算法相比,本文提出的聚類算法可平均提高3.7%的聚類準確度,更有利于降低系統(tǒng)計算復雜度,節(jié)約終端能耗,提高定位精度。 ·研究了接入點(Access Point, AP)選擇問題。來自不同AP的RSS信號所包含的信息量是不同的,在當前各個公共熱點高密度部署AP情況下,這種差異尤為明顯。因此并不是所有的AP提供的RSS信號都有利于定位,很多RSS可能受到各種各樣的噪聲影響,含有大量的冗余信息,不僅不會提高系統(tǒng)的定位精度,反而起到相反的作用。因此需要對RSS信號也就是AP的定位能力進行判別,篩選出最優(yōu)的AP集合用于定位。針對已有的AP選擇算法沒有考慮AP的查全率和查準率問題,本文基于信息熵理論,提出了基于信息增益權重的AP選擇算法。實驗表明,利用該算法優(yōu)化的AP定位子集合,更有利于去掉冗余的AP,提高定位算法的解算效率和定位精度。 ·研究了提取RSS信號的有效定位特征問題。采用特征提取算法提取RSS信號的定位特征,有利于去掉RSS信號包含的冗余信息,提高RSS信號的可信度。本文針對已有算法只考慮有效提取RSS的線性特征問題,提出了基于核函數(shù)的直接判別特征提取(KD-LDA)算法,該算法可充分利用RSS信號的非線性特征。實驗表明,聯(lián)合本文提出的聚類和AP選擇算法,采用學習機器支持向量回歸定位模型,可實現(xiàn)在1米內的定位精度置信概率達37.1%,最大誤差為4.12米。與傳統(tǒng)的定位算法相比,可顯著提高系統(tǒng)小誤差定位(1米內)的概率,縮小系統(tǒng)的定位誤差范圍,優(yōu)化系統(tǒng)定位性能。
[Abstract]:With the wide application of mobile intelligent terminal and the rapid popularization of wireless network, the application demand based on Location Based Services (LBS) has shown a rapid and substantial growth trend. LBS has rapidly developed and popularized to all fields of social life and production, and has gradually shown a good technical development prospects. Location technology is closely related to the development of location-based services with the help of location information requirements. Reliable and efficient indoor location technology is the premise and key to LBS.
Most of the existing indoor positioning technologies require additional dedicated hardware facilities. The cost of positioning is high. The positioning accuracy and coverage are limited by hardware conditions, which is not conducive to the application and promotion of LBS in indoor environment. Indoor positioning technology, which makes full use of existing WLAN public infrastructure, does not need any other special equipment, only needs specific positioning software to achieve positioning through mobile intelligent terminals. Because of its low cost of positioning, and can meet the needs of most indoor applications for positioning accuracy, WLAN indoor positioning technology has become indoor positioning. However, with the widespread deployment of indoor wireless access points and the continuous decrease of intelligent terminal equipment, indoor radio propagation environment is becoming more and more complex, RSS shows a high degree of variability and complexity, which seriously affects the positioning accuracy of WLAN fingerprint positioning system based on RSS, and gives indoor positioning of location fingerprint based on WLAN. Technology brings a whole new research content, and also poses a more difficult challenge for researchers.
In this paper, the location fingerprint positioning technology based on RSS in WLAN is investigated and studied in depth. With the practical point of view and LBS as the application background, the key problem of improving the reliability and validity of RSS signal is focused on to improve the reliability and validity of indoor positioning. Combined with simulation and experiment, the main technical links of WLAN fingerprint location, such as location region clustering, AP selection, RSS signal location feature extraction, are studied.
In order to describe the distribution of RSS signals better, this paper selects four typical indoor environments (ordinary residential buildings, office buildings, teaching buildings and shopping malls) for signal collection, and analyzes the effects of personnel, receiver direction and sample size on RSS signals. Experimental results show that IDGD algorithm can reduce the number of samples by about 70% compared with traditional localization techniques based on histogram and Gaussian model under the same localization accuracy. It can reduce data collection workload, save location cost and improve positioning accuracy of the system.
The clustering problem of large localization target area is studied. In a wide range of indoor localization environment, the statistical characteristics of RSS change much more. For learning localization algorithm, learning the entire localization area will increase the algorithm complexity, and the localization model is not optimal, which is not conducive to improving the positioning accuracy of the system. Clustering algorithm is used to divide the large localization target region into several smaller localization sub-regions, and then a regional localization model is established in each localization sub-region, which will reduce the computational complexity and improve the localization accuracy. The experimental results show that compared with K-means clustering algorithm, the clustering algorithm proposed in this paper can improve the clustering accuracy by an average of 3.7%, which is more conducive to reducing the system computational complexity, saving terminal energy consumption and improving the positioning accuracy.
Access Point (AP) selection is studied. RSS signals from different APs contain different amounts of information, especially in the current situation of high density deployment of AP in public hot spots. Because there is a lot of redundant information, it will not improve the positioning accuracy of the system, but will play the opposite role. Therefore, it is necessary to discriminate the positioning ability of RSS signal, that is, AP, and select the best AP set for positioning. In theory, an AP selection algorithm based on information gain weight is proposed. Experiments show that the optimized AP subset is more conducive to remove redundant AP and improve the algorithm efficiency and positioning accuracy.
This paper studies the problem of extracting the effective location features of RSS signals.Using the feature extraction algorithm to extract the location features of RSS signals is helpful to remove the redundant information contained in RSS signals and improve the reliability of RSS signals.Aiming at the problem that the existing algorithms only consider the effective extraction of the linear features of RSS signals,a direct discriminant feature based on kernel function is proposed. Experimental results show that the proposed clustering and AP selection algorithm combined with the learning machine support vector regression localization model can achieve a confidence probability of 37.1% and a maximum error of 4.12 meters within 1 meter. Compared with the traditional localization algorithm, the proposed algorithm can be significantly improved. Increase the probability of small error positioning (within 1 meter), reduce the range of positioning error, and optimize the positioning performance of the system.
【學位授予單位】：華東師范大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN925.93
本文編號：2231921
[Abstract]:With the wide application of mobile intelligent terminal and the rapid popularization of wireless network, the application demand based on Location Based Services (LBS) has shown a rapid and substantial growth trend. LBS has rapidly developed and popularized to all fields of social life and production, and has gradually shown a good technical development prospects. Location technology is closely related to the development of location-based services with the help of location information requirements. Reliable and efficient indoor location technology is the premise and key to LBS.
Most of the existing indoor positioning technologies require additional dedicated hardware facilities. The cost of positioning is high. The positioning accuracy and coverage are limited by hardware conditions, which is not conducive to the application and promotion of LBS in indoor environment. Indoor positioning technology, which makes full use of existing WLAN public infrastructure, does not need any other special equipment, only needs specific positioning software to achieve positioning through mobile intelligent terminals. Because of its low cost of positioning, and can meet the needs of most indoor applications for positioning accuracy, WLAN indoor positioning technology has become indoor positioning. However, with the widespread deployment of indoor wireless access points and the continuous decrease of intelligent terminal equipment, indoor radio propagation environment is becoming more and more complex, RSS shows a high degree of variability and complexity, which seriously affects the positioning accuracy of WLAN fingerprint positioning system based on RSS, and gives indoor positioning of location fingerprint based on WLAN. Technology brings a whole new research content, and also poses a more difficult challenge for researchers.
In this paper, the location fingerprint positioning technology based on RSS in WLAN is investigated and studied in depth. With the practical point of view and LBS as the application background, the key problem of improving the reliability and validity of RSS signal is focused on to improve the reliability and validity of indoor positioning. Combined with simulation and experiment, the main technical links of WLAN fingerprint location, such as location region clustering, AP selection, RSS signal location feature extraction, are studied.
In order to describe the distribution of RSS signals better, this paper selects four typical indoor environments (ordinary residential buildings, office buildings, teaching buildings and shopping malls) for signal collection, and analyzes the effects of personnel, receiver direction and sample size on RSS signals. Experimental results show that IDGD algorithm can reduce the number of samples by about 70% compared with traditional localization techniques based on histogram and Gaussian model under the same localization accuracy. It can reduce data collection workload, save location cost and improve positioning accuracy of the system.
The clustering problem of large localization target area is studied. In a wide range of indoor localization environment, the statistical characteristics of RSS change much more. For learning localization algorithm, learning the entire localization area will increase the algorithm complexity, and the localization model is not optimal, which is not conducive to improving the positioning accuracy of the system. Clustering algorithm is used to divide the large localization target region into several smaller localization sub-regions, and then a regional localization model is established in each localization sub-region, which will reduce the computational complexity and improve the localization accuracy. The experimental results show that compared with K-means clustering algorithm, the clustering algorithm proposed in this paper can improve the clustering accuracy by an average of 3.7%, which is more conducive to reducing the system computational complexity, saving terminal energy consumption and improving the positioning accuracy.
Access Point (AP) selection is studied. RSS signals from different APs contain different amounts of information, especially in the current situation of high density deployment of AP in public hot spots. Because there is a lot of redundant information, it will not improve the positioning accuracy of the system, but will play the opposite role. Therefore, it is necessary to discriminate the positioning ability of RSS signal, that is, AP, and select the best AP set for positioning. In theory, an AP selection algorithm based on information gain weight is proposed. Experiments show that the optimized AP subset is more conducive to remove redundant AP and improve the algorithm efficiency and positioning accuracy.
This paper studies the problem of extracting the effective location features of RSS signals.Using the feature extraction algorithm to extract the location features of RSS signals is helpful to remove the redundant information contained in RSS signals and improve the reliability of RSS signals.Aiming at the problem that the existing algorithms only consider the effective extraction of the linear features of RSS signals,a direct discriminant feature based on kernel function is proposed. Experimental results show that the proposed clustering and AP selection algorithm combined with the learning machine support vector regression localization model can achieve a confidence probability of 37.1% and a maximum error of 4.12 meters within 1 meter. Compared with the traditional localization algorithm, the proposed algorithm can be significantly improved. Increase the probability of small error positioning (within 1 meter), reduce the range of positioning error, and optimize the positioning performance of the system.
【學位授予單位】：華東師范大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN925.93
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