多天線定位算法研究
發(fā)布時間：2018-08-17 10:59
【摘要】：基于Wi-Fi的室內(nèi)定位常采用RSSI作為定位參量,而RSSI受環(huán)境和硬件設備的影響大。因此,本論文采用能有效的表征定位點頻率和空間特征的CSI作為定位參量和機器學習算法中的kNN算法作為定位算法,來實現(xiàn)更為準確的室內(nèi)定位。本文主要研究內(nèi)容如下:(1)本文研究了Wi-Fi室內(nèi)定位采用的定位方法,包括現(xiàn)有的室內(nèi)定位估計方法和定位算法。在分析定位估計方法時,比較了在Wi-Fi室內(nèi)定位中使用RSSI與CSI作為定位參量的優(yōu)劣,給出本論文采用CSI作為定位參量的原因。在確定使用CSI作為定位參量后,研究和改進了基于CSI的定位估計算法。(2)在定位算法的選擇上,不同于傳統(tǒng)的三角質(zhì)心法、雙曲線法和最小二乘法這三種算法,介紹了機器學習算法中的kNN算法和Bayes算法,并對其在基于CSI時的定位性能進行了實驗分析。實驗結果表明,kNN算法的定位性能優(yōu)于Bayes算法。其中,定位性能的評估包括平均定位誤差和誤差累計分布函數(shù)CDF。(3)建立離線階段訓練指紋庫,這對系統(tǒng)的定位效果有著重要的影響。在CSI信號采集后,對CSI數(shù)據(jù)不同的處理方式和定位參量的提取是建立指紋庫的重點。因此,本文提出了不同的算法對獲取的CSI進行處理并采用PCA對CSI數(shù)據(jù)進行降維得到新的定位參量。對以上兩種定位估計算法進行了比較分析,結果表明,這兩種算法都較傳統(tǒng)的處理方式具有更優(yōu)的定位效果。同時,采用PCA處理后得到的CSI特征值作為定位參量時的定位性能達到最優(yōu)。通過仿真工具和實驗平臺,討論不同實驗環(huán)境以及不同訓練數(shù)據(jù)對最終定位性能的影響。本論文在理論研究的基礎上,利用Matlab分析在不同定位算法和定位參量時,各個因素對定位性能的影響。最終實驗結果表明,平均定位精度在論文給定實驗條件下可以達到0.863m的定位精度,較傳統(tǒng)基于CSI的算法提高了20%。
[Abstract]:RSSI is often used as the positioning parameter in indoor positioning based on Wi-Fi, and RSSI is greatly affected by environment and hardware equipment. Therefore, in this paper, CSI, which can effectively represent the frequency and spatial characteristics of the location points, is used as the location parameter and the kNN algorithm in the machine learning algorithm is used as the location algorithm to achieve more accurate indoor positioning. The main contents of this paper are as follows: (1) this paper studies the localization methods used in Wi-Fi indoor positioning, including the existing indoor location estimation methods and localization algorithms. When analyzing the location estimation method, the advantages and disadvantages of using RSSI and CSI as positioning parameters in Wi-Fi indoor positioning are compared, and the reason why CSI is used as location parameter in this paper is given. After using CSI as the location parameter, the location estimation algorithm based on CSI is studied and improved. (2) in the selection of location algorithm, it is different from the traditional tripod center method, hyperbolic method and least square method. This paper introduces the kNN algorithm and Bayes algorithm in machine learning algorithm, and analyzes the localization performance of the machine learning algorithm based on CSI. Experimental results show that the location performance of KNN algorithm is better than that of Bayes algorithm. The evaluation of location performance includes mean location error and cumulative error distribution function (CDF). (3) Establishment of off-line training fingerprint database, which has an important impact on the positioning effect of the system. After the acquisition of CSI signal, the key point of establishing fingerprint database is to extract different processing methods and location parameters of CSI data. Therefore, different algorithms are proposed to process the acquired CSI and to reduce the dimension of the CSI data by PCA to obtain the new location parameters. The comparison and analysis of the above two algorithms show that the two algorithms have better localization effect than the traditional methods. At the same time, the location performance is optimized when the CSI eigenvalue obtained by PCA processing is used as the location parameter. Through simulation tools and experimental platforms, the effects of different experimental environments and different training data on the final positioning performance are discussed. On the basis of theoretical research, Matlab is used to analyze the influence of various factors on location performance in different localization algorithms and parameters. The final experimental results show that the average positioning accuracy can reach 0.863 m under given experimental conditions, which is 20% higher than the traditional algorithm based on CSI.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN92
本文編號：2187390
[Abstract]:RSSI is often used as the positioning parameter in indoor positioning based on Wi-Fi, and RSSI is greatly affected by environment and hardware equipment. Therefore, in this paper, CSI, which can effectively represent the frequency and spatial characteristics of the location points, is used as the location parameter and the kNN algorithm in the machine learning algorithm is used as the location algorithm to achieve more accurate indoor positioning. The main contents of this paper are as follows: (1) this paper studies the localization methods used in Wi-Fi indoor positioning, including the existing indoor location estimation methods and localization algorithms. When analyzing the location estimation method, the advantages and disadvantages of using RSSI and CSI as positioning parameters in Wi-Fi indoor positioning are compared, and the reason why CSI is used as location parameter in this paper is given. After using CSI as the location parameter, the location estimation algorithm based on CSI is studied and improved. (2) in the selection of location algorithm, it is different from the traditional tripod center method, hyperbolic method and least square method. This paper introduces the kNN algorithm and Bayes algorithm in machine learning algorithm, and analyzes the localization performance of the machine learning algorithm based on CSI. Experimental results show that the location performance of KNN algorithm is better than that of Bayes algorithm. The evaluation of location performance includes mean location error and cumulative error distribution function (CDF). (3) Establishment of off-line training fingerprint database, which has an important impact on the positioning effect of the system. After the acquisition of CSI signal, the key point of establishing fingerprint database is to extract different processing methods and location parameters of CSI data. Therefore, different algorithms are proposed to process the acquired CSI and to reduce the dimension of the CSI data by PCA to obtain the new location parameters. The comparison and analysis of the above two algorithms show that the two algorithms have better localization effect than the traditional methods. At the same time, the location performance is optimized when the CSI eigenvalue obtained by PCA processing is used as the location parameter. Through simulation tools and experimental platforms, the effects of different experimental environments and different training data on the final positioning performance are discussed. On the basis of theoretical research, Matlab is used to analyze the influence of various factors on location performance in different localization algorithms and parameters. The final experimental results show that the average positioning accuracy can reach 0.863 m under given experimental conditions, which is 20% higher than the traditional algorithm based on CSI.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN92
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