多天線(xiàn)定位算法研究
發(fā)布時(shí)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(shè)備的影響大。因此,本論文采用能有效的表征定位點(diǎn)頻率和空間特征的CSI作為定位參量和機(jī)器學(xué)習(xí)算法中的kNN算法作為定位算法,來(lái)實(shí)現(xiàn)更為準(zhǔn)確的室內(nèi)定位。本文主要研究?jī)?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(jì)方法和定位算法。在分析定位估計(jì)方法時(shí),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(jìn)了基于CSI的定位估計(jì)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(xiàn)法和最小二乘法這三種算法,介紹了機(jī)器學(xué)習(xí)算法中的kNN算法和Bayes算法,并對(duì)其在基于CSI時(shí)的定位性能進(jìn)行了實(shí)驗(yàn)分析。實(shí)驗(yàn)結(jié)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(píng)估包括平均定位誤差和誤差累計(jì)分布函數(shù)CDF。(3)建立離線(xiàn)階段訓(xùn)練指紋庫(kù),這對(duì)系統(tǒng)的定位效果有著重要的影響。在CSI信號(hào)采集后,對(duì)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(kù)的重點(diǎn)。因此,本文提出了不同的算法對(duì)獲取的CSI進(jìn)行處理并采用PCA對(duì)CSI數(shù)據(jù)進(jìn)行降維得到新的定位參量。對(duì)以上兩種定位估計(jì)算法進(jìn)行了比較分析,結(jié)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(shí),采用PCA處理后得到的CSI特征值作為定位參量時(shí)的定位性能達(dá)到最優(yōu)。通過(guò)仿真工具和實(shí)驗(yàn)平臺(tái),討論不同實(shí)驗(yàn)環(huán)境以及不同訓(xùn)練數(shù)據(jù)對(duì)最終定位性能的影響。本論文在理論研究的基礎(chǔ)上,利用Matlab分析在不同定位算法和定位參量時(shí),各個(gè)因素對(duì)定位性能的影響。最終實(shí)驗(yàn)結(jié)果表明,平均定位精度在論文給定實(shí)驗(yàn)條件下可以達(dá)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(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN92
本文編號(hào)：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(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN92
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