本文選題：WiFi指紋定位　切入點(diǎn)：邊緣重疊法　出處：《大連理工大學(xué)》2014年碩士論文

【摘要】：近些年,隨著WiFi技術(shù)在無(wú)線網(wǎng)絡(luò)應(yīng)用中的普及,室內(nèi)WiFi定位成了研究的熱門(mén)課題。一種常用的室內(nèi)定位方法為基于接收信號(hào)強(qiáng)度(Received Signal Strength, RSS)的位置指紋法,該方法可行性強(qiáng)、定位成本低,受到研究人員的廣泛關(guān)注。 位置指紋法雖然能夠較好地在室內(nèi)實(shí)現(xiàn)精確定位,但是當(dāng)指紋數(shù)量增多時(shí),定位算法的復(fù)雜度也會(huì)增大,系統(tǒng)的實(shí)時(shí)性就比較差,尤其是對(duì)目標(biāo)進(jìn)行跟蹤時(shí),對(duì)定位系統(tǒng)的實(shí)時(shí)性要求會(huì)更高,傳統(tǒng)定位方法難以滿(mǎn)足要求。為此,本文較為深入地探討了基于接收信號(hào)強(qiáng)度的定位和跟蹤問(wèn)題,主要工作如下： 首先,在實(shí)際環(huán)境搭建了實(shí)驗(yàn)平臺(tái),實(shí)際測(cè)量了大量的數(shù)據(jù),分析了影響室內(nèi)WiFi定位的關(guān)鍵因素,包括RSS與位置的關(guān)系、人對(duì)RSS的影響、可檢測(cè)到的AP數(shù)目的變化以及RSS的概率分布情況等,為進(jìn)一步研究提供了保障； 其次,為了降低算法的復(fù)雜度,本文采用分層定位的思想,將定位分為粗定位和精定位兩步。這一方法中,區(qū)域劃分很重要,本文提出了一種實(shí)際可行的邊緣重疊法,并分別采用壓縮感知和最小歐幾里德距離方法進(jìn)行區(qū)域判定。本文在仿真場(chǎng)景和實(shí)際環(huán)境中進(jìn)行了實(shí)驗(yàn),將這種方法與現(xiàn)有的經(jīng)典的聚類(lèi)方法(比如,仿射傳播聚類(lèi))做了比較研究,實(shí)驗(yàn)結(jié)果表明,采用邊緣重疊法能達(dá)到更好的定位精度； 最后,對(duì)跟蹤技術(shù)進(jìn)行了研究,主要研究了粒子濾波算法,并將基于邊緣重疊法的分層指紋定位技術(shù)與現(xiàn)有的loose-coupling PF相結(jié)合,實(shí)現(xiàn)了對(duì)目標(biāo)的跟蹤。分層技術(shù)的引入,并沒(méi)有對(duì)定位精度造成很大影響,而且減少了算法的復(fù)雜度,提高了系統(tǒng)的實(shí)時(shí)性。
[Abstract]:In recent years, with the popularity of WiFi technology in wireless network applications, indoor WiFi positioning has become a hot topic.A common indoor location method is based on received Signal received Signal fingerprint (RSS). This method is feasible and low in cost, and has been widely concerned by researchers.Although the location fingerprint method can achieve accurate location in the room, when the number of fingerprints increases, the complexity of the location algorithm will also increase, and the real-time performance of the system is relatively poor, especially when tracking the target.The real-time requirement of the positioning system will be higher, the traditional positioning method is difficult to meet the requirements.Therefore, this paper deeply discusses the localization and tracking based on the received signal strength. The main work is as follows:First of all, the experimental platform is built in the actual environment, and a large number of data are measured, and the key factors affecting indoor WiFi positioning are analyzed, including the relationship between RSS and location, the influence of people on RSS.The change of the number of AP and the probability distribution of RSS provide the guarantee for further research.Secondly, in order to reduce the complexity of the algorithm, this paper adopts the idea of hierarchical localization, and divides the location into coarse location and fine location.In this method, the region division is very important. In this paper, a practical edge overlap method is proposed, and the compression sensing method and the minimum Euclidean distance method are used to determine the region.In this paper, experiments are carried out in the simulation scene and the actual environment. The results show that this method is compared with the existing classical clustering methods (for example, affine propagation clustering).The edge overlap method can achieve better positioning accuracy.Finally, the tracking technology is studied, the particle filter algorithm is mainly studied, and the hierarchical fingerprint location technology based on the edge overlap method is combined with the existing loose-coupling PF to achieve the target tracking.The introduction of stratification technology does not have a great impact on the positioning accuracy and reduces the complexity of the algorithm and improves the real-time performance of the system.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN92

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本文編號(hào)：1716121