本文選題：RFID + 閱讀器��； 參考：《湖北工業(yè)大學(xué)》2017年碩士論文

【摘要】：射頻識(shí)別(Radio Frequency Identification,RFID)是一種利用射頻信號(hào)達(dá)到非接觸的自動(dòng)識(shí)別目標(biāo)對(duì)象的技術(shù),通過此技術(shù)不僅可以方便地獲取存儲(chǔ)在目標(biāo)對(duì)象中的信息外,還可以對(duì)目標(biāo)對(duì)象進(jìn)行跟蹤定位。RFID技術(shù)與快速發(fā)展的互聯(lián)網(wǎng)、多媒體、物聯(lián)網(wǎng)產(chǎn)業(yè)相結(jié)合,在物流領(lǐng)域、交通運(yùn)輸領(lǐng)域、醫(yī)療行業(yè)都有應(yīng)用。因?yàn)槲锫?lián)網(wǎng)的快速發(fā)展,除了需要使用全球定位系統(tǒng)(GPS)對(duì)交通運(yùn)輸中物品進(jìn)行跟蹤外,還需要對(duì)存儲(chǔ)在倉庫中的物品進(jìn)行貨物的出庫、入庫和定位。在室內(nèi)環(huán)境中,GPS系統(tǒng)無法滿足室內(nèi)定位的需要,而使用RFID技術(shù)則對(duì)物品進(jìn)行定位成為近幾年的研究熱點(diǎn)。本論文主要針對(duì)幾種基于無線信號(hào)強(qiáng)度的RFID室內(nèi)定位算法進(jìn)行了系統(tǒng)研究,對(duì)以下幾個(gè)方面進(jìn)行了研究和探討:首先調(diào)研了射頻識(shí)別系統(tǒng)的硬件組成和工作原理,對(duì)當(dāng)前射頻識(shí)別技術(shù)的研究方向和發(fā)展趨勢(shì)進(jìn)行了分析。其次對(duì)各種RFID定位技術(shù),如基于無線信號(hào)到達(dá)時(shí)間定位技術(shù)、基于信號(hào)到達(dá)角度定位技術(shù)和基于無線信號(hào)強(qiáng)度定位技術(shù)等進(jìn)行了研究,得出基于無線信號(hào)強(qiáng)度的定位技術(shù)更加適合市場(chǎng)的需求,然后對(duì)基于無線信號(hào)強(qiáng)度技術(shù)的各種定位算法進(jìn)行了詳細(xì)說明。然后使用MATLAB軟件對(duì)三邊測(cè)量法、三邊質(zhì)心法、基于參考標(biāo)簽的權(quán)重算法仿真實(shí)驗(yàn),對(duì)每種定位算法的實(shí)驗(yàn)仿真結(jié)果分析了優(yōu)缺點(diǎn)。最后提出使用粒子群算法進(jìn)行室內(nèi)定位,對(duì)基于參考標(biāo)簽的粒子群算法進(jìn)行了仿真實(shí)驗(yàn),認(rèn)為基于無線信號(hào)強(qiáng)度的室內(nèi)定位使用粒子群算法較為合適。粒子群算法的優(yōu)勢(shì)是不用額外添加昂貴的實(shí)驗(yàn)硬件設(shè)備的情況下,使用價(jià)格相對(duì)低廉的RFID電子參考標(biāo)簽作為基準(zhǔn),不僅可以提高定位的準(zhǔn)確度,還使得基于參考標(biāo)簽的室內(nèi)定位算法的穩(wěn)定性得到提高,粒子群算法的主要思想是建立一種逐步優(yōu)化的數(shù)學(xué)模型,使用測(cè)量得到的無線信號(hào)強(qiáng)度數(shù)據(jù)轉(zhuǎn)換成待定位電子標(biāo)簽與估算位置之間的距離,逐步縮小待定位電子標(biāo)簽與估算位置之間的距離,最后得到一個(gè)最優(yōu)解。最后利用實(shí)驗(yàn)室的設(shè)備實(shí)現(xiàn)了基于參考標(biāo)簽的粒子群算法定位實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果符合預(yù)期。
[Abstract]:Radio Frequency Identification (RFID) is a non-contact automatic object identification technology, which can not only obtain the information stored in the target object conveniently. The technology of RFID can also be used in the field of logistics, transportation and medical industry, combining with the rapid development of Internet, multimedia and Internet of things industry. Due to the rapid development of the Internet of things, it is necessary to use GPS to track the goods in transportation, but also to export, enter and locate the goods stored in the warehouse. In the indoor environment, GPS system can not meet the needs of indoor positioning, and RFID technology has become a research hotspot in recent years. In this paper, several RFID indoor localization algorithms based on wireless signal strength are studied systematically. The following aspects are studied and discussed: firstly, the hardware composition and working principle of RFID system are investigated. The research direction and development trend of RFID technology are analyzed. Secondly, various RFID localization technologies, such as wireless signal arrival time location, signal arrival angle location and wireless signal intensity localization, are studied. It is concluded that the localization technology based on wireless signal intensity is more suitable for the market demand, and then various localization algorithms based on wireless signal intensity technology are explained in detail. Then the MATLAB software is used to measure the triangulation method, the centroid method, the weight algorithm based on reference label simulation experiment, and the advantages and disadvantages of each localization algorithm are analyzed. Finally, the particle swarm optimization (PSO) algorithm is proposed for indoor localization. The PSO algorithm based on reference label is simulated, and it is considered that the PSO algorithm based on wireless signal intensity is more suitable for indoor localization. The advantage of particle swarm optimization is that it can not only improve the accuracy of location, but also use the relatively cheap RFID electronic reference tag as the benchmark without adding expensive experimental hardware. It also improves the stability of indoor localization algorithm based on reference label. The main idea of particle swarm optimization is to establish a mathematical model of stepwise optimization. The wireless signal strength data obtained from the measurement are converted into the distance between the tag and the estimated position, and the distance between the tag and the estimated position is gradually reduced, and an optimal solution is obtained. Finally, the experiment of particle swarm optimization based on reference label is carried out by using laboratory equipment, and the experimental results are in line with expectations.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.44

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本文編號(hào)：2073065