本文選題：室內(nèi)定位 + 聚類指紋庫��； 參考：《武漢大學(xué)學(xué)報(bào)(信息科學(xué)版)》2014年11期

【摘要】：針對室內(nèi)環(huán)境基于RSSI定位不穩(wěn)定問題,提出了以幾何信息改進(jìn)基于指紋庫的KNN定位算法。根據(jù)室內(nèi)幾何布局建立了聚類指紋庫,提出了表征點(diǎn)位幾何特性的點(diǎn)散發(fā)性強(qiáng)度(geometric strength of sporadic,GSS)概念。利用最鄰近樣本點(diǎn)的GSS判別移動(dòng)終端所在參考點(diǎn)RP控制網(wǎng)結(jié)構(gòu)以動(dòng)態(tài)選擇KNN關(guān)鍵參數(shù)K,構(gòu)建最佳多邊形為約束準(zhǔn)則自適應(yīng)選取后K-1個(gè)鄰近點(diǎn),建立了基于幾何聚類指紋庫的約束加權(quán)KNN室內(nèi)定位模型。結(jié)果表明,改進(jìn)后定位模型可以更好地估計(jì)終端位置信息,其中幾何聚類指紋庫是改善定位準(zhǔn)確性的關(guān)鍵,約束KNN能夠有效地提高室內(nèi)定位精度。
[Abstract]:Aiming at the instability of indoor environment location based on RSSI, an improved KNN location algorithm based on fingerprint database is proposed. A cluster fingerprint library was established according to the indoor geometric layout, and the concept of point radiometric strength of sporadicus was proposed. The GSS of the nearest sample points is used to judge the RP control network structure of the reference point where the mobile terminal is located in order to dynamically select the key parameters of KNN, and to construct the best polygon as the constraint criterion to adaptively select the K-1 adjacent points. A constrained weighted KNN indoor location model based on geometric clustering fingerprint database is established. The results show that the improved location model can better estimate the terminal location information, in which the geometric clustering fingerprint database is the key to improve the location accuracy, and the constrained KNN can effectively improve the indoor positioning accuracy.
【作者單位】： 中國礦業(yè)大學(xué)國土環(huán)境與災(zāi)害監(jiān)測國家測繪局重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家863計(jì)劃資助項(xiàng)目(2013AA12A201) 江蘇高校優(yōu)勢學(xué)科建設(shè)工程資助項(xiàng)目 中央高校基本科研業(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(2013RC16) 新世紀(jì)優(yōu)秀人才支持計(jì)劃資助項(xiàng)目(NCET-13-1019)~~
【分類號】：TN95
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本文編號：1805145