本文選題：近鄰傳播算法　切入點(diǎn)：方差濾波　出處：《計(jì)算機(jī)應(yīng)用研究》2017年10期 　論文類型：期刊論文

【摘要】：目前傳統(tǒng)的室內(nèi)指紋定位算法中存在以下幾個(gè)問題:首先在構(gòu)建指紋庫(kù)時(shí)采用平均值的方式容易受到噪聲點(diǎn)影響而降低定位精度;其次使用歐氏距離衡量待定位點(diǎn)與指紋點(diǎn)之間的距離可能引入信號(hào)強(qiáng)度距離較近、物理距離較遠(yuǎn)的參考點(diǎn)參與估計(jì)待定位點(diǎn)的位置,從而增大定位誤差,以及當(dāng)參考點(diǎn)數(shù)量較大時(shí),由于K近鄰算法的計(jì)算量較大,造成定位過程耗時(shí)較大,能源耗費(fèi)較多的情況;最后由于K近鄰算法無(wú)法根據(jù)實(shí)際情況確定參與定位的參考點(diǎn)個(gè)數(shù)而限制了定位系統(tǒng)的精確性和拓展性。針對(duì)上述問題,設(shè)計(jì)了一種基于近鄰傳播算法的動(dòng)態(tài)自適應(yīng)室內(nèi)指紋定位算法。該算法在離線階段對(duì)在每一個(gè)參考點(diǎn)采集的信號(hào)強(qiáng)度值使用方差濾波算法去除噪聲值,然后利用加入了參考點(diǎn)物理信息的近鄰傳播算法對(duì)參考點(diǎn)進(jìn)行聚類處理。在在線階段,通過進(jìn)行粗略定位和精確定位動(dòng)態(tài)地估計(jì)待定位點(diǎn)的物理位置。經(jīng)過實(shí)驗(yàn)證明,所提出的新算法較對(duì)比算法有較高的精確度和穩(wěn)定度。
[Abstract]:At present, there are several problems in the traditional indoor fingerprint localization algorithm: firstly, the average value method is easy to be affected by noise points and the location accuracy is reduced when constructing fingerprint database; Secondly, using Euclidean distance to measure the distance between the undetermined site and the fingerprint point may lead to the closer distance of signal intensity, and the reference point which is far away from the physical distance may participate in the estimation of the location of the undetermined site, thus increasing the positioning error. And when the number of reference points is large, because of the large amount of computation of K-nearest neighbor algorithm, the localization process is time-consuming and the energy consumption is more. Finally, the K-nearest neighbor algorithm can not determine the number of reference points involved in the location according to the actual situation, which limits the accuracy and expansibility of the positioning system. A dynamic adaptive indoor fingerprint location algorithm based on nearest neighbor propagation algorithm is designed, which uses variance filtering algorithm to remove the noise value of signals collected at each reference point in off-line phase. Then the reference points are clustered by using the nearest neighbor propagation algorithm with physical information of reference points. In the online stage, the physical location of the undetermined sites is dynamically estimated by rough location and accurate location. The proposed algorithm has higher accuracy and stability than the contrast algorithm.
【作者單位】： 信息工程大學(xué)理學(xué)院;
【分類號(hào)】：TN92

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本文編號(hào)：1588378