【摘要】：針對(duì)無(wú)線傳感器網(wǎng)絡(luò)全節(jié)點(diǎn)定位求精問(wèn)題展開(kāi)研究,應(yīng)用自組織映射算法進(jìn)行定位求精,提出一種優(yōu)化領(lǐng)域函數(shù),實(shí)現(xiàn)快速收斂的雙向調(diào)整定位算法.利用傳感器節(jié)點(diǎn)作為神經(jīng)元節(jié)點(diǎn),通過(guò)節(jié)點(diǎn)間距離相關(guān)度建立自組織神經(jīng)元網(wǎng)絡(luò),通過(guò)雙向調(diào)整鄰域函數(shù)實(shí)現(xiàn)算法對(duì)節(jié)點(diǎn)間距與測(cè)量距離誤差的正負(fù)性的適應(yīng)能力,達(dá)到收斂性、高定位精度性、快速性要求,最終實(shí)現(xiàn)傳感器網(wǎng)絡(luò)的自組織定位.應(yīng)用MATLAB仿真對(duì)本文提出的算法與單向調(diào)整算法進(jìn)行比較,本文提出的算法較大地提高了算法的收斂性和定位精度,較好地反映傳感器節(jié)點(diǎn)的拓?fù)浣Y(jié)構(gòu),且穩(wěn)定性好.
[Abstract]:In order to solve the problem of all-node location refinement in wireless sensor networks (WSN), the self-organizing mapping algorithm is applied to the localization refinement, and an optimized domain function is proposed to realize the fast convergence bidirectional adjustment localization algorithm. The sensor node is used as the neuron node, the self-organizing neural network is established by the distance correlation between the nodes, and the adaptive ability of the algorithm to the positivity of the distance between the nodes and the measurement distance error is realized by bidirectional adjustment of the neighborhood function. To achieve convergence, high positioning accuracy, fast requirements, and finally achieve the sensor network self-organization localization. The proposed algorithm is compared with the unidirectional adjustment algorithm by using MATLAB simulation. The proposed algorithm greatly improves the convergence and positioning accuracy of the algorithm, and reflects the topology structure of the sensor node well, and has good stability.
【作者單位】： 三峽大學(xué)計(jì)算機(jī)與信息學(xué)院;三峽大學(xué)智能視覺(jué)與圖像信息研究所;
【基金】：湖北省自然科學(xué)基金項(xiàng)目(2012FFC09701)資助 水電工程智能視覺(jué)監(jiān)測(cè)湖北省重點(diǎn)實(shí)驗(yàn)室開(kāi)放基金項(xiàng)目(2014KLA05)資助
【分類號(hào)】：TP212.9;TN929.5

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