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基于因子圖和聯(lián)合消息傳遞的無(wú)線(xiàn)網(wǎng)絡(luò)協(xié)作定位算法

發(fā)布時(shí)間:2018-01-19 19:23

  本文關(guān)鍵詞: 近似貝葉斯推理 因子圖 置信傳播 平均場(chǎng)方法 無(wú)線(xiàn)傳感器網(wǎng)絡(luò) 協(xié)作定位 出處:《計(jì)算機(jī)應(yīng)用》2017年05期  論文類(lèi)型:期刊論文


【摘要】:針對(duì)現(xiàn)有基于消息傳遞算法的無(wú)線(xiàn)網(wǎng)絡(luò)節(jié)點(diǎn)定位算法復(fù)雜度和通信開(kāi)銷(xiāo)過(guò)高的問(wèn)題,提出一種基于測(cè)距的、低復(fù)雜度低協(xié)作開(kāi)銷(xiāo)的聯(lián)合消息傳遞節(jié)點(diǎn)定位算法。所提算法考慮參考節(jié)點(diǎn)位置的不確定性以減少誤差累積,并將消息約束為高斯函數(shù)以降低通信開(kāi)銷(xiāo)。首先,根據(jù)系統(tǒng)的概率模型和因子分解設(shè)計(jì)因子圖;然后,根據(jù)狀態(tài)轉(zhuǎn)移模型和測(cè)距模型的特點(diǎn),分別使用置信傳播和平均場(chǎng)方法計(jì)算預(yù)測(cè)消息和協(xié)作消息;最后,在每次迭代過(guò)程中,通過(guò)非線(xiàn)性項(xiàng)的泰勒展開(kāi)將非高斯置信消息近似為高斯函數(shù)。仿真分析表明,所提算法的定位性能與基于粒子的SPAWN算法接近,但節(jié)點(diǎn)間傳輸?shù)男畔⒂纱罅苛W幼優(yōu)榫迪蛄亢蛥f(xié)方差矩陣,同時(shí)計(jì)算復(fù)雜度也大幅降低。
[Abstract]:In order to solve the problem of high complexity and communication overhead of the existing wireless network node location algorithm based on message passing algorithm, a new method based on ranging is proposed. A joint messaging node location algorithm with low complexity and low collaboration overhead. The proposed algorithm considers the uncertainty of the reference node position to reduce the error accumulation and restricts the message to Gao Si function to reduce the communication overhead. According to the probability model of the system and factor decomposition design factor graph; Then, according to the characteristics of the state transition model and the ranging model, the predictive message and the cooperative message are calculated by using the confidence propagation method and the mean field method, respectively. Finally, in each iteration process, the non-#china_person0# confidence message is approximated to Gao Si function through the Taylor expansion of the nonlinear term. The simulation results show that. The localization performance of the proposed algorithm is similar to that of the Particle based SPAWN algorithm, but the information transmitted between nodes is changed from a large number of particles to mean vector and covariance matrix, and the computational complexity is also greatly reduced.
【作者單位】: 國(guó)家數(shù)字交換系統(tǒng)工程技術(shù)研究中心;洛陽(yáng)師范學(xué)院物理與電子信息學(xué)院;鄭州大學(xué)信息工程學(xué)院;
【基金】:國(guó)家自然科學(xué)基金資助項(xiàng)目(61571402,61401401)~~
【分類(lèi)號(hào)】:TN929.5;TP212.9
【正文快照】: 0引言在基于無(wú)線(xiàn)傳感器網(wǎng)絡(luò)(Wireless Sensor Network,WSN)的應(yīng)用中,傳感器節(jié)點(diǎn)檢測(cè)到的信息若沒(méi)有準(zhǔn)確的位置信息將變得毫無(wú)價(jià)值[1]。但考慮到成本和能量限制,一般只有少數(shù)參考節(jié)點(diǎn)的位置是已知的,其他大部分節(jié)點(diǎn)(稱(chēng)為待定位節(jié)點(diǎn))通過(guò)鄰近的參考節(jié)點(diǎn)的位置和與其之間的距離等

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本文編號(hào):1445259


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