本文選題：無線傳感器網(wǎng)絡(luò) + 非視距��； 參考：《東北大學(xué)》2014年碩士論文

【摘要】：無線傳感器網(wǎng)絡(luò)(Wireless Sensor Network, WSN)是一門新興的信息采集與處理技術(shù),具有極為廣闊的應(yīng)用前景。移動節(jié)點(diǎn)定位作為WSN的關(guān)鍵技術(shù),是無線傳感器網(wǎng)絡(luò)研究的熱點(diǎn)問題之一。目前的研究通常僅考慮視距狀態(tài)(Line-of-sight, LOS),但在實(shí)際應(yīng)用中,信號的非視距(Non-line-of-sight, NLOS)傳播現(xiàn)象是普遍存在的,并將導(dǎo)致定位算法的精度大大降低。本文對NLOS環(huán)境下的節(jié)點(diǎn)定位問題展開了深入的討論和研究,目的在于提高NLOS環(huán)境下移動節(jié)點(diǎn)的定位精度。本文分析了NLOS距離測量值的殘差特性,并提出嚴(yán)格殘差選擇機(jī)制以對距離測量值進(jìn)行狀態(tài)鑒別。由于距離測量值通常既包含LOS測量值又包含NLOS測量值,因此,充分利用LOS測量值進(jìn)行定位可以有效提高算法的定位精度。本文首先應(yīng)用擴(kuò)展卡爾曼濾波(Extended Kalman Filter, EKF)算法的線性回歸模型生成測量值的殘差,并利用LOS測量值與NLOS測量值的殘差差異完成測量值狀態(tài)的準(zhǔn)確鑒別。仿真結(jié)果表明,在NLOS環(huán)境下,采用嚴(yán)格殘差選擇機(jī)制進(jìn)行測量值的狀態(tài)鑒別,結(jié)合變節(jié)點(diǎn)EKF算法可以得到較高的定位精度。基于M-估計算法定位思想,本文提出了一種移動節(jié)點(diǎn)魯棒定位算法。通過分析NLOS殘差的統(tǒng)計特性,提出了一種基于最鄰近估計的變量核密度估計算法,以估算殘差的概率密度函數(shù)。然后結(jié)合M-估計的定位思想,提出了一種基于變量核密度估計的移動節(jié)點(diǎn)定位算法。實(shí)驗(yàn)結(jié)果表明,該算法克服了M-估計算法需要模型匹配與人工調(diào)整參數(shù)的局限性,實(shí)現(xiàn)了對不同環(huán)境下NLOS誤差的抑制�？紤]到NLOS誤差的特性,本文提出了一種基于投票選擇機(jī)制的概率數(shù)據(jù)關(guān)聯(lián)算法。利用NLOS誤差標(biāo)準(zhǔn)差大于測量誤差標(biāo)準(zhǔn)差的特性,然后結(jié)合高頻測距數(shù)據(jù)處理思想,提出了一種基于投票選擇機(jī)制的數(shù)據(jù)處理算法對距離測量值進(jìn)行篩選,并保留可靠的測量值。在此基礎(chǔ)上,提出了一種改進(jìn)的概率數(shù)據(jù)關(guān)聯(lián)算法對經(jīng)投票篩選后的多個測量值進(jìn)行數(shù)據(jù)融合,最后應(yīng)用基于參考節(jié)點(diǎn)選擇的線性最小二乘估計算法計算出移動節(jié)點(diǎn)的位置。該算法有效地削弱了各種類型的NLOS誤差,提高了移動節(jié)點(diǎn)的定位精度。本文系統(tǒng)地研究了非視距環(huán)境下WSN的移動節(jié)點(diǎn)定位算法,并通過一系列仿真實(shí)驗(yàn)與現(xiàn)場實(shí)驗(yàn)對所提算法進(jìn)行分析。實(shí)驗(yàn)結(jié)果證明了本文所提算法在NLOS環(huán)境下均具有較強(qiáng)的魯棒性和較高的定位精度。
[Abstract]:Wireless Sensor Network, WSN) is a new technology of information acquisition and processing, which has a wide application prospect. As a key technology of WSN, mobile node location is one of the hot issues in wireless sensor networks. At present, only Line-of-sight (LOSN) is considered in the current research. However, in practical applications, the phenomenon of non-line-of-sight (NLOSs) propagation of signals is widespread, which will result in the reduction of the accuracy of the localization algorithm. In this paper, the problem of node location in NLOS environment is discussed and studied in order to improve the positioning accuracy of mobile nodes in NLOS environment. In this paper, the residual characteristics of NLOS distance measurements are analyzed, and a strict residual selection mechanism is proposed to identify the state of the distance measurements. Because the distance measurement value usually includes both the LOS and NLOS measurements, the localization accuracy of the algorithm can be improved by fully utilizing the LOS measurement values. In this paper, the linear regression model of extended Kalman Filter, EKF) algorithm is first used to generate the residual error of the measured value, and the state of the measured value is accurately identified by the difference between the measured value of LOS and that of the value of NLOS. The simulation results show that in the NLOS environment, the strict residual selection mechanism is used to identify the state of the measured values, and the variable node EKF algorithm is used to obtain higher positioning accuracy. Based on the idea of M- estimation algorithm, a robust location algorithm for mobile nodes is proposed in this paper. By analyzing the statistical properties of NLOS residuals, a variable kernel density estimation algorithm based on nearest neighbor estimation is proposed to estimate the probability density function of the residuals. Then, a mobile node location algorithm based on variable kernel density estimation is proposed based on the idea of M- estimation. The experimental results show that the algorithm overcomes the limitation of the M- estimation algorithm which requires model matching and manual adjustment of parameters, and realizes the suppression of NLOS errors in different environments. Considering the characteristic of NLOS error, a probabilistic data association algorithm based on voting selection mechanism is proposed in this paper. Taking advantage of the fact that the standard deviation of NLOS error is larger than the standard deviation of measurement error, and combining with the idea of high frequency ranging data processing, a data processing algorithm based on voting selection mechanism is proposed to filter the distance measurement value and keep the reliable measurement value. On this basis, an improved probabilistic data association algorithm is proposed for data fusion of several measured values selected by voting. Finally, the location of mobile nodes is calculated by using the linear least square estimation algorithm based on the selection of reference nodes. The algorithm effectively weakens all kinds of NLOS errors and improves the location accuracy of mobile nodes. In this paper, the mobile node location algorithm of WSN in the non-line-of-sight environment is systematically studied, and the proposed algorithm is analyzed through a series of simulation experiments and field experiments. The experimental results show that the proposed algorithm is robust and accurate in NLOS environment.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN929.5;TP212.9

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