井下無線傳感器網絡比例差分修正的RSSI節(jié)點定位算法
發(fā)布時間：2018-08-20 16:47
【摘要】：礦井下對人員的定位在礦井的安全生產中是極其核心的一部分。無線傳感器網絡技術近些年來作為一類新興的對信息感知、處理和交互技術,將會為井下安監(jiān)系統(tǒng)帶來飛速的發(fā)展和進步。井下安監(jiān)系統(tǒng)最核心的問題是井下節(jié)點的定位問題,沒有定位技術井下安全系統(tǒng)的信息采集就失去了其根本的價值。節(jié)點定位技術在無線傳感器網絡中占據著最基礎,最核心的部分。在礦井下的特殊環(huán)境中,傳統(tǒng)的GPS定位方法因為需要接收衛(wèi)星信號而無法滿足在井下環(huán)境中的定位監(jiān)測。目前,井下的定位系統(tǒng)主要采用射頻識別的方式,即在人靠近射頻節(jié)點時才能被檢測到,進而得到定位信息,但是射頻識別的定位方式只是一種被動的監(jiān)測方式,無法實現(xiàn)對井下人員的實時定位反饋,針對現(xiàn)在井下安監(jiān)系統(tǒng)不能實時定位、定位精度差、性能不穩(wěn)定的問題,結合無線傳感器網絡知識和井下環(huán)境中無線傳感器網絡的定位現(xiàn)狀,提出了兩種新型井下無線傳感器網絡節(jié)點定位方法,并且對兩種方法進行仿真驗證。本文在基于RSSI的多邊定位算法的基礎上,利用錨節(jié)點之間的相互關系獲取比例差分系數,將這個系數應用在通過RSSI方法測量得到的節(jié)點之間的距離上。RSSI測量方法在不同環(huán)境下的測量需要不同的傳播模型,且RSSI測距方法由于自身的限制條件,即RSSI對距離近的目標測距精度要遠遠好于距離遠的目標。目標未知節(jié)點首先讀取在通信范圍內的信標節(jié)點廣播的信息,得到RSSI強度值,通過卡爾曼濾波的方法對RSSI信號除去信號中的噪聲,除去噪聲的接收信號強度值可以獲得更加精確的距離值,繼而利用最靠近目標節(jié)點的錨節(jié)點和其余錨節(jié)點構建差分模型,獲取系統(tǒng)差分誤差,在對目標節(jié)點測距時去掉系統(tǒng)差分誤差,得到更加精確的距離值;然后利用比例差分的方法繼續(xù)修正RSSI測距,經過仿真實驗,得到定位效果明顯好于傳統(tǒng)RSSI定位算法精度。針對原始的加權質心定位不夠精確,使用了改進的加權質心的方法對井下巷道內的節(jié)點進行定位。分析了在巷道環(huán)境下節(jié)點分布時存在的問題,提出解決方案。在此基礎上,進一步采取比例差分的方法對獲取到的距離修正,進一步優(yōu)化加權系數,使節(jié)點的定位精度更加逼近真實效果。
[Abstract]:The positioning of personnel under the mine is an extremely important part of the safety production of the mine. Wireless sensor network (WSN) technology, as a new technology of information perception, processing and interaction, will bring rapid development and progress for underground safety monitoring system in recent years. The core problem of downhole safety monitoring system is the location of underground nodes. Without positioning technology, the information collection of downhole safety system will lose its fundamental value. Node location technology occupies the most basic and core part in wireless sensor networks. In the special environment under the mine, the traditional GPS positioning method can not meet the need to receive satellite signals and can not meet the location monitoring in the underground environment. At present, the downhole positioning system mainly adopts the method of radio frequency identification, that is, it can only be detected when people are near the radio frequency node, and then the location information can be obtained. However, the location mode of radio frequency identification is only a passive monitoring method. Can not realize the real-time positioning feedback to the downhole personnel, aiming at the problem that the downhole safety monitoring system can not locate in real time, the positioning accuracy is poor, the performance is unstable, Combined with the knowledge of wireless sensor network and the status quo of wireless sensor network location in underground environment, two new underground wireless sensor network node localization methods are proposed, and the two methods are simulated and verified. In this paper, based on the multilateral localization algorithm based on RSSI, the proportional difference coefficient is obtained by using the relationship between anchor nodes. This coefficient is applied to the distance between nodes measured by RSSI method. The measurement of RSSI in different environments requires different propagation models, and the RSSI ranging method is limited by its own conditions. That is, the ranging accuracy of RSSI is much better than that of long range target. The target unknown node first reads the information broadcast by the beacon node in the communication range, obtains the RSSI intensity value, and removes the noise from the RSSI signal by Kalman filter. The received signal intensity value without noise can obtain more accurate distance value, and then use the anchor node closest to the target node and the other anchor nodes to construct a differential model to obtain the differential error of the system. The system difference error is removed and the range value is more accurate. Then the method of proportional difference is used to continue to modify the RSSI ranging. The simulation results show that the accuracy of the localization algorithm is better than that of the traditional RSSI localization algorithm. Because the original weighted centroid location is not accurate, the improved weighted centroid method is used to locate the nodes in the underground roadway. The problems of node distribution in roadway environment are analyzed, and the solutions are put forward. On this basis, the method of proportional difference is adopted to correct the distance obtained, and the weighting coefficient is further optimized, so that the positioning accuracy of the node is closer to the real effect.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.5;TP212.9
本文編號：2194318
[Abstract]:The positioning of personnel under the mine is an extremely important part of the safety production of the mine. Wireless sensor network (WSN) technology, as a new technology of information perception, processing and interaction, will bring rapid development and progress for underground safety monitoring system in recent years. The core problem of downhole safety monitoring system is the location of underground nodes. Without positioning technology, the information collection of downhole safety system will lose its fundamental value. Node location technology occupies the most basic and core part in wireless sensor networks. In the special environment under the mine, the traditional GPS positioning method can not meet the need to receive satellite signals and can not meet the location monitoring in the underground environment. At present, the downhole positioning system mainly adopts the method of radio frequency identification, that is, it can only be detected when people are near the radio frequency node, and then the location information can be obtained. However, the location mode of radio frequency identification is only a passive monitoring method. Can not realize the real-time positioning feedback to the downhole personnel, aiming at the problem that the downhole safety monitoring system can not locate in real time, the positioning accuracy is poor, the performance is unstable, Combined with the knowledge of wireless sensor network and the status quo of wireless sensor network location in underground environment, two new underground wireless sensor network node localization methods are proposed, and the two methods are simulated and verified. In this paper, based on the multilateral localization algorithm based on RSSI, the proportional difference coefficient is obtained by using the relationship between anchor nodes. This coefficient is applied to the distance between nodes measured by RSSI method. The measurement of RSSI in different environments requires different propagation models, and the RSSI ranging method is limited by its own conditions. That is, the ranging accuracy of RSSI is much better than that of long range target. The target unknown node first reads the information broadcast by the beacon node in the communication range, obtains the RSSI intensity value, and removes the noise from the RSSI signal by Kalman filter. The received signal intensity value without noise can obtain more accurate distance value, and then use the anchor node closest to the target node and the other anchor nodes to construct a differential model to obtain the differential error of the system. The system difference error is removed and the range value is more accurate. Then the method of proportional difference is used to continue to modify the RSSI ranging. The simulation results show that the accuracy of the localization algorithm is better than that of the traditional RSSI localization algorithm. Because the original weighted centroid location is not accurate, the improved weighted centroid method is used to locate the nodes in the underground roadway. The problems of node distribution in roadway environment are analyzed, and the solutions are put forward. On this basis, the method of proportional difference is adopted to correct the distance obtained, and the weighting coefficient is further optimized, so that the positioning accuracy of the node is closer to the real effect.
【學位授予單位】：江西理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.5;TP212.9
【參考文獻】
相關期刊論文 前10條
1 姚維強;張金藝;鮑深;梁濱;;iBeacon網絡下的區(qū)域化雙層定位體系[J];應用科學學報;2017年01期
2 王千;金光;鈕俊;;一種基于RSSI的混合定位算法[J];傳感技術學報;2015年12期
3 于泉;徐保國;;一種基于RSSI的三角形質心改進算法[J];電子設計工程;2015年03期
4 胡偉;朱西平;文紅;曾曉麗;;基于四面體質心迭代的三維APIT定位算法研究[J];傳感技術學報;2013年10期
5 李慧浩;許寶杰;左云波;吳國新;;基于小波變換和EMD方法提取趨勢項對比研究[J];儀器儀表與分析監(jiān)測;2013年03期
6 謝波;江一夫;嚴恭敏;任宏科;;個人導航融合建筑平面信息的粒子濾波方法[J];中國慣性技術學報;2013年01期
7 肖麗萍;劉曉紅;;一種基于跳數修正的DV-Hop定位算法[J];傳感技術學報;2012年12期
8 劉凱;余君君;譚立雄;;跳數加權DV-Hop定位算法[J];傳感技術學報;2012年11期
9 王長征;湯文亮;徐燕;;無線傳感器網絡中四面體三維質心定位算法[J];傳感器與微系統(tǒng);2012年08期
10 吳俊;張榆鋒;;經驗模態(tài)分解和小波分解濾波特性的比較研究[J];云南大學學報(自然科學版);2012年03期
相關碩士學位論文 前1條
1 劉捚;基于卡爾曼濾波的WSNs定位系統(tǒng)研究與設計[D];華中師范大學;2014年
，本文編號：2194318
本文鏈接：http://sikaile.net/kejilunwen/zidonghuakongzhilunwen/2194318.html
最近更新
教材專著
