基于卡爾曼濾波的接收信號強度指示差值定位算法
doi: 10.11999/JEIT180268
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1.
杭州電子科技大學(xué)電子信息學(xué)院 ??杭州 ??310018
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2.
中國電波傳播研究所 ??青島 ??266107
Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter
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1.
College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
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2.
China Research Institute of Radioware Propagation, Qingdao 266107, China
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摘要:
針對頻譜監(jiān)測系統(tǒng)中被監(jiān)測信號無法控制并且沒有任何先驗知識，只能通過對信號被動監(jiān)測，即接收與處理信號來估計信號源位置的要求，該文提出一種基于接收信號強度指示差值(RSSID)的定位算法，并利用卡爾曼濾波提高其定位精度。該文將兩監(jiān)測站之間的RSSID轉(zhuǎn)換成信號源到兩監(jiān)測站的距離之比，根據(jù)距離之比構(gòu)造定位方程矩陣，進而利用最小二乘法求取信號源位置。仿真結(jié)果表明：所提算法比經(jīng)典RSSI定位算法性能更優(yōu)，降低了環(huán)境因素對定位精度的影響，并且能更好地滿足參數(shù)較少的定位服務(wù)需求，可以有效地應(yīng)用于頻譜監(jiān)測系統(tǒng)中。同時，卡爾曼濾波可以有效改善系統(tǒng)的定位精度，達到預(yù)期的定位效果。
Abstract:The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to construct the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.
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表 1 單目標(biāo)定位10000次誤差統(tǒng)計分析(km)
是否預(yù)處理 定位方法 最大誤差 最小誤差 平均誤差 否 RSSI定位 5.0802 0.0239 1.3993 否 RSSID定位 4.6224 0.0076 0.8527 是 RSSI定位 1.4537 0.2273 0.6249 是 RSSID定位 0.8801 0.0068 0.2683 
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表 2 多目標(biāo)定位10000次平均誤差統(tǒng)計分析(km)
是否預(yù)處理 定位方法 最大誤差 最小誤差 平均誤差 否 RSSI定位 1.8602 1.3599 1.5911 否 RSSID定位 1.1015 0.7620 0.9170 是 RSSI定位 1.5312 0.9470 1.1530 是 RSSID定位 0.8284 0.1948 0.2930
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王先義, 陳丹俊, 劉斌, 等. 復(fù)雜電磁環(huán)境戰(zhàn)場頻譜管理[J]. 中國電子科學(xué)研究院學(xué)報, 2008, 3(4): 338–344. doi: 10.3969/j.issn.1673-5692.2008.04.002WANG Xianyi, CHEN Danjun, LIU Bin, et al. Battlefield spectrum management in complex electromagnetic environment[J]. Journal of China Academy of Electronics and Information Technology, 2008, 3(4): 338–344. doi: 10.3969/j.issn.1673-5692.2008.04.002 史小紅. 基于TDOA 的無線定位方法及其性能分析[J]. 東南大學(xué)學(xué)報(自然科學(xué)版), 2013, 43(2): 252–257. doi: 10.3969/j.issn.1001-0505.2013.02.005SHI Xiaohong. TDOA-based wireless positioning method and its performance[J]. Journal of Southeast University (Natural Science Edition) , 2013, 43(2): 252–257. doi: 10.3969/j.issn.1001-0505.2013.02.005 WARNER G A and DOSSO S E. Bowhead whale localization using time-difference-of-arrival data from asynchronous recorders[J]. Journal of the Acoustical Society of America, 2017, 141(3): 1921. doi: 10.1121/1.4978438 WEI Junming and XU Changbin. Improvement of software defined radio based TDOA source localization[C]. Annual Conference of the IEEE Industrial Electronics Society, Dallas, USA, 2015: 5307–5313. LIU Congfeng, YANG Jie, and WANG Fengshuai. Joint TDOA and AOA location algorithm[J]. Journal of Systems Engineering & Electronics, 2013, 24(2): 183–188. doi: 10.1109/JSEE.2013.00023 NOROOZI A. Algebraic solution of source location estimation using TDOA and AOA measurements[C]. Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, 2017: 1609–1614. 曲付勇, 孟祥偉. 基于約束總體最小二乘方法的到達時差到達頻差無源定位算法[J]. 電子與信息學(xué)報, 2014, 36(5): 1075–1081. doi: 10.3724/SP.J.1146.2013.01019QU Fuyong and MENG Xiangwei. Source localization using TDOA and FDOA measurements based on constrained total least squares algorithm[J]. Journal of Electronics &Information Technology, 2014, 36(5): 1075–1081. doi: 10.3724/SP.J.1146.2013.01019 LU Zhiyu, BA Bin, WANG Jianhui, et al. A direct position determination method with combined TDOA and FDOA based on particle filter[J]. Chinese Journal of Aeronautics, 2018, 31(1): 161–168. doi: 10.1016/j.cja.2017.11.007 陳淑敏, 喬曉田, 毛佳, 等. 基于接收信號強度(RSSI) 的室內(nèi)二次定位方法[J]. 傳感技術(shù)學(xué)報, 2015, 28(4): 572–577. doi: 10.3969/j.issn.1004-1699.2015.04.020CHEN Shumin, QIAO Xiaotian, MAO Jia, et al. RSSI-based twice location method for indoor positioning[J]. Chinese Journal of Sensors and Actuators, 2015, 28(4): 572–577. doi: 10.3969/j.issn.1004-1699.2015.04.020 ZHENG Jungang, WU Chengdong, HAO Chu, et al. Localization algorithm based on RSSI and distance geometry constrain for wireless sensor network[C]. IEEE International Conference on Electrical and Control Engineering, Wuhan, China, 2010: 2836–2839. TAZAWA R, HONMA N, MIURA A, et al. RSSI-based localization using wireless beacon with three-element array[J]. IEICE Transactions on Communications, 2018, 101(2). doi: 10.1587/transcom.2017ISP0027 MARC A K J and KAZUNORI O. LRD: A distributed and accurate localization technique for wireless sensors networks[C]. TENCON 2010-2010 IEEE Region 10 Conference, Fukuoka, Japan, 2010: 234–239. 謝樹果, 葉知秋, 王磊, 等. 一種基于場強測量的輻射源定位方法[J]. 電波科學(xué)學(xué)報, 2013, 28(6): 1133–1138. doi: 10.13443/j.cjors.2013.06.024XIE Shuguo, YE Zhiqiu, WANG Lei, et al. Radio source locating method based on signal strength measurement[J]. Chinese Journal of Radio Science, 2013, 28(6): 1133–1138. doi: 10.13443/j.cjors.2013.06.024 卞國龍, 黃海松, 王安憶, 等. 無線傳感器網(wǎng)絡(luò)定位技術(shù)的研究[J]. 實驗室研究與探索, 2017, 36(6): 122–127. doi: 10.3969/j.issn.1006-7167.2017.06.029BIAN Guolong, HUANG Haisong, WANG Anyi, et al. Research on localization technology of wireless sensor networks[J]. Research and Exploration in Laboratory, 2017, 36(6): 122–127. doi: 10.3969/j.issn.1006-7167.2017.06.029 JUNG J Y, KANG D O, CHOI J H, et al. D2D distance measurement using Kalman filter algorithm for distance-based service in an office environment[C]. International Conference on Advanced Communication Technology (ICACT), Seoul, South Korea, 2015: 221–224. ZHANG Kaibi, ZHANG Yangchuan, and WAN Subo. Research of RSSI indoor ranging algorithm based on Gaussian - Kalman linear filtering[C]. IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi'an, China, 2016: 1628–1632. YU Zongzuo and GUO Gaizhi. Improvement of positioning technology based on RSSI in ZigBee networks[J]. Wireless Personal Communications, 2016, 95(3): 1–20. doi: 10.1007/s11277-016-3860-1 ZHOU Cheng, YUAN Jiazheng, LIU Hongzhe, et al. Bluetooth indoor positioning based on RSSI and Kalman filter[J]. Wireless Personal Communications, 2017, 96(3): 1–16. doi: 10.1007/s11277-017-4371-4 方震, 趙湛, 郭鵬, 等. 基于RSSI測距分析[J]. 傳感技術(shù)學(xué)報, 2007, 20(11): 2526–2530. doi: 10.3969/j.issn.1004-1699.2007.11.037FANG Zhen, ZHAO Zhan, GUO Peng, et al. Analysis of distance measurement based on RSSI[J]. Chinese Journal of Sensors and Actuators, 2007, 20(11): 2526–2530. doi: 10.3969/j.issn.1004-1699.2007.11.037 -
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