GNSS World of China
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| Citation: | NIE Dawei, ZHU Hai, WU Fei, HAN Xuefa. Weighted positioning method based on RSSI probability distribution and Bayesian estimation[J]. GNSS World of China, 2022, 47(2): 52-59. doi: 10.12265/j.gnss.2021080902
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Aiming at the problem that the traditional Wi-Fi positioning technology based on distance measurement does not consider the distribution characteristics of received signal strength indication (RSSI) values, which may result in poor indoor positioning results, this paper proposes a weighted positioning method based on RSSI probability distribution and Bayesian estimation. On the basis of studying the stationarity and distribution characteristics of RSSI. The method introduces the prior RSSI probability distribution into to the calculation of weight through Bayesian estimation. It can also give lower weights to outliers go as to reduce the impact of environmental noise and external uncertain factors on the positioning accuracy, then the position with the largest weight will be taken as the positioning result. Experimental results show that compared with results of trilateral localization, weighted centroid localization and weight correction algorithm, the average error of this method is reduced by 45.4%, 14.6%, 8.2%, and the error of cumulative probability within 50% is reduced by 66.7%, 42.1%, 32.4%.
| [1] |
DAVIDSON P, PICHÉ R. A survey of selected indoor positioning methods for smartphones[J]. IEEE communications surveys and tutorials. 2017, 19(2): 1347-1370. DOI: 10.1109/COMST.2016.2637663
|
| [2] |
REN A, ZHOU F, RAHMAN A, et al. A study of indoor positioning based on UWB base-station configurations[C]//2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017. DOI: 10.1109/IAEAC.2017.8054352
|
| [3] |
CHEN D Y, SHIN G K , JIANG Y R, et al. Locating and tracking BLE beacons with smartphones[C]//The 13th International Conference on emerging Networking EXperiments and Technologies, 2017: 263-275. DOI: 10.1145/3143361.3143385
|
| [4] |
DONG Z Y, XU W M, ZHUANG H. Research on ZigBee indoor technology positioning based on RSSI[J]. Procedia computer science, 2019(154): 424-429. DOI: 10.1016/j.procs.2019.06.060
|
| [5] |
SAAB S S, NAKAD Z S. A standalone RFID Indoor positioning system using passive tags[J]. IEEE transactions on industrial electronics. 2011, 58(5): 1961-1970. DOI: 10.1109/TIE.2010.2055774
|
| [6] |
KUL G, ÖZYER T, TAVLI B. IEEE 802.11 WLAN based real time indoor positioning: literature survey and experimental investigations[C]//The 9th International Conference on Future Networks and Communications (FNC'14), 2014, 34: 157-164. DOI: 10.1016/j.procs.2014.07.078
|
| [7] |
叶佳卉. 基于TDOA的室内定位算法设计与实现[D]. 武汉: 华中科技大学, 2013.
|
| [8] |
许佩佩. 基于TOA方案的超宽带室内定位技术研究[D]. 南京: 东南大学, 2016.
|
| [9] |
KOTARU M, JOSHI K, BHARADIA D, et al. SpotFi: decimeter level localization using Wi-Fi[J]. Computer communication review, 2015, 45(4): 269-282. DOI: 10.1145/2785956.2787487
|
| [10] |
YOUSSEF M, AGRAWALA A. The horus WLAN location determination system[J]. Wireless networks, 2008(14): 357-374. DOI: 10.1007/s11276-006-0725-7
|
| [11] |
LEE S, KIM J, MOON N. Random forest and Wi-Fi fingerprint-based indoor location recognition system using smart watch[J]. Human-centric computing and information sciences. 2019, 9(1): 6. DOI: 10.1186/s13673-019-0168-7
|
| [12] |
李世鹏. 基于RSSI概率分布的室内定位技术的研究与实现[D]. 长春: 东北师范大学, 2019.
|
| [13] |
RU L L, ZHANG L H. A weighted centroid localization algorithm for wireless sensor networks based on weight correction[C]//The 9th International Conference on Advanced Infocomm Technology (ICAIT), 2017: 165-169. DOI: 10.1109/ICAIT.2017.8388908
|
| [14] |
SADOWSKI S, SPACHOS P. RSSI-Based indoor localization with the internet of things[J]. IEEE access, 2018(6): 30149-30161. DOI: 10.1109/ACCESS.2018.2843325
|
| [15] |
严志. 一种基于高斯和滤波的蓝牙信标室内定位算法[J]. 全球定位系统, 2021, 46(3): 94-98. DOI: 10.12265/j.gnss.2020110602
|
| [16] |
LI L G, WU Y F, REN Y J, et al. A RSSI localization algorithm based on interval analysis for indoor wireless sensor networks[C]//IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, 2013. DOI: 10.1109/GreenCom-iThings-CPSCom.2013.90
|
| [17] |
方震, 赵湛, 郭鹏, 等. 基于RSSI测距分析[J]. 传感技术学报, 2007, 20(11): 170-174.
|
| [18] |
刘雨, 肖本贤, 尹柏强. 基于修正RSSI值的四边形加权质心定位算法[J]. 电子测量与仪器学报, 2020, 34(10): 107-114.
|
| [19] |
刘宏立, 周登, 徐琨, 等. 基于RSSI的自适应权重定位算法[J]. 传感器与微系统, 2017, 36(3): 140-143.
|
| [20] |
XIE S G, HU Y J, WANG Y. Weighted centroid localization algorithm based on least square for wireless sensor networks[C]// IEEE International Conference on Consumer Electronics-China. 2014. DOI: 10.1109/ICCE-China.2014.7029867
|
| [21] |
BOSE A, FOH C H. A practical path loss model for indoor WiFi positioning enhancement[C]//The 6th International Conference on Information, Communications & Signal Processing, 2007. DOI: 10.1109/ICICS.2007.4449717
|
| [22] |
LI G Q, GENG E X, YE Z Y, et al. Indoor positioning algorithm based on the improved RSSI distance model[J]. Sensors, 2018, 18(9): 2820. DOI: 10.3390/s18092820
|
| [23] |
WANG Z M, ZHENG Y. The study of the weighted centroid localization algorithm based on RSSI[C]// International Conference on Wireless Communication and Sensor Network, 2014. DOI: 10.1109/WCSN.2014.63
|
| [24] |
陈双金. 时间序列单位根检验方法比较[D]. 成都: 电子科技大学, 2013.
|
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