基于陣列天線和稀疏貝葉斯學(xué)習(xí)的室內(nèi)定位方法
doi: 10.11999/JEIT190314 cstr: 32379.14.JEIT190314
-
1.
西安電子科技大學(xué)雷達(dá)信號處理國家重點(diǎn)實(shí)驗(yàn)室 西安 710071
-
2.
中國測繪科學(xué)研究院 北京 100830
Indoor Localization Algorithm Based on Array Antenna and Sparse Bayesian Learning
-
1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
-
2.
Chinese Academy of Surveying and Mapping, Beijing 100830, China
-
摘要:
由于多徑和非同源等因素的影響，傳統(tǒng)基于藍(lán)牙信號強(qiáng)度的室內(nèi)定位方法的性能精度和穩(wěn)定性都不高。針對基于藍(lán)牙信號的復(fù)雜室內(nèi)環(huán)境定位問題，該文提出基于低成本陣列天線的室內(nèi)定位方法，該方法利用單通道輪采極化敏感陣列天線對藍(lán)牙信號進(jìn)行采樣，然后結(jié)合暗室測量獲得的準(zhǔn)確陣列流形和極化快收斂稀疏貝葉斯學(xué)習(xí)(P-FCSBL)算法實(shí)現(xiàn)信源的角度估計(jì)，最后通過角度實(shí)現(xiàn)定位。該方法充分利用極化信息和角度信息來實(shí)現(xiàn)目標(biāo)和多徑信號的分離，同時(shí)對單信源的同時(shí)采樣保證了估計(jì)的穩(wěn)定性。最后通過實(shí)測數(shù)據(jù)處理驗(yàn)證了該方法的有效性。
-
關(guān)鍵詞:
- 室內(nèi)定位 /
- 極化快收斂稀疏貝葉斯學(xué)習(xí) /
- 極化敏感陣列天線
Abstract:Due to the influence of many factors such as multipath and multi-source, the traditional indoor localization algorithms based on Bluetooth signal strength have low performance in accuracy and stability. In order to solve the location problem in complex indoor environment based on Bluetooth signal, an indoor localization algorithm based on low-cost array antenna is developed. The algorithm utilizes single-channel using switch-antenna polarization sensitive array to sample Bluetooth signal, then combines the accurate array manifold measured in dark room and the algorithm of Polarized Fast Converging Sparse Bayesian Learning (P-FCSBL) to estimate the source’s angle, and finally gets the target location by angle. This algorithm makes full use of polarization information and angle information to separate target and multipath signal, and simultaneous sampling of one source ensures estimation stability. Finally, the effectiveness of the method is verified by the real data.
-
初始化： $ {\left( {{\sigma ^2}} \right)^{\left( 1 \right)}}{\rm{ = }}{\widehat \sigma ^2}$ $ {{{v}}_i} = {{{S}}_i}{{{h}}_i},i = 1,2, ··· ,P$ $ {{D}} = \left[ {{{{v}}_1}\;{{{v}}_2}\; ··· \;{{{v}}_P}} \right]$ $ \gamma _i^{\left( 1 \right)} = \left| {{{v}}_i^{\rm{H}}{{x}}} \right|/\left| {{{v}}_i^{\rm{H}}{{{v}}_i}} \right|,i = 1,2, ··· ,P$ $ {{{C}}^{\left( 1 \right)}} = \displaystyle\sum\limits_{i{\rm{ = }}1}^P {\gamma _i^{\left( 1 \right)}{{v}}_i^{\rm{H}}{{{v}}_i} + {{\left( {{\sigma ^2}} \right)}^{\left( 1 \right)}}{{I}}} $ 迭代： $ {\mu ^{\left( j \right)}} = {{{\varGamma}} ^{\left( j \right)}}{{{D}}^{\rm{H}}}{\left( {{{{M}}^{\left( j \right)}}} \right)^{ - 1}}{{x}}$ $ {\left( {{\sigma ^2}} \right)^{\left( {j + 1} \right)}} = \left( {1/N} \right)\left[ {\left\| {{{x}} - {{D}}{{{\mu}} ^{\left( j \right)}}} \right\|_2^2 + {{\left( {{\sigma ^2}} \right)}^{\left( j \right)}}\displaystyle\sum\limits_1^P {\gamma _i^{\left( j \right)}{{v}}_i^{\rm{H}}{{\left( {{{{C}}^{\left( j \right)}}} \right)}^{ - 1}}{{{v}}_i}} } \right]$ $ \gamma _i^{\left( {j + 1} \right)} = {\left| {\gamma _i^{\left( j \right)}{{v}}_i^{\rm{H}}{{\left( {{{{C}}^{\left( j \right)}}} \right)}^{ - 1}}{{x}}} \right|^2},i = 1,2, ··· ,P$ $ {{{C}}^{\left( {j + 1} \right)}} = \displaystyle\sum\limits_{i{\rm{ = }}1}^P {\gamma _i^{\left( {j + 1} \right)}{{v}}_i^{\rm{H}}{{{v}}_i} + {{\left( {{\sigma ^2}} \right)}^{\left( {j + 1} \right)}}{{I}}} $ 直到得到一個(gè)滿足要求的稀疏解。 
下載: 導(dǎo)出CSV
表 1 兩次實(shí)驗(yàn)定位誤差分析統(tǒng)計(jì)表
實(shí)驗(yàn) 平均定位誤差(m) 誤差≤0.2 m的占比(%) 誤差≤0.4 m的占比(%)) 誤差≤1 m的占比(%) 軌跡1 RSS 1.0858 0 3.48 36.32 軌跡2 RSS 1.0196 0 3.98 40.80 軌跡1 FCSBL 0.1247 87.16 100 100 軌跡2 FCSBL 0.1843 58.29 98.10 100 軌跡1 P-FCSBL 0.0930 90.79 100 100 軌跡2 P-FCSBL 0.0990 93.23 100 100
下載: 導(dǎo)出CSV
-
WANG Bang, CHEN Qiuyun, YANG L T, et al. Indoor smartphone localization via fingerprint crowdsourcing: Challenges and approaches[J]. IEEE Wireless Communications, 2016, 23(3): 82–89. doi: 10.1109/MWC.2016.7498078 陳銳志, 陳亮. 基于智能手機(jī)的室內(nèi)定位技術(shù)的發(fā)展現(xiàn)狀和挑戰(zhàn)[J]. 測繪學(xué)報(bào), 2017, 46(10): 1316–1326. doi: 10.11947/j.AGCS.2017.20170383CHEN Ruizhi and CHEN Liang. Indoor positioning with smartphones: The state-of-the-art and the challenges[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1316–1326. doi: 10.11947/j.AGCS.2017.20170383 周牧, 劉儀瑤, 楊小龍, 等. 基于Wi-Fi即時(shí)定位與映射像素模板匹配的室內(nèi)運(yùn)動地圖構(gòu)建與定位[J]. 電子與信息學(xué)報(bào), 2018, 40(5): 1050–1058. doi: 10.11999/JEIT170781ZHOU Mu, LIU Yiyao, YANG Xiaolong, et al. Indoor mobility map construction and localization based on Wi-Fi simultaneous localization and mapping pixel template matching[J]. Journal of Electronics &Information Technology, 2018, 40(5): 1050–1058. doi: 10.11999/JEIT170781 李方敏, 張韜, 劉凱, 等. 基于距離測量和位置指紋的室內(nèi)定位方法研究[J]. 計(jì)算機(jī)學(xué)報(bào), 2019, 42(2): 339–350. doi: 10.11897/SP.J.1016.2019.00339LI Fangmin, ZHANG Tao, LIU Kai, et al. An indoor positioning method based on range measuring and location fingerprinting[J]. Chinese Journal of Computers, 2019, 42(2): 339–350. doi: 10.11897/SP.J.1016.2019.00339 SADOWSKI S and SPACHOS P. RSSI-based indoor localization with the internet of things[J]. IEEE Access, 2018, 6: 30149–30161. doi: 10.1109/ACCESS.2018.2843325 ZHANG Meiyan and CAI Wenyu. Multivariate polynomial interpolation based indoor fingerprinting localization using Bluetooth[J]. IEEE Sensors Letters, 2018, 2(4): 7001704. doi: 10.1109/LSENS.2018.2878558 GU Yu and REN Fuji. Energy-efficient indoor localization of smart hand-held devices using Bluetooth[J]. IEEE Access, 2015, 3: 1450–1461. doi: 10.1109/ACCESS.2015.2441694 LOVóN-MELGAREJO J, CASTILLO-CARA M, HUARCAYA-CANAL O, et al. Comparative study of supervised learning and metaheuristic algorithms for the development of Bluetooth-based indoor localization mechanisms[J]. IEEE Access, 2019, 7: 26123–26135. doi: 10.1109/ACCESS.2019.2899736 SUBEDI S, GANG H S, KO N Y, et al. Improving indoor fingerprinting positioning with affinity propagation clustering and weighted centroid fingerprint[J]. IEEE Access, 2019, 7: 31738–31750. doi: 10.1109/ACCESS.2019.2902564 XIAO Chao, YANG Daiqin, CHEN Zhenzhong, et al. 3-D BLE indoor localization based on denoising autoencoder[J]. IEEE Access, 2017, 5: 12751–12760. doi: 10.1109/ACCESS.2017.2720164 SUN Wei, XUE Min, YU Hongshan, et al. Augmentation of fingerprints for indoor WiFi localization based on Gaussian process regression[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10896–10905. doi: 10.1109/TVT.2018.2870160 田增山, 王陽, 周牧, 等. 基于自適應(yīng)漸消記憶的藍(lán)牙序列匹配定位算法[J]. 電子與信息學(xué)報(bào), 2019, 41(6): 1381–1388. doi: 10.11999/JEIT18063TIAN Zengshan, WANG Yang, ZHOU Mu, et al. Adaptive fading memory based Bluetooth sequence matching localization algorithm[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1381–1388. doi: 10.11999/JEIT18063 GUO Xiansheng, LI Lin, ANSARI N, et al. Accurate WiFi localization by fusing a group of fingerprints via a global fusion profile[J]. IEEE Transactions on Vehicular Technology, 2018, 67(8): 7314–7325. doi: 10.1109/TVT.2018.2833029 YU Ning, ZHAN Xiaohong, ZHAO Shengnan, et al. A precise dead reckoning algorithm based on Bluetooth and multiple sensors[J]. IEEE Internet of Things Journal, 2018, 5(1): 336–351. doi: 10.1109/JIOT.2017.2784386 陳顯舟, 陳周, 楊旭, 等. 陣列單通道輪采式快速高精度定位算法[J]. 現(xiàn)代雷達(dá), 2017, 39(8): 49–53. doi: 10.16592/j.cnki.1004-7859.2017.08.011CHEN Xianzhou, CHEN Zhou, YANG Xu, et al. Fast high-resolution passive localization algorithm based on single-channel using switch-antenna array[J]. Modern Radar, 2017, 39(8): 49–53. doi: 10.16592/j.cnki.1004-7859.2017.08.011 WANG Zetao, XIE Wenchong, DUAN Keqing, et al. Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar[J]. Signal Processing, 2017, 130: 159–168. doi: 10.1016/j.sigpro.2016.06.023 -
計(jì)量
- 文章訪問數(shù): 2725
- HTML全文瀏覽量: 1003
- PDF下載量: 145
- 被引次數(shù): 0
下載:
