基于LANDMARC與壓縮感知的雙段式室內(nèi)定位算法

李麗娜; 馬俊; 龍躍; 徐攀峰

doi:10.11999/JEIT151050

基于LANDMARC與壓縮感知的雙段式室內(nèi)定位算法

doi: 10.11999/JEIT151050

1.
(遼寧大學(xué)物理學(xué)院沈陽(yáng) 110036) ②(國(guó)網(wǎng)山東省電力公司電力科學(xué)研究院濟(jì)南 250002)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61403176)，遼寧省教育廳科學(xué)技術(shù)研究項(xiàng)目(L2013003)

計(jì)量
- 文章訪問(wèn)數(shù): 1545
- HTML全文瀏覽量: 211
- PDF下載量: 519
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-09-17
- 修回日期: 2016-03-07
- 刊出日期: 2016-07-19

Double Stage Indoor Localization Algorithm Based on LANDMARC and Compressive Sensing

1.
(College of Physics, Liaoning University, Shenyang 110036, China)

Funds:

The National Natural Science Foundation of China (61403176), Science and Technology Research Project of Educational Commission of Liaoning Province of China (L2013003)

摘要

摘要: 鑒于已有室內(nèi)定位算法定位精度與運(yùn)算效率之間的矛盾，該文提出一種將LANDMARC區(qū)域定位與基于模擬退火優(yōu)化正則化正交匹配追蹤(SROMP)的壓縮感知位置估計(jì)相結(jié)合的雙段式定位算法(LANDMARC- SROMP CS)。首先，利用LANDMARC定位算法快速鎖定目標(biāo)所在區(qū)域范圍；在鎖定的區(qū)域內(nèi)，再引入壓縮感知理論實(shí)現(xiàn)目標(biāo)位置估計(jì)。此部分，首先根據(jù)鎖定區(qū)域范圍建立虛擬參考標(biāo)簽；然后由新型組合核函數(shù)相關(guān)向量機(jī)算法訓(xùn)練得到室內(nèi)傳播損耗模型，計(jì)算獲得虛擬標(biāo)簽處接收信號(hào)強(qiáng)度值，構(gòu)建測(cè)量矩陣；最后利用SROMP壓縮感知重構(gòu)算法求解出目標(biāo)的位置索引矩陣，對(duì)索引矩陣中的位置相關(guān)點(diǎn)加權(quán)平均得到目標(biāo)的位置信息。實(shí)驗(yàn)結(jié)果表明，所提定位算法平均定位誤差為0.6445 m，算法運(yùn)算效率相對(duì)較高，可以較好地滿足室內(nèi)定位的要求。
- 室內(nèi)定位 /
- 壓縮感知 /
- 模擬退火 /
- 正則化正交匹配追蹤 /
- 相關(guān)向量機(jī)
Abstract: In consideration of the contradiction between the positioning accuracy and computational efficiency of the previous indoor positioning algorithm, a double stage positioning algorithm (LANDMARC- SROMP CS) using LANDMARC combined with Compressive Sensing based on the Regularized Orthogonal Matching Pursuit optimized by the Simulated annealing algorithm (SROMP) is put forward. First of all, LANDMARC location algorithm is used to lock the target area quickly; then in the locked area, Compressive Sensing (CS) theory is introduced to realize the target position estimation. In this part, firstly, the virtual reference tags are constructed according to the scale of the locked area; then, the measurement matrix is constructed by the received signal strength data of the virtual reference tags, and the signal strength data are calculated by the indoor propagation loss model which is trained by a new relevance vector machine algorithm based on mixed kernel functions. At last, the SROMP compressive sensing reconstruction algorithm is used to get the position index matrix, and the position information of the target also can be obtained through a simple weighted average calculation. The experimental results show that the average positioning error of the proposed algorithm is only 0.6445 m, and the computation efficiency of the proposed algorithm is relatively high, which can meet the indoor positioning requirements well.
- Indoor localization /
- Compressive Sensing (SC) /
- Simulated annealing /
- Regularized Orthogonal Matching Pursuit (ROMP) /
- Relevance Vector Machine (RVM)

HTML全文

參考文獻(xiàn)(20)

YANG Ting and ZHU Liping. A review of modern indoor localization systems[C]. Proceedings of the 2nd National Conference on Information Technology and Computer Science, Shanghai, 2015: 10-11.

WU Ling, and HUANG Liya. Improvement of location methods based on RFID[J]. The Journal of China Universities of Posts and Telecommunications, 2013, 20(6): 36-41.

LI Chengtie, WANG Jinkuan, and HAN Yinghua. An efficient compressed sensing-based cross-layer congestion control scheme for wireless sensor networks[C]. Proceedings of IEEE 26th Chinese Control and Decision conference, Shanghai, 2014: 5-6.

何風(fēng)行, 余志軍, 劉海濤. 基于壓縮感知的無(wú)線傳感器網(wǎng)絡(luò)多目標(biāo)定位算法[J]. 電子與信息學(xué)報(bào), 2012, 34(3): 716-721. doi: 10.3724/SP.J.1146.2011.00405.

HE Fenghang, YU Zhijun, and LIU Haitao. Multiple target localization via compressed sensing in wireless sensor networks[J]. Journal of Electronics Information Technology, 2012, 34(3): 716-721. doi: 10.3724/SP.J.1146.2011.00405.

MIR Y U, KOPPARAPU V R, and YAND Dongkai. An enhanced K-nearest neighbor algorithm for indoor positioning systems in a WLAN[C]. Proceedings of 2014 IEEE Computers, Communications and IT Applications Conference (ComComAp), Beijing, 2014: 5-8.

ZHANG Rongbiao, GUO Jianguang, CHU Fuhuan, et al. Environmental-adaptive indoor radio path loss model for wireless sensor networks localization[J]. AEU - International Journal of Electronics and Communications, 2011, 65(12): 1023-1031.

MIHALIS A N, HATICE G, and MAJA P. Output- associative RVM regression for dimensional and continuous emotion prediction[J]. Image and Vision Computing, 2012, 30(3): 186-196.

趙春暉, 張燚, 王玉磊. 基于小波核主成分分析的相關(guān)向量機(jī)高光譜圖像分類[J]. 電子與信息學(xué)報(bào), 2012, 34(8): 29-32. doi: 10.3724/SP.J.1146.2011.01282.

ZHAO Chunhui , ZHANG Yi, and WANG Yulei. Relevant vector machine classification of hyperspectral image based on wavelet kernel principal component analysis[J]. Journal of Electronics Information Technology, 2012, 34(8): 29-32. doi: 10.3724/SP.J.1146.2011.01282.

NURCIHAN C. Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks[J]. Journal of African Earth Sciences, 2014, 100(11): 634-644.

CLMENT D and FRDRIC E M. Stationary max-stable processes with the Markov property[J]. Stochastic Processes and their Applications, 2014, 124(6): 2266-2279.

唐朝偉, 李超群, 燕凱, 等. 基于LISOMAP的相關(guān)向量機(jī)入侵檢測(cè)模型[J]. 計(jì)算機(jī)應(yīng)用, 2012, 32(9): 2606-2608.

TANG Chaowei, LI Chaoqun, YAN Kai, et al. Intrusion detection model based on LISOMAP relevant vector machine[J]. Journal of Computer Applications, 2012, 32(9): 2606-2608.

WANG Li, HU Jianfeng, and LIU Yongwei. Proceedings of an adaptive line search scheme for compressed sensing based on nemirovski's algorithm[C]. Proceedings of the 2nd National Conference on Information Technology and Computer Science, Shanghai, 2015: 7-8.

GHAEDI M, GHAEDI A M, HOSSAINPOUR M, et al. Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study[J]. Journal of Industrial and Engineering Chemistry, 2014, 25(4): 1641-1649.

李蘊(yùn)華. 壓縮感知框架下基于ROMP 算法的圖像精確重構(gòu)[J]. 計(jì)算機(jī)應(yīng)用, 2011, 31(10): 2714-2716.

LI Yunhua. Precise image reconstruction based on ROMP algorithm in compressive sensing[J]. Journal of Computer Applications, 2011, 31(10): 2714-2716.

LIN C K Y, Haley K B, and SPARKS C. A comparative study of both standard and adaptive versions of threshold accepting and simulated annealing algorithms in three scheduling problems[J]. European Journal of Operational Research, 1995, 83(2): 330-346.

OLG A S P, EFSTATHIOS Z, FADY R M, et al. Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis[J]. Food Research International, 2013, 50(1): 241-249.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問(wèn)統(tǒng)計(jì)