無線傳感器網(wǎng)絡(luò)中基于壓縮感知的動態(tài)目標(biāo)定位算法

孫保明; 郭艷; 李寧; 錢鵬

doi:10.11999/JEIT151203

無線傳感器網(wǎng)絡(luò)中基于壓縮感知的動態(tài)目標(biāo)定位算法

doi: 10.11999/JEIT151203

基金項目:

國家自然科學(xué)基金(61571463, 61371124, 61272487, 61472445, 61201217)

計量
- 文章訪問數(shù): 1524
- HTML全文瀏覽量: 104
- PDF下載量: 603
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-10-29
- 修回日期: 2016-03-29
- 刊出日期: 2016-08-19

Mobile Target Localization Algorithm Using Compressive Sensing in Wireless Sensor Networks

Funds:

The National Natural Science Foundation of China (61571463, 61371124, 61272487, 61472445, 61201217)

摘要

摘要: 傳統(tǒng)的動態(tài)目標(biāo)定位算法需要采集、存儲和處理大量數(shù)據(jù)，并不適用于能量受限的無線傳感器網(wǎng)絡(luò)。針對該缺陷，該文提出一種基于壓縮感知的動態(tài)目標(biāo)定位算法。該算法利用目標(biāo)的運動規(guī)律設(shè)計稀疏表示基，從而將動態(tài)目標(biāo)定位問題轉(zhuǎn)化為稀疏信號恢復(fù)問題。針對傳統(tǒng)觀測矩陣難以實現(xiàn)的缺陷，該算法設(shè)計可實現(xiàn)且與稀疏表示基相關(guān)性低的稀疏觀測矩陣，從而保證了算法的重構(gòu)性能。該算法的特點是可利用較少的數(shù)據(jù)采集實現(xiàn)動態(tài)目標(biāo)定位，從而大大延長無線傳感器網(wǎng)絡(luò)的壽命。仿真結(jié)果表明，該文所提出的基于壓縮感知的動態(tài)目標(biāo)定位算法具有較好的定位性能。
- 無線傳感器網(wǎng)絡(luò) /
- 動態(tài)目標(biāo)定位 /
- 壓縮感知 /
- 稀疏表示基 /
- 觀測矩陣
Abstract: Traditional mobile target localization algorithms are not suitable for wireless sensor networks as they need to collect, store, and process a mass of data. To address this issue, a mobile target localization algorithm based on compressive sensing is proposed. Two sparse representation bases are designed by exploiting the movement characteristics of mobile targets, therefore the mobile target localization issue is transferred into a sparse signal recovery issue. To avoid the unpractical limitation of traditional measurement matrices, two sparse measurement matrices are proposed that are practical and lowly coherent with the designed representation bases. The characteristic of this algorithm is that mobile target localization can be achieved by collecting a little data, thus prolonging the lifetime of wireless sensor networks. Simulation results indicate that the proposed localization algorithm based on compressive sensing is highly efficient.
- Wireless sensor networks /
- Mobile target localization /
- Compressive sensing /
- Sparse representation basis /
- Measurement matrix

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參考文獻(23)

AKYILDIZ I F, SU W, SANKARASUBRAMANIAM Y, et al. Wireless sensor networks: a survey[J]. Computer Networks, 2002, 38(4): 393-422.

LIU Y, YANG Z, WANG X, et al. Location, localization, and localizability[J]. Journal of Computer Science and Technology, 2010, 25(2): 274-297.

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 12891306.

CAND E J. Compressive sampling[C]. International Congress of Mathematicians, Madrid, Spain, 2006: 1433-1452.

CEVHER V, DUARTE M, and BARANIUK R G. Distributed target localization via spatial sparsity[C]. Proceedings of the European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, 2008: 25-29.

CHEN F, VALAEE S, and TAN Z. Multiple target localization using compressive sensing[C]. IEEE Global Telecommunications Conference (GLOBECOM), Honolulu, HI, USA, 2009: 1-6.

ZHANG B, CHEN X, ZHANG N, et al. Sparse target counting and localization in sensor networks based on compressive sensing[C]. IEEE International Conference on Computer Communications (INFOCOM), Shanghai, China, 2011: 2255-2263.

何風(fēng)行, 余志軍, 劉海濤. 基于壓縮感知的無線傳感器網(wǎng)絡(luò)多目標(biāo)定位算法[J]. 電子與信息學(xué)報, 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 Tevhnology, 2012, 34(3): 716-721. doi: 10.3724/SP.J.1146. 2011.00405.

趙春暉, 許云龍, 黃輝. 基于LU分解的稀疏目標(biāo)定位算法[J]. 電子與信息學(xué)報, 2013, 35(9): 2234-2239. doi: 10.3724/ SP.J.1146. 2012.01527.

ZHAO Chunhui, XU Yunlong, and HUANG Hui. Localization algorithm of sparse targets based on LU- decomposition[J]. Journal of Electronics Information Tevhnology, 2013, 35(9): 2234-2239. doi: 10.3724/SP.J.1146. 2012.01527.

李一兵, 黃輝, 葉方, 等. 基于奇異值分解的壓縮感知定位算法[J]. 中南大學(xué)學(xué)報(自然科學(xué)版), 2014, 45(5): 1516-1521.

LI Yibing, HUANG Hui, YE Fang, et al. Target localization via compressed sensing based on SVD[J]. Journal of Central South University (Natural Science), 2014, 45(5): 1516-1521.

GUYEN G K, VAN NGUYEN T, and SHIN H. Learning dictionary and compressive sensing for WLAN localization[C]. IEEE Wireless Communications and Networking Conference (WCNC), Istanbul, Turkey, 2014: 2910-2915.

陳偉, 顏俊, 朱衛(wèi)平. 利用壓縮感知與多邊測量技術(shù)的無線傳感器網(wǎng)絡(luò)定位算法[J]. 信號處理, 2014, 30(6): 728-735.

CHEN Wei, YAN Jun, and ZHU Weiping. Wireless sensor network location algorithm using compressive sensing and multilateral measurements[J]. Journal of Signal Processing, 2014, 30(6): 728-735.

王婷婷, 柯煒, 孫超. 自適應(yīng)環(huán)境變化的RSS室內(nèi)定位方法[J]. 通信學(xué)報, 2014, 35(10): 210-217.

WANG Tingting, KE Wei, and SUN Chao. Environmental- adaptive RSS-based indoor localization[J]. Journal on Communications, 2014, 35(10): 210-217.

LIU L, CUI T, and L W. A range-free multiple target localization algorithm using compressive sensing theory in wireless sensor networks[C]. IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Philadelphia, PA, USA, 2014: 690-695.

呂偉杰, 崔婷婷, 劉超, 等. 一種新的基于壓縮感知的WSN多目標(biāo)定位方法[J]. 系統(tǒng)仿真技術(shù), 2015, 11(1): 6-13.

L Weijie, CUI Tingting, LIU Chao, et al. A new multiple target localization based on compressed sensing theory in WSN[J]. System Simulation Technology, 2015, 11(1): 6-13.

SONG C and XIA S. Sparse signal recovery by minimization under restricted isometry property[J]. IEEE Signal Processing Letters, 2014, 21(9): 1154-1158.

WU X and LIU M. In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing[C]. Proceedings of the 11th ACM International Conference on Information Processing in Sensor Networks, Beijing, China, 2012: 1-12.

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