Distributed location model and algorithm with position uncertainty

doi:10.1016/S1005-8885(14)60285-5

中国邮电高校学报(英文) ›› 2014, Vol. 21 ›› Issue (2): 48-56.doi: 10.1016/S1005-8885(14)60285-5

Distributed location model and algorithm with position uncertainty

史玉龙¹,崔琪楣²,曹思琦¹,陶小峰³

1. 北京邮电大学
2. 北京邮电大学新技术研究中心
3. 北京邮电大学无线新技术研究所

收稿日期:2013-11-20 修回日期:2014-03-12 出版日期:2014-04-30 发布日期:2014-04-30
通讯作者: 史玉龙 E-mail:shiyulong@bupt.edu.cn
基金资助:
国家自然基金;国家创新研究群体科学基金

Distributed location model and algorithm with position uncertainty

Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2013-11-20 Revised:2014-03-12 Online:2014-04-30 Published:2014-04-30
Contact: Yu-Long SHI E-mail:shiyulong@bupt.edu.cn
Supported by:
the National Natural Science Foundation of China (61001119), the Beijing Higher Education Young Elite Teacher Project (YETP0429), and the National Hi-Tech Research and Development Program of China (2012AA01A50604).

摘要/Abstract

摘要：

The localization of multiple mobile terminals (MTs) is an encouraging paradigm of applications in wireless networks. Peer-to-peer communication between MTs facilitates the cooperative localization of multiple MTs. For sake of low complexity and high robustness, investigations often focus on the distributed algorithm in cooperative localization. However, the impact from position uncertainty of cooperative MT lacks of analysis in related works. A new distributed location model, as well as corresponding algorithm, is devised when considering both the distance measurement error and the position uncertainty of MTs, which is more judicious than the traditional model for distributed cooperative localization scenario. In addition, the performance of proposed algorithm is analyzed through Cramér-Rao lower bound (CRLB). Simulations indicate that the algorithm outperforms traditional methods in terms of accuracy and robustness.

关键词:

distributed algorithm, cooperative localization, position uncertainty

Abstract:

Key words:

distributed algorithm, cooperative localization, position uncertainty

参考文献

1. Cui Q M, Zhang X F. Research analysis of wireless localization with insufficient resources for next-generation mobile communication networks. International Journal of Communication Systems, 2013, 26(9): 1206-1226

2. Wymeersch H, Lien J, Win M Z. Cooperative localization in wireless networks. Proceedings of the IEEE, 2009, 97(2) : 427-450

3. Chan Y T, Ho K C. A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 1994, 42(8): 1905-1915

4. Costa J A, Patwari N, Hero A O. Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Transactions on Sensor Networks, 2006, 2(1): 39-64

5. Chen H, Wang G, Wang Z, et al. Non-line-of-sight node localization based on semi-definite programming in wireless sensor networks. IEEE Transactions on Wireless Communications, 2012, 11(1): 108-116

6. Srirangarajan S, Tewfik A H, Luo Z Q. Distributed sensor network localization using SOCP relaxation. IEEE Transactions on Wireless Communications, 2008, 7(11-1): 4886-4895

7. Tao J, Buehrer R M. A set-theoretic approach to collaborative position location for wireless networks. IEEE Transactions on Mobile Computing, 2011, 10(9): 1264-1275

8. Zhang Y H, Cui Q M, Zhang P, et al. Cooperative localization for next-generation converged networks with closed-form solution. Journal of China Universities of Posts and Telecommunications, 2009, 16(5): 56-61

9. Zhu S, Ding Z. Distributed cooperative localization of wireless sensor networks with convex hull constraint. IEEE Transactions on Wireless Communications, 2011, 10(7): 2150-2161

10. Alsindi N, Pahlava K, Alavi B. A novel cooperative localization algorithm for indoor sensor networks. Proceedings of the IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’06), Sep 11-14, 2006, Helsinki, Finland. Piscataway, NJ, USA: IEEE, 2006: 6p

11. Carter M W, Jin H H, Saunders M A, et al. SPACELOC: an adaptive sub-problem algorithm for scalable wireless sensor network localization. SIAM Journal on Optimization, 2006 ,17(4): 1102-1128

12. Cheung K W, So H C, Ma W K, et al. Least squares algorithms for time-of-arrival-based mobile location. IEEE Transactions on Signal Processing, 2004, 52(4): 1121-1128

13. Huffel V S, Vandewalle J. The total least squares problem: computational aspects and analysis. Philadelphia, PA, USA: SIAM, 1991

14. Lui K W K, Ma W K, So H C, et al. Semi-definite programming algorithms for sensor network node localization with uncertainties in anchor positions and/or propagation speed. IEEE Transactions on Signal Processing, 2009, 57(2): 752-763

15. de Moor B. Structured total least squares and L2 approximation problems. Linear Algebra and Its Applications, 1993, 188-189: 163-205

16. Markovsky I, Rastello M L, Premoli A, et al. The element-wise weighted total least squares problem. Computational Statistics and Data Analysis, 2005, 50 (1): 181-209

17. Guvenc I, Chong C C, Watanabe F, et al. NLOS identification and weighted least-squares localization for UWB systems using multipath channel statistics. EURASIP Journal on Advances in Signal Processing, 2008(36): 271984

18. Kukush A, Huffel S V. Consistency of elementwise-weighted total least squares estimator in a multivariate errors-invariables model AX = B. Metrika, 2004, 59: 75-97

19. Patwari N, Hero A O, Perkins M, et al. Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 2003, 51(8): 2137-2148

Distributed location model and algorithm with position uncertainty

Distributed location model and algorithm with position uncertainty

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价