×

Neighborhood Kalman estimation for distributed localization in wireless sensor networks. (English) Zbl 1400.94068

Summary: Accurate location information plays an important role in the performance of wireless sensor networks since many mission applications depend on it. This paper proposes a fully distributed localization algorithm based on the concept of data fusion, allowing the full intranodes information including the correlations among estimates to take part in the algorithm. By properly constructing and updating the estimates as well as the corresponding covariance matrices, the algorithm can fuse intranodes information to generate more accurate estimates on the sensor locations with a fast speed. Finally, numerical simulations are given as examples to demonstrate the effectiveness of the algorithm.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)
62M20 Inference from stochastic processes and prediction

Software:

DILAND
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Kalman, R. E., A new approach to linear filtering and prediction problems, Journal of Basic Engineering, 82, 1, 35-45, (1960)
[2] Jazwinski, A. H., Stochastic Processes and Filtering Theory, (2007), Courier Corporation
[3] Lefferts, E. J.; Markley, F. L.; Shuster, M. D., Kalman filtering for spacecraft attitude estimation, Journal of Guidance, Control, and Dynamics, 5, 5, 417-429, (1982)
[4] Julier, S. J.; Uhlmann, J. K.; Hall, D.; Llinas, J., General decentralized data fusion with covariance intersection (ci), Handbook of Data Fusion, (2001), Boca Raton, Fla, USA: CRC Press, Boca Raton, Fla, USA
[5] Mansoor-ul-Haque; Khan, F. A.; Iftikhar, M., Optimized energy-efficient iterative distributed localization for wireless sensor networks, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC ’13), IEEE
[6] Patwari, N.; Ash, J. N.; Kyperountas, S.; Hero, A. O.; Moses, R. L.; Correal, N. S., Locating the nodes: cooperative localization in wireless sensor networks, IEEE Signal Processing Magazine, 22, 4, 54-69, (2005)
[7] Mao, G.; Fidan, B.; Anderson, B. D. O., Wireless sensor network localization techniques, Computer Networks, 51, 10, 2529-2553, (2007) · Zbl 1120.68021
[8] Franceschini, F.; Galetto, M.; Maisano, D.; Mastrogiacomo, L., A review of localization algorithms for distributed wireless sensor networks in manufacturing, International Journal of Computer Integrated Manufacturing, 22, 7, 698-716, (2009)
[9] Pal, A., Localization algorithms in wireless sensor networks: current approaches and future challenges, Network Protocols and Algorithms, 2, 1, 45-73, (2010)
[10] Panwar, A.; Kumar, S. A., Localization schemes in wireless sensor networks, Proceedings of the 2nd International Conference on Advanced Computing and Communication Technologies (ACCT ’12), IEEE
[11] Han, G.; Xu, H.; Duong, T. Q.; Jiang, J.; Hara, T., Localization algorithms of wireless sensor networks: a survey, Telecommunication Systems, 52, 4, 2419-2436, (2013)
[12] Niculescu, D.; Nath, B., Ad hoc positioning system (APS), Proceedings of the IEEE Global Telecommunicatins Conference (GLOBECOM ’01), IEEE
[13] Sheu, J.-P.; Hu, W.-K.; Lin, J.-C., Distributed localization scheme for mobile sensor networks, IEEE Transactions on Mobile Computing, 9, 4, 516-526, (2010)
[14] He, Z.; Ma, Y.; Tafazolli, R., Cooperative localization in a distributed base station scenario, Proceedings of the IEEE 73rd Vehicular Technology Conference (VTC Spring ’11), IEEE
[15] Priyantha, N. B.; Balakrishnan, H.; Demaine, E.; Teller, S., Poster abstract: anchor-free distributed localization in sensor networks, Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys ’03)
[16] Xia, M.; Sun, P.; Wang, X.; Jin, Y.; Chen, Q., Distributed beacon drifting detection for localization in unstable environments, Mathematical Problems in Engineering, 2013, (2013)
[17] Wen, L.; Cui, L.; Chai, S.; Zhang, B., Neighbor constraint assisted distributed localization for wireless sensor networks, Mathematical Problems in Engineering, 2014, (2014)
[18] Khan, U. A.; Kar, S.; Moura, J. M., Distributed sensor localization in random environments using minimal number of anchor nodes, IEEE Transactions on Signal Processing, 57, 5, 2000-2016, (2009) · Zbl 1391.94268
[19] Khan, U. A.; Kar, S.; Moura, J. M. F., DILAND: an algorithm for distributed sensor localization with noisy distance measurements, IEEE Transactions on Signal Processing, 58, 3, 1940-1947, (2010) · Zbl 1392.94628
[20] Zhu, S.; Ding, Z., Distributed cooperative localization of wireless sensor networks with convex hull constraint, IEEE Transactions on Wireless Communications, 10, 7, 2150-2161, (2011)
[21] Hu, L.; Evans, D., Localization for mobile sensor networks, Proceedings of the 10th Annual International Conference on Mobile Computing and Networking (MobiCom ’04), ACM
[22] Rudafshani, M.; Datta, S., Localization in wireless sensor networks, Proceedings of the 6th IEEE International Symposium on Information Processing in Sensor Networks (IPSN ’07)
[23] Win, M. Z.; Conti, A.; Mazuelas, S.; Shen, Y.; Gifford, W. M.; Dardari, D.; Chiani, M., Network localization and navigation via cooperation, IEEE Communications Magazine, 49, 5, 56-62, (2011)
[24] Olfati-Saber, R., Kalman-consensus filter: optimality, stability, and performance, Proceedings of the 48th IEEE Conference on Decision and Control Held Jointly with the 28th Chinese Control Conference (CDC/CCC ’09), IEEE
[25] Carli, R.; Chiuso, A.; Schenato, L.; Zampieri, S., Distributed Kalman filtering based on consensus strategies, IEEE Journal on Selected Areas in Communications, 26, 4, 622-633, (2008)
[26] Moore, D.; Leonard, J.; Rus, D.; Teller, S., Robust distributed network localization with noisy range measurements, Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys ’04), ACM
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.