×

Sensor self-localization with beacon position uncertainty. (English) Zbl 1161.94366

Summary: We propose algorithms for distributed sensor self-localization using beacon nodes. These beacon nodes broadcast some information which describes their positions. The sensor nodes with unknown location information utilize these descriptions along with the characteristics of received signals to obtain estimates of their positions. Sensors with resolved positions, in the successive stages of the algorithm also broadcast their location information to other sensors so that they can resolve their own positions. Conditional upon the availability of probabilistic distributions of noise processes, we propose iterative and Monte Carlo sampling-based methods for obtaining sensor location descriptions. We also provide approximate hybrid Cramér-Rao bounds for distributed sensor self-localization and compare them with the proposed algorithms. We demonstrate the performance of the proposed algorithms through extensive computer simulations.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)
93E10 Estimation and detection in stochastic control theory
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Zhao, F.: Sensor networks: an information processing approach, (2003)
[2] Chong, C. Y.; Kumar, S. P.: Sensor networks: evolution, opportunities, and challenges, Proceedings of the IEEE 91, No. 8, 1247-1256 (2003)
[3] R.L. Moses, D. Krishnamurthy, R.M. Patterson, A self-localization method for wireless sensor networks, EURASIP Journal on Applied Signal Processing (2003) 348 – 358. · Zbl 1065.94524 · doi:10.1155/S1110865703212063
[4] Patwari, N.; Iii, A. O. Hero; Perkins, M.; Correal, N. S.; O’dea, R. J.: Relative location estimation in wireless sensor networks, IEEE transactions on signal processing 51, No. 8, 2137-2148 (2003)
[5] Bulusu, N.; Heidemann, J.; Estrin, D.: GPS-less low cost outdoor localization for very small devices, IEEE personal communications magazine 7, 28-34 (2000)
[6] B.M. Sadler, R.J. Kozick, L. Tong, Multimodal sensor localization using a mobile access point, in: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP-05), Philadelphia, 2005, pp. 753 – 756.
[7] D. Niculescu, B. Nath, Ad Hoc positioning system (APS), in: Proceedings of GLOBECOM San Antonio.
[8] Savvides, A.; Park, H.; Srivastava, M. B.: The bits and flops of the n-hop multilateration primitive for node localization problems, , 112-121 (2002)
[9] Langendoen, K.; Reijers, N.: Distributed localization in wireless sensor networks: a quantitative comparison, Computer networks 43, No. 4, 499-518 (2003) · Zbl 1069.68653 · doi:10.1016/S1389-1286(03)00356-6
[10] Sichitiu, M. L.; Ramadurai, V.: Localization of wireless sensor networks with a mobile beacon, (2004)
[11] Galstyan, A.; Krishnamachari, B.; Lerman, K.; Pattem, S.: Distributed online localization in sensor networks using a moving target, , 61-70 (2004)
[12] P.M. Djurić, M. Vemula, M.F. Bugallo, J. Miguez, Non-cooperative localization of binary sensors, in: IEEE Workshop on Statistical Signal Processing, 2005.
[13] Pathirana, P. N.; Bulusu, N.; Savkin, A. V.; Jha, S. K.: Node localization using mobile robots in delay-tolerant sensor networks, IEEE transactions on mobile computing 4, No. 3, 285-296 (2005)
[14] 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, No. 4, 54-69 (July 2005)
[15] Savvides, A.; Girod, L.; Srivastava, M. B.; Estrin, D.: Localization in sensor networks, (2004)
[16] A. Savvides, C.-C. Han, M.B. Srivastava, Dynamic fine-grained localization in Ad-Hoc networks of sensors, in: MOBICOM, 2001, pp. 166 – 179.
[17] Fox, D.; Hightower, J.; Kauz, H.; Liao, L.; Patterson, D.: Bayesian techniques for location estimation, (October 2003)
[18] V. Ramadurai, M.L. Sichitiu, Localization in wireless sensor networks: a probabilistic approach, in: International Conference on Wireless Networks, 2003, pp. 275 – 281.
[19] V. Ceveher, R. Chellapa, J. McClellan, Gaussian approximations for energy-based detection and localization in sensor networks, in: IEEE Statistical Signal Processing Workshop, IEEE, 2007.
[20] D. Marinakis, G. Dudek, Probabilistic self-localization for sensor networks, in: AAAI National Conference on Artificial Intelligence, Boston, MA, 2006.
[21] Ihler, A. T.; Iii, J. W. Fisher; Moses, R. L.; Willsky, A. S.: Nonparametric belief propagation for self-localization of sensor networks, IEEE journal on selected areas in communications 23, No. 4, 809-819 (2005)
[22] Nocedal, J.; Wright, S. J.: Numerical optimization, (1999) · Zbl 0930.65067
[23] Kay, S. M.: Fundamentals of statistical signal processing: estimation theory, (1993) · Zbl 0803.62002
[24] Sayed, A.; Kailath, T.: A state-space approach to adaptive RLS filtering, Signal processing magazine, IEEE 11, No. 3, 18-60 (1994)
[25] Liu, J. S.: Monte Carlo strategies in scientific computing, (2001) · Zbl 0991.65001
[26] Rey, W. J. J.: Introduction to robust and quasi-robust statistical methods, (1983) · Zbl 0525.62040
[27] Van-Trees, H. L.: Detection, estimation, and modulation theory, (1968) · Zbl 0202.18002
[28] Rockah, Y.; Schultheiss, P.: Array shape calibration using sources in unknown locations — part i: Far-field sources, IEEE transactions on acoustics, speech, and signal processing 35, No. 3, 286-299 (1987)
[29] H. Messer, The hybrid Cramer – Rao lower bound — from practice to theory, in: Sensor Array and Multichannel Processing, 2006, pp. 304 – 307.
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.