Enhanced Bilinear Approach for Sensor Network Self-Localization Using Noisy TOF Measurements


This paper develops a new algorithm for sensor network self-localization, which is an enhanced version of the existing Crocco’s method in [11]. The algorithm explores the noisy time of flight (TOF) measurements that quantify the distances between sensor nodes to be localized and sources also at unknown positions. The newly proposed technique first obtains rough estimates of the sensor node and source positions, and then it refines the estimates via a least squares estimator (LSE). The LSE takes into account the geometrical constraints introduced by the desired global coordinate system to improve performance. Simulations show that the new technique offers superior localization accuracy over the original Crocco’s algorithm under small measurement noise condition.

Share and Cite:

Gao, X. , Yang, L. and Peng, L. (2014) Enhanced Bilinear Approach for Sensor Network Self-Localization Using Noisy TOF Measurements. Journal of Computer and Communications, 2, 23-28. doi: 10.4236/jcc.2014.27004.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Verdone, R., Dardari, D., Mazzini, G. and Conti, A. (2008) Wireless Sensor and Actuator Networks. Elsevier. http://dx.doi.org/10.1007/978-3-540-77690-1
[2] Krishnamachari, B. (2005) Networking Wireless Sensor. Cambridge University Press, Cambridge. http://dx.doi.org/10.1017/CBO9780511541025
[3] Tsow, F., Forzani, E., Rai, A., Wang, R. and Tsui, R. (2009) A Wearable and Wireless Sensor System for Real-Time Monitoring of Toxic Environmental Volatile Organic Compounds. IEEE Sensors Journal, 9, 1734-1740. http://dx.doi.org/10.1109/JSEN.2009.2030747
[4] Moore, D., Leonard, J., Rus, D. and Teller, S. (2004) Robust Distributed Network Localization with Noisy Range Measurement. Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, 50-61.
[5] Borg, I. and Groenen, P. (2005) Modern Multidimensional Scaling: Theory and Applications. Springer.
[6] Lin, L., So, H.C., Frankie, F.K.W., Chan, T.Y. and Ho, K.C. (2013) A New Constrained Weighted Least Squares Algorithm for TDOA-Based Localization. Signal Processing, 93, 2872-2878. http://dx.doi.org/10.1016/j.sigpro.2013.04.004
[7] McCowan, I., Lincoln, M. and Himawan, I. (2008) Microphone Array Shape Calibration in Diffuse Noise Fields. IEEE Transactions on Audio, Speech, Language Processing, 16, 666-670. http://dx.doi.org/10.1109/TASL.2007.911428
[8] Moses, R.L., Krishnamurthy, D. and Patterson, R.M. (2003) Self-Localization Method for Wireless Sensor Networks. EURASIP Journal on Applied Signal Processing, 348-358. http://dx.doi.org/10.1155/S1110865703212063
[9] Mensing, C. and Plass, S. (2006) Positioning Algorithms for Cel-lular Networks Using TDOA. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4, 513-516.
[10] Guevara, J., Jimenez, A.R., Prieto, J.C. and Seco, F. (2012) Auto-Localization Algorithm for Local Positioning Systems. Ad Hoc Networks, 10, 1090-1100. http://dx.doi.org/10.1016/j.adhoc.2012.02.003
[11] Crocco, M., Bue, A.D. and Murino, V. (2012) Bilinear Approach to the Position Self-Calibration of Multiple Sensors, IEEE Transaction on Signal Processing, 60, 660-673. http://dx.doi.org/10.1109/TSP.2011.2175387
[12] Torrieri, D.J. (1984) Statistical Theory of Passive Location Systems. IEEE Transactions on Aerospace Electronic Systems, AES-20, 183-198. http://dx.doi.org/10.1109/TAES.1984.310439

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.