Novel Acoustic Source Localization Method in WSN Based on LSSVR Regression Modeling


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To solve the problem of acoustic source localization in wireless sensor networks (WSN) under interference of environmental noise, a novel acoustic source localization method in WSN based on Least Square Support Vector Regression (LSSVR) modeling (ASL-LRM) was proposed. The ideal measured values of acoustic sensors were used to compose feature vector at first. Then LSSVR models were built by LSSVR modeling on the mapping relation between feature vector and acoustic source coordinate. The acoustic source was then located by inputting feature vector composed of real measured values of the sensors into LSSVR models. The modeling parameters optimization method based on localization effect in sample locations was also discussed. Experiments were performed in 100 test locations. RMSE values by ASL-LRM method in 72-76 test locations were less than MLE method and reduced by 60%-74% at most. In lower signal-to-noise ratio case, there were 87 test locations where RMSE values by ASL-LRM method were less than 2 meters, while there were only 12 test locations by MLE method. It shows ASL-LRM method achieves better localization effects in a large part of the region surrounded by sensor nodes. It especially has advantage on the occasions like lower signal-to-noise ratio or high precision localization.



Advanced Materials Research (Volumes 468-471)

Edited by:

Wenzhe Chen, Pinqiang Dai, Yonglu Chen, Dingning Chen and Zhengyi Jiang




X. P. Zhang and Y. Wang, "Novel Acoustic Source Localization Method in WSN Based on LSSVR Regression Modeling", Advanced Materials Research, Vols. 468-471, pp. 2296-2303, 2012

Online since:

February 2012




[1] T. C. Collier, A. N. G. Kirschel and C. E. Taylor: Journal of the Acoustical Society of America. Vol. 128(2010), pp.182-189.

[2] G. Vakulya and G. Simon: IEEE Transactions on Instrumentation and Measurement. Vol. 60(2011), pp.1820-1829.

[3] M. Mansouri and L. Khoukhi: Proc. of 7th International Wireless Communications and Mobile Computing Conference. 2011, pp.1837-1842.

[4] S. Bartoletti and A. Conti: Proc. of IEEE International Conference on Ultra-Wideband, 2011, pp.258-262.

[5] X. Sheng, and Y. Hu: IEEE Transactions on Signal Processing, Vol. 53(2005), pp.44-53.

[6] N. Kitakoga and T. Ohtsuki: Proc. of IEEE Vehicular Technology Conference. 2006, pp.2494-2498.

[7] Z. M. Saric, D. D. Kukolj and N. D. Teslic: Circuits, Systems, and Signal Processing. Vol. 29(2010), pp.837-856.

[8] M. Esteki, M. Rezayat, and H. S. Ghaziaskar: Journal of Supercritical Fluids. Vol. 54(2010), pp.222-230.

[9] Guixiong Liu, Xiaoping Zhang and Songbin Zhou: Optics and Precision Engineering. Vol. 17(2009), pp.1766-1773(In Chinese).

[10] De Brabanter K., De Brabanter J., A. K. Suykens Johan: IEEE Transactions on Neural Networks. Vol. 22(2011), pp.110-120.


[11] D. Petropoulos and A. C. Nearchou: Assembly Automation. Vol. 31(2011), pp.118-129.

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