LOCATION ESTIMATION OF BROADBAND SOURCE WITH ADAPTED MUSIC ALGORITHM

Abstract

This study deals with location estimation of the broadband acoustic source in the distributed sensor network. The proposed algorithm is based on the MUSIC (Multiple Signal Classification) algorithm, which solves the localization problem in time domain. The broadband source signal arrives at each sensor with different amplitudes and delay times. Therefore, the signals which are reached at the sensors are the noisy versions of the each other. The collected sensor data is called the observation set, and the cross correlation matrix is calculated by using the observation set. The correlation matrix provides a measure of the similarity between each sensor signal and the relative delay time between the received sensor signals which can be determined by using this data. A reference time point is chosen among the received signals and each time delays are eliminated according to this signal after calculating the cross correlation matrix. Consequently, the source location estimation task is performed by MUSIC algorithm in time domain. Moreover, the simulation results which demonstrate the performance of the method are given in the paper

Keywords

• Broadband source, location estimation, MUSIC algorithm, sensor network

References

1. [1] E.A. Lehman, D.B. Ward, R.C. Williamson, “Experimental Comparison of Particle Filtering Algorithms for Acoustic Source Localization in a Reverberant Room”, Acoustic, Speech, and Signal Processing Procedings (ICASSP), IEEE International Conference, Vol. 5, Issue 6-10, April 2003 pp. V - 177-80.
2. [2] X. Sheng, Y.H. Hu, “Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks”, Signal Processing, IEEE Trans., Vol. 53, Issue 1, Jan. 2005, pp. 44-53.
3. [3] J.C. Chen, K. Yao, R.E. Hudson, “Source Localization And Beamforming”, Signal Processing Magazine IEEE, Vol. 19, Issue 2, Mar., 2002, pp. 30-39.
4. [4] X. Sheng, Y.H. Hu, “Maximum Likelihood Wireless Sensor Network Source Localization Using Acoustic Signal Energy Measurements”, IEEE Tran. Sig. Proc., (2003).
5. [5] J.O. Chen, R.E. Hudson, K. Yao, “Maximum-Likelihood Source Localization and Unknown Sensor Location Estimation for Wideband Signals in the Near-Field”, IEEE Trans. Sig. Proc., Vol. 50, No. 8, Aug. (2002), pp. 1843-1854.
6. [6] S. Valaee, B. Champagne, P. Kabal, “Localization Of Wideband Signals Using LeastSquares And Total Least-Squares Approaches”, IEEE Trans. Signal Proc., Vol. 47, No. 5, May, 1999, pp. 1213-1222.
7. [7] N. Yuen, B. Friedlander, “Performance Analysis of Higher Order ESPRIT for Localization of Near-Field Sources”, IEEE Trans. Signal Proc., Vol. 46, No. 3, Mar., 1998, pp. 709-719.
8. [8] R.O. Schmidt, “Multiple Emitter Location and Signal Parameter Estimation”, IEEE Trans Antennas Propagat., Vol. AP-34, Mar., 1986, pp. 276-280.

9. [9] L. Zhou, Y.J. Zhao, H. Cui, “High Resolution Wideband DOA Estimation Based on Modified MUSIC Algorithm”, Pro. IEEE Int. Conf. Inf. and Auto., 2008, June 20-23, Zhangjiajie, China, pp. 20-22.
10. [10] P. Stoica, A. Nehorai, “MUSIC, Maximum Likelihood, And Cramer-Rao Bound: Further Results And Comparisons”, IEEE Transactions On Acoustic, Speech, And Signal Processing, Vol. 37, No. 5, Dec., 1990, pp. 2140-2150.
11. [11] H. Krim, M. Viberg, “Two Decades Of Array Signal Processing Research”, IEEE Signal Processing Magazine, July, 1996, pp. 67-94.