Array and multichannel signal processing using nonparametric statistics
No Thumbnail Available
Doctoral thesis (article-based)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Report / Helsinki University of Technology, Signal Processing Laboratory, 31
AbstractIn array signal processing a group of sensors located at distinct spatial locations is deployed to measure a propagating wavefield. The multichannel output is then processed to provide information about parameters of interest. Application areas include smart antennas in communications, radar, sonar and biomedicine. When deriving array signal processing algorithms the noise is typically modeled as a white Gaussian random process. A shortcoming of the estimation procedures derived under Gaussian assumption is that they are extremely sensitive to deviations from the assumed model, i.e. they are not robust. In real-world applications the assumption of white Gaussian noise is not always valid. Consequently, there has been a growing interest in estimation methods which work reliably in both Gaussian and non-Gaussian noise. In this thesis, new statistical procedures for array and multichannel signal processing are developed. In the area of array signal processing, the work concentrates on high-resolution subspace-based Direction Of Arrival (DOA) estimation and estimation of the number of source signals. Robust methods for DOA estimation and estimation of the number of source signals are derived. Spatial-smoothing based extensions of the techniques to deal with coherent signals are also derived. The methods developed are based on multivariate nonparametric statistics, in particular sign and rank covariance matrices. It is shown that these statistics may be used to obtain convergent estimates of the signal and noise subspaces for a large family of symmetric noise distributions. Simulations reveal that the techniques developed exhibit near-optimal performance when the noise distribution is Gaussian and are highly reliable if the noise is non-Gaussian. Multivariate nonparametric statistics are also applied to frequency estimation and estimation of the eigenvectors of the covariance matrix. Theoretical justification for the techniques is shown and their robust performance is illustrated in simulations.
multichannel signal processing, nonparametric statistics, array signal processing, signal modeling, robust estimation, DOA, subspace-based direction of arrival
- S. Visuri, V. Koivunen and H. Oja. Sign and rank covariance matrices. Journal of Statistical Planning and Inference, vol. 91, no. 2, pp. 557-575, 2000.
- S. Visuri, H. Oja and V. Koivunen. Multichannel signal processing using spatial rank covariance matrices. In Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP'99), vol. 1, pp. 75-79, (Antalya, Turkey), June 1999.
- S. Visuri, H. Oja and V. Koivunen. Robust subspace DoA estimation for wireless communications. In Proceedings of the 2000 IEEE 51st Vehicular Technology Conference (VTC2000-Spring), vol. 3, pp. 2551-2555, (Tokyo, Japan), May 2000.
- S. Visuri, H. Oja and V. Koivunen. Nonparametric statistics for DOA estimation in the presence of multipath. In Proceedings of the First IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2000), pp. 356-360, (Cambridge, MA, USA), March 2000.
- S. Visuri, H. Oja and V. Koivunen. Subspace-based direction of arrival estimation using nonparametric statistics. Helsinki University of Technology Signal Processing Laboratory, Report 30, 2000. IEEE Transactions on Signal Processing, accepted for publication with minor revision.
- S. Visuri, H. Oja and V. Koivunen. Nonparametric statistics for subspace based frequency estimation. In Proceedings of the X European Signal Processing Conference (EUSIPCO-2000), vol. 3, pp. 1261-1264, (Tampere, Finland), September 2000.