Source separation and tracking for time-varying systems

Many situations arise when it is desired to recover from a noisy mixture of data both an estimate of a propagating source signal as well as its angular position. Accordingly, the problem of source separation and tracking for time-varying systems of moving sources is studied. Using techniques borrowe...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2008-07, Vol.44 (3), p.1198-1214
Main Authors: Coviello, C.M., Yoon, P.A., Sibul, L.H.
Format: Article
Language:English
Subjects:
Adaptive filters
Aircraft components
Angular position
Decomposition
Decorrelation
Direction of arrival estimation
Estimates
Independent component analysis
Laboratories
Linear algebra
Mathematical models
Sensor arrays
Separation
Signal processing
Signal processing algorithms
Source separation
Studies
Time varying systems
Tracking
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Summary:Many situations arise when it is desired to recover from a noisy mixture of data both an estimate of a propagating source signal as well as its angular position. Accordingly, the problem of source separation and tracking for time-varying systems of moving sources is studied. Using techniques borrowed from independent component analysis (ICA) and numerical linear algebra, a processing solution is formulated. The ULV decomposition (ULVD), a member of the complete orthogonal decompositions (CODs) family is used to normalize and decorrelate the data. Since the ULVD is known to stably update and downdate with time-varying data, this step is named ULVD adaptive whitening. The ICA-based EASI (equivariant adaptive separation based on independence) algorithm is adapted to provide not only separated sources but direction-of-arrival (DOA) estimates. Developed improvements to this technique are an adaptive filter method for estimating the mixing matrix from the newly estimated signals as well as a simple cross-over detection scheme. Finally, the developed solutions are tested in simulation examples using meaningful performance measures.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2008.4655374