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- Ottersten, Björn, et al.
(författare)
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Stochastic maximum likelihood estimation in sensor arrays by weighted subspace fitting
- 1989
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Ingår i: Conference Record - Asilomar Conference on Circuits, Systems & Computers. - Pacific Grove, CA, USA : Linköping University. ; , s. 599-603, s. 599-603
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Konferensbidrag (refereegranskat)abstract
- The problem of estimating parameters of multiple narrowband emitter signals from sensor array data is considered. Under the assumption of Gaussian distributed emitter signals, the stochastic maximum-likelihood (ML) technique is known to provide statistically efficient estimates, i.e., it achieves the Cramer-Rao bound (CRB). A multidimensional signal subspace method, termed weighted subspace fitting (WSF), has recently been proposed. It is shown that the WSF and ML estimates are asymptotically identical (for large data records). As a consequence, the WSF method is asymptotically efficient, assuming temporally white Gaussian signal waveforms and noise.
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- Roy, R, et al.
(författare)
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ESPRIT and Uniform Linear Arrays
- 1989
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Ingår i: Proceedings of the 33rd SPIE International Technical Symposium. - : SPIE. ; , s. 370-380
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Konferensbidrag (refereegranskat)abstract
- ESPRIT is a recently developed and patented technique for high-resolution estimation of signal parameters. It exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, and in addition achieves performance improvement as measured by parameter estimate error variance. It is also manifestly more robust with respect to sensor errors (e.g. gain, phase, and location errors) than other methods as well. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering two identical but other-wise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform linear arrays of identical sensor elements. Phased array radars are commonplace in high-resolution direction finding systems, and uniform tapped delay lines (i.e., constant rate A/D converters) are the rule rather than the exception in digital signal processing systems. Such arrays possess many invariances, and are amenable to other types of analysis, which is one of the main reasons such structures are so prevalent. Recent developments in high-resolution algorithms of the signal/noise subspace genre including total least squares (TLS) ESPRIT applied to uniform linear arrays are summarized. ESPRIT is also shown to be a generalization of the root-MUSIC algorithm (applicable only to the case of uniform linear arrays of omni-directional sensors and unimodular cisoids). Comparisons with various estimator bounds, including CramerRao bounds, are presented.
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- Roy, R, et al.
(författare)
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Recent Advances in Multidimensional Sensor Array Signal Processing
- 1989
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Ingår i: Proceedings of the 6th ASSP Multidimensional Signal Processing Workshop.
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Konferensbidrag (refereegranskat)abstract
- Summary form only given, ESPRIT is a patented technique for high-resolution estimation of signal parameters that exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, with virtually no loss in performance as measured by parameter estimate variance. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering tow identical but otherwise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform arrays of identical sensor elements with displacements in more than one dimension. The uniformly sampled phased-array radar is a typical example, and such systems are commonplace in high-resolution direction finding systems. Such arrays possess many invariances in potentially more than one dimension. Recent developments in extending the concepts behind ESPRIT to multiple invariances and multidimensional parameter spaces were examined.
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