| 21. |
- Costa, Mario, et al.
(författare)
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Array Processing in the Face of Nonidealities
- 2014
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Ingår i: Academic Press Library in Signal Processing: Volume 3 Array and Statistical Signal Processing. - 9780124115972 ; , s. 819-857
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Real-world sensor arrays are typically composed of elements with individual directional beampatterns and are subject to mutual coupling, cross-polarization effects as well as mounting platform reflections. Errors in the array elements’ positions are also common in sensor arrays built in practice. Such nonidealities need to be taken into account for optimal array signal processing and in finding related performance bounds. Moreover, problems related to beam-steering and cancellation of the signal-of-interest in beamforming applications may be prevented. Otherwise, an array processor may experience a significant performance degradation. In this chapter we provide techniques that allow the practitioner to acquire the steering vector model of real-world sensor arrays so that various nonidealities are taken into account. Consequently, array processing algorithms may avoid performance losses caused by array modeling errors. These techniques include model-based calibration and auto-calibration methods, array interpolation, as well as the wavefield modeling principle or manifold separation technique. Robust methods are also briefly considered since they are useful when the array nonidealities are not described by the employed steering vector model. Extensive array processing examples related to direction-finding and beamforming are included demonstrating that optimal or close-to optimal performance may be achieved despite the array nonidealities.
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| 22. |
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| 23. |
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| 24. |
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| 25. |
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| 26. |
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| 27. |
- Feng, S, et al.
(författare)
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3D Face Recognition Using Affine Integral Invariants
- 2006
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Ingår i: IEEE international conf. on ASSP (ICASSP-06). ; II, s. 189-192
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Konferensbidrag (refereegranskat)abstract
- A new 3D face representation and recognition approach is presented in this paper. Two sets of facial curves are extracted from a face range image, and a novel facial feature representation, the affine integral invariant, is introduced to mitigate the effect of pose on the facial curves. A human face is shown to be representable by a smallsubset of those affine integral invariant curves. A recognition procedure based on the Discriminant Analysis and Jensen-Shannon Divergence analysis is proposed. Substantiating examples are provided with an achieved classification accuracy of 92.57%.
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| 28. |
- Ferreol, A., et al.
(författare)
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A New Expression of the Asymptotic Performances of Maximum Likelihood DOA Estimation Method With Modeling Errors
- 2004
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Ingår i: Proceeding EURASIP 2004, Vienna, Austria.. - 2219-5491. - 9783200001657 ; , s. 2163-2166
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Konferensbidrag (refereegranskat)abstract
- This paper provides a new analytic expression of the RMS (Root Mean Square) error and bias of the Maximum Likelihood (ML) Direction Of Arrival (DOA) estimator in the presence of steering vectors modeling errors. The reference [4] proposes a first order approximation of these performances which is adapted to small modeling errors. In order to take into account larger modeling errors and provide tools for designing experimental set-up, a more accurate and easily usable derivation of these performances is necessary For such an investigation, the DOA estimation errors are written as an hermitean form with a stochastic vector composed by the modeling errors. Finally, a closed form expression between the performances (bias and RMS error) and statistical moments of the model error are deduced from the statistics of the hermitean form. Simulations confirm the theoretical results.
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| 29. |
- Ferreol, A., et al.
(författare)
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Modeling error sensitivity of the MUSIC algorithm conditioned on resolved sources
- 2008
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Ingår i: European Signal Processing Conference. - 2219-5491.
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Konferensbidrag (refereegranskat)abstract
- When the correlation matrix is known, the resolution power of subspace algorithms is infinite. In the presence of modelling errors, even if the correlation matrix is known, sources can no longer be resolved with certainty. Focusing on the MUSIC algorithm [1], the purpose of this work is to provide closed form expression of bias and variance versus the model mismatch (these errors can be different for each source). Un-like previous work, these performance measures are derived conditioned on the success of a certain source resolution test. Among the resolution definitions proposed in [2], we investigate which one is more suitable for our purposes. Numerical results support the theoretical investigations. Our findings are of a great interest for the determination of the necessary antenna calibration accuracy to achieve specifications on the estimator performance.
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| 30. |
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