| 1. |
- Asztély, David, et al.
(författare)
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Generalised Array Manifold Model for Wireless Communication Channels with Local Scattering
- 1998
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Ingår i: IEE Proceedings Radar, Sonar and Navigation. - : IEEE. - 1350-2395. ; 145:1, s. 51-57
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Tidskriftsartikel (refereegranskat)abstract
- The authors propose the use of a generalised array manifold for parameterised spatial signature estimation in wireless communication channels with local scattering. The array manifold commonly used for point sources is generalised to include linear combinations of the nominal array response vectors and their derivatives. The motivation behind this idea is to obtain better estimates of the spatial signatures for direction of arrival (DOA) based signal waveform estimation. The estimators proposed exploit the orthogonality between the so-called noise and signal subspaces, leading to a separable solution for the derivative coefficients. As a result, a search is required for the DOAs only. For uniform linear arrays, the spatial signatures are shown to be approximately Vandermonde vectors with damped modes, and a closed-form estimator such as ESPRIT may be used in this case. Simulation examples are included to compare the signal estimation performance obtained using the proposed generalised manifold and the conventional array manifold.
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| 2. |
- Jansson, Magnus, et al.
(författare)
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Robust Weighted Subspace Fitting in the Presence of Array Model Errors
- 1998
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Ingår i: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing. - : IEEE. ; , s. 1961-1964
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Konferensbidrag (refereegranskat)abstract
- Model error sensitivity is an issue common to all high resolution direction of arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to array calibration errors have also appeared in the literature. Herein, a weighted subspace fitting method for a wide class of array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly enough, the method reduces to the WSF (MODE) estimator if no model errors are present. On the other hand, when model errors dominate, the proposed method turns out to be equivalent to the “model-errors-only subspace fitting method”. Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated.
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| 3. |
- Jansson, Magnus, et al.
(författare)
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Weighted subspace fitting for general array error models
- 1998
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Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 46:9, s. 2484-2498
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Tidskriftsartikel (refereegranskat)abstract
- Model error sensitivity is an issue common to all high-resolution direction-of-arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to army calibration errors have also appeared in the literature. Herein, one such approach is adopted that assumes that the errors due to finite samples and model errors are of comparable size. A weighted subspace fitting method for very general array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly, the method reduces to the WSF (MODE) estimator if no model errors are present, Vice versa, assuming that model errors dominate, the method specializes to the corresponding "model-errors-only subspace fitting method." Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated. The paper also contains a large sample analysis of one of the alternative methods, namely, MAPprox, It is shown that MAPprox also provides minimum variance estimates under reasonable assumptions.
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