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Abstract - SSAP2

SSAP2.1	Subspace Domain Forwards-Backwards Averaging M. Zatman (MIT Lincoln Lab, USA) In this paper a procedure which filters out roughly half of the array manifold errors for approximately centro-symmetric arrays is described. The procedure - subspace domain forwards-backwards averaging - improves the performance of subspace based direction finding algorithms such as MUSIC and ESPRIT. Experimental data from the Mountaintop system are used to confirm the theoretical results.
SSAP2.2	Closed-Form Direction-Finding with Arbitrarily Spaced Electromagnetic Vector-Sensors at Unknown Locations K. Wong (Nanyang Technological University, Singapore); M. Zoltowski (Purdue University, USA) This paper introduces a novel closed-form ESPRIT-based algorithm for multi-source direction finding using arbitrarily spaced electromagnetic vector-sensors whose locations need {\em not} be known. The electromagnetic vector-sensor, already commercially available, consists of six co-located but diversely polarized antennas separately measuring all six electromagnetic-field components of an incident wavefield. In this novel algorithm, ESPRIT exploits the non-spatial inter-relationsamong the six unknown electromagnetic-field components of each source and produces from the measured data a set of eigenvalues, from which the source's electromagnetic-field vector may be estimated to within a complex scalar. Application of a vector cross-product operation to this ambiguous electromagnetic-field vector estimate produces an unambiguous estimate of that source's normalized Poynting-vector, which contains as its components the source's Cartesian direction-cosines. Monte Carlo simulation results verify the efficacy and versatility of this innovative scheme.
SSAP2.3	Subspace Tracking using a Constrained Hyperbolic URV Decomposition A. Van Der Veen (Delft University of Technology, The Netherlands) The class of Schur subspace estimators provides a parametrization of all minimal-rank matrix approximants that lie within a specified distance of a given matrix, and in particular gives expressions for the column spans of these approximants. In this paper, we derive an updating algorithm for an interesting member of the class, making use of a constrained hyperbolic URV-like decomposition.
SSAP2.4	Joint Angle-Frequency Estimation Using Multi-Resolution Esprit A. Lemma, A. Van Der Veen, E. Deprettere (Delft University of Technology, The Netherlands) Multi-resolution ESPRIT is an extension of the ESPRIT direction finding algorithm to antenna arrays with multiple baselines. A short (half wavelength) baseline is necessary to avoid aliasing, a long baseline is preferred for accuracy. The MR-ESPRIT algorithm allows to combine both estimates. The same algorithm can be used for multi-resolution frequency estimation using two different sampling frequencies. We show how this can be used to construct a joint angle-frequency estimator.
SSAP2.5	Robust Weighted Subspace Fitting in the Presence of Array Model Errors M. Jansson (Royal Institute of Technology, Sweden); L. Swindlehurst (Brigham Young University, USA); B. Ottersten (Royal Institute of Technology, Sweden) 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 in 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.
SSAP2.6	Robust Adaptive Subspace Detectors for Space Time Processing A. Zeira (Signal Processing Technology Ltd., USA); B. Friedlander (University of California, Davis, USA) In this paper we consider the problem of detecting a subspace signal when there is uncertainty in the subspace. Such uncertainty usually causes a mismatch between the detector and the signal to be detected, which may lead to significant loss in performance. To improve the robustness of the detection procedure we apply robust adaptive subspace detectors based on extending the dimension of the signal subspace. We Consider two types of adaptive constant false alarm rate (CFAR) detector structures: CFAR generalized likelihood ratio detector (CFAR GLR) and CFAR matched subspace detector (CFAR MSD). Using Monte-carlo simulations, we study the performance of the robust adaptive subspace detectors for space-time processing.
SSAP2.7	A Geometrical Framework for the Determination of Ambiguous Directions in Subspace Methods A. Flieller, P. Larzabal, H. Clergeot (LESiR, France) In subspace parameter estimation techniques, like MUCSIC, degradations may occur due to parasite peaks in the pectrum, which may be connected to high sidelobes in the beam pattern or to ambiguities themselves. The aim of this paper is to study the presence of ambiguities in an array of given planar geometry. We propose a general framework for the analysis and therefore we obtain a generalisation of results given in recent publications for rank one and two ambiguities. For rank k greater than three ambiguities, the study is restricted to linear arrays, for which we derive original and synthetic results. We present a geometrical construction able to determine all the ambiguous directions which can appear for a given linear array. The method allows determination of any rank ambiguities and for each ambiguous direction set, the rank of ambiguity is obtained. The search is exhaustive. Application of the method requires no assumption for the linear array and is easy to implement. An example is detailed for a non uniform linear array.
SSAP2.8	Weighted Subspace Fitting Using Subspace Perturbation Expansions R. Vaccaro (University of Rhode Island, USA) This paper presents a new approach to deriving statistically optimal weights for weighted subspace fitting (WSF) algorithms. The approach uses a formula called a ``subspace perturbation expansion,'' which shows how the subspaces of a matrix change when the matrix elements are perturbed. The perturbation expansion is used to derive an optimal WSF algorithm for estimating directions of arrival in array signal processing.
SSAP2.9	Case Study of Principal Component Inverse and Cross Spectral Metric for Low Rank Interference Adaptation B. Freburger, D. Tufts (University of Rhode Island, USA) This paper presents a review of the Principal Component Inverse method of rapidly adaptive signal detection and contrasts the use of Pricipal Components with the more recent Cross Spectral Metric method for the Generalized Sidelobe Canceller.The CSM method is optimal with known statistics and has been shown to outperfrom the PCI method in many cases of unknown covariance. This paper describes a scenario which represents a class of covariances where the PCI method can be expected to outperform the CSM method. The choice of method is therefore more subtle than previously thought.
SSAP2.10	On Sources Covariance Matrix Singularities and High-Resolution Active Wideband Source Localization D. Goncalves, P. Gounon (CEPHAG, France) High resolution eigenstructure-based techniques for signal source localization are known to be ineffective when the source covariance matrix is not of full rank. We present here two techniques to circumvent this problem in the context of wideband active source localization. An extension is made to show how eigenstructure methods can be applied even when there is only one snapshot available to estimate the wideband spectral matrices.

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