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Abstract - SSAP5 |
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SSAP5.1 |
A Polynomial Rooting Approach to the Localization of Coherently Scattered Sources J. Goldberg, H. Messer (Tel Aviv University, Israel) The problem of passive localization of coherently scattered sources with an array of sensors is considered. The spatial extent of such a source is typically characterized by an angular mean and an angular spreading parameter. The maximum likelihood estimator for this problem requires a complicated search of dimension equal to twice the number of sources. However, a previously reported sub-optimal MUSIC type method reduces the search dimension to two. In this paper, the search over the angular mean parameter in the above MUSIC type technique is replaced by a potentially more efficient polynomial rooting procedure. Computer simulations verify that for sufficiently high polynomial order and sufficiently high SNR, the proposed method achieves the Cramer-Rao Bound. |
| SSAP5.2 |
Generalised Spatial Smoothing Using Partial Arrays for Multiple Target Bearing Estimation Y. Abramovich, N. Spencer (CSSIP, Australia) This paper considers the problem of bearing estimation for a small number of radar targets which cannot be resolved in range or Doppler frequency. Bearing estimation for non-fluctuating targets involves a single "snapshot" resulting from a multi-channel optimum (matched) filtering process. The standard spatial smoothing technique may be applied to this single-snapshot model, but only for uniform linear antenna arrays. Here we introduce a special class of nonuniform geometry with embedded "partial arrays" and a corresponding "generalised spatial smoothing" (GSS) algorithm. The partial array characteristics determine the resulting bearing estimation accuracy. A two-stage bearing estimation procedure is proposed. The initialization stage involves spatial averaging over all suitable partial arrays. The refinement stage uses a local maximum-likelihood search. Typical radar model simulations and Cramer-Rao bound calculations demonstrate the efficiency of this approach compared with standard spatial smoothing using a uniform linear array. |
| SSAP5.3 |
Maximum Likelihood Propagation-Delay Estimation in Unknown Correlated Noise Using Antenna Arrays: Application to Global Navigation Satellite Systems G. Seco, J. Fernandez (Universitat Politecnica de Catalunya, Spain) The problem of estimating the propagation-delay of a desired signal in the presence of interferences and multipath propagation is adressed. This paper presents the maximum likelihood (ML) propagation-delay estimator for a signal arriving at a sensor array. The novel characteristic herein is that the desired signal impinges on the array with a known steering vector. This fact allows to assume an unknown and arbitrary spatially colored noise. The Cramer-Rao bound (CRB) for the problem at hand is derived and numerically compared with the variance of the MLE. The MLE is applied to the Global Navigation Satellite Systems, in order to reduce the serious performance deterioration that the interferences and the multipath progation produce. We show that in the presence of coherent reflections of the desired signal the presented estimator is no longer the MLE and becomes biased. However, its bias is much lower than that of other conventional estimators. |
| SSAP5.4 |
Modified IQML and a Statistically Efficient Method for Direction Estimation without Eigendecomposition M. Kristensson, M. Jansson, B. Ottersten (Royal Institute of Technology, Sweden) This paper deals with direction estimation of signals impinging on a uniform linear sensor array. A well known algorithm for this problem is IQML. Unfortunately, the IQML estimates are in general biased, especially in noisy scenarios. We propose a modification of IQML (MIQML) that gives consistent estimates at approximately the same computational cost. In addition, an algorithm with an estimation error covariance which is asymptotically identical to the asymptotic Cramer-Rao lower bound is presented. The optimal algorithm resembles weighted subspace fitting or MODE, but achieves optimal performance without having to compute an eigendecomposition of the sample covariance matrix. |
| SSAP5.5 |
Linear Constrained Reduced Rank and Polynomial Order Methods H. Guan, R. De Groat, E. Dowling, D. Linebarger (The University of Texas, Dallas, USA) The Subspace-based Reduced Rank and Polynomial Order (RRPO) methods were proposed recently, which estimate a reduced order linear prediction polynomial whose roots are the desired "signal roots". In this paper, we describe how to extend the RRPO methods to include constraints involving known signal information. The usefulness of the proposed constrained RRPO methods is demonstrated by an application to DOA findings over a wide range of scenarios. Simulation results indicate that by incorporating known signal information such as source direction angle, the estimation of unknown source directions can be significantly improved, especially when the unknown source is weak, closely spaced and highly coherent with the known source. |
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