Showing papers by "Mats Viberg published in 1994"

Blind estimation of multiple co-channel digital signals using an antenna array

Abstract: Proposes a novel approach for separating and estimating multiple co-channel digital signals using an antenna array. The spatial response of the array is unknown. The authors exploit the temporal structure of the digital signals to simultaneously determine the array response and the bit sequence for each signal. Uniqueness of the estimates is established for signals with BPSK modulation format. This new approach is applicable to an unknown array geometry and propagation environment, which is particularly useful in digital mobile communications. Simulation results demonstrate its promising performance. >

A Bayesian approach to auto-calibration for parametric array signal processing

Abstract: A number of techniques for parametric (high-resolution) array signal processing have been proposed in the last few decades. With few exceptions, these algorithms require an exact characterization of the array, including knowledge of the sensor positions, sensor gain/phase response, mutual coupling, and receiver equipment effects. Unless all sensors are identical, this information must typically be obtained by experimental measurements (calibration). In practice, of course, all such information is inevitably subject to errors. Several different methods have been proposed for alleviating the inherent sensitivity of parametric methods to such modelling errors. The technique proposed in the present paper is related to the class of so-called auto-calibration procedures, but it is assumed that certain prior knowledge of the array response errors is available. This is a reasonable assumption in most applications, and it allows for more general perturbation models than does pure auto-calibration. The optimal maximum a posteriori (MAP) estimator for the problem at hand is formulated, and a computationally more attractive large-sample approximation is derived. The proposed technique is shown to be statistically efficient, and the achievable performance is illustrated by numerical evaluation and computer simulation. >

Analysis of the combined effects of finite samples and model errors on array processing performance

Abstract: The principal sources of estimation error in sensor array signal processing applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics While the effects of these errors have been studied individually, their combined effect has not yet been rigorously analyzed The authors undertake such an analysis for the class of so-called subspace fitting algorithms In addition to deriving first-order asymptotic expressions for the estimation error, they show that an overall optimal weighting exists for a particular array and noise covariance error model In a companion paper, the optimally weighted subspace fitting method is shown to be asymptotically equivalent with the more complicated maximum a posteriori estimator Thus, for the model in question, no other method can yield more accurate estimates for large samples and small model errors Numerical examples and computer simulations are included to illustrate the obtained results and to verify the asymptotic analysis for realistic scenarios >

Instrumental variable approach to array processing in spatially correlated noise fields

Abstract: High-performance signal parameter estimation from sensor array data is a problem which has received much attention. A number of so-called eigenvector (EV) techniques such as MUSIC, ESPRIT, WSF, and MODE have been proposed in the literature. The EV techniques for array processing require knowledge of the spatial noise correlation matrix that constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. The IV technique relies on the same basic geometric properties as the EV methods to obtain parameter estimates. However, by exploiting the temporal correlation of the source signals, no knowledge of the spatial noise covariance is required. The asymptotic properties of the IV estimator are examined and an optimal IV method is derived. Computer simulations are presented to study the properties of the IV estimators in samples of practical length. The proposed algorithm is also shown to perform better than MUSIC on a full-scale passive sonar experiment. >

Reception of multiple co-channel digital signals using antenna arrays with applications to PCS

Abstract: Antenna arrays can be used to increase system capacity in PCS networks by supporting multiple co-channel users per cell in receive and in transmit. We propose a novel approach for separating multiple digital signals received at an antenna array. Our approach exploits the temporal structure of digital signals to simultaneously determine the array response and the symbol sequence for each signal. Uniqueness of the estimates is established for signals with BPSK modulation format. This approach for separating digital signals is applicable to an unknown array geometry and propagation environment, which is particularly useful in PCS applications. Simulation results are presented to demonstrate the performance of this new technique. >

A Comparison of Model-Based Detection and Adaptive Sidelobe Cancelling for Radar Array Processing

Abstract: The recent development of fast A/D converters and digital signal processors has considerably affected the modern radar system design. In the most popular configuration to date, the main channel (a conventional beamformer) is digitized along with a number of auxiliary channels. This configuration forms the basis for the adaptive sidelobe canceller (ASLC), which has been proposed for mitigating the influence of jammers that are present in the sidelobes of the main channel of the array. The ASLC can be efficiently implemented in real-time using recursive least-squares techniques, and has been demonstrated to perform well in certain scenarios. However, the ASLC has a number of shortcomings. The method fails, for instance when the target signal is too strong. This drawback can be eliminated by applying a parametric approach. Herein, the exact maximum likelihood (ML) estimator assuming a sinusoidal target signal is derived. The computational complexity of the ML estimator is found to be comparable to that of the ASLC. Initial simulation results indicate that the ML and ASLC methods perform similar at low SNR's, but that the ML estimator does not share the signal cancellation phenomena observed in thw ASLC.

A Bayesian approach to direction finding with parametric array uncertainty

Abstract: With few exceptions, high-resolution source localization algorithms require an exact characterization of the array, including knowledge of the sensor positions, sensor gain/phase response, mutual coupling, and receiver equipment effects. In practice, all such information is inevitably subject to errors. Recently, several different methods have been proposed for alleviating the inherent sensitivity of parametric methods to such modeling errors. The technique proposed herein is related to the class of so-called auto-calibration procedures, but it is assumed that certain prior knowledge of the array response errors is available. The optimal maximum a posteriori (MAP) estimator for the problem at hand is formulated, and a more computationally attractive large-sample approximation is derived. In addition, the performance advantage of the algorithm is illustrated by an example involving a linear array mounted on a flexible structure. >

Optimal direction finding with partially calibrated arrays in spatially correlated noise fields

Abstract: The problem of using a partly calibrated array for maximum likelihood (ML) bearing estimation of possibly coherent signals buried in unknown correlated noise fields is shown to admit a neat solution under fairly general conditions. The ML estimator introduced in this paper (and referred to as MLE) is shown to be asymptotically equivalent to a subspace-based bearing estimator proposed by Wu and Wong (see IEEE Trans. Signal Processing, vol. 42, Sept. 1994) (called UNCLE and re-derived herein by a simpler approach than in the original work). A statistical analysis is performed, proving that the MLE and UNCLE methods are asymptotically equivalent and statistically efficient. In a simulation study, the methods are also found to possess very similar finite-sample properties. >