Jacob Sheinvald

Direction finding of coherent signals via spatial smoothing for uniform circular arrays

We present a preprocessing technique that in conjunction with spatial smoothing circumvents the difficulty of direction-of-arrival estimation of coherent signals in the case of uniform circular arrays. Special consideration is given to problems arising in practice, such as mutual coupling and array geometry imperfections. Simulation results illustrating the performance of this scheme in conjunction with the MUSIC method are included. >

A least-squares approach to joint diagonalization

We present a new least-squares-based approach for the joint diagonalization problem arising in blind beamforming. The resulting estimation criterion turns out to coincide with that proposed by Cardoso and Souloumaic (see IEE Proc. F, Radar Signal Process., vol.140, no.6, p.362-70, Dec. 1993) on intuitive grounds, thus establishing the optimality of their criterion in the least-squares (LS) sense.

Direction finding with fewer receivers via time-varying preprocessing

We present a new technique for localizing multiple narrowband sources by a passive sensor array based on dimensionality-reducing time-varying preprocessing of the sensor outputs. The technique allows a significant reduction in the number of receivers required for the implementation with only two receivers sufficing in the extreme case. The estimation method we use is based on approximating the corresponding maximum likelihood estimator by a computationally simpler generalized least squares (GLS) estimator that is proved to be both consistent and asymptotically efficient. Simulation results confirming the theoretical results are included.

Localization of multiple sources with moving arrays

We consider the problem of localizing multiple narrowband stationary signals using an arbitrary time-varying array such as an array mounted on a moving platform. We assume a Gaussian stochastic model for the received signals and employ the generalized least squares (GLS) estimator to get an asymptotically efficient estimation of the model parameters. In case the signals are a priori known to be uncorrelated, the estimator allows the exploitation of this prior knowledge to its benefit. For the important case of a translational motion of a rigid array, a computationally efficient spatial-smoothing method is presented. Simulation results confirming the theoretical results are included.

Detection and localization in colored noise via generalized least squares

A method for detection and localization of multiple signals in spatially colored noise by an arbitrary passive sensor array is presented. The method also enables exploitation of prior knowledge that the signals are uncorrelated so as to improve the performance and to allow detection and localization even if the number of signals exceeds the number of sensors. The estimation, based on the generalized least squares criterion, is both consistent and asymptotically efficient. The detection is performed via the minimum description length (MDL) principle and is proved to be consistent. Simulation results confirming the theoretical results are included.

On maximum-likelihood localization of coherent signals

We consider the problem of localizing multiple signal sources in the special case where all the signals are known a priori to be coherent. A maximum-likelihood estimator (MLE) is constructed for this special case, and its asymptotical performance is analyzed via the Cramer-Rao bound (CRB). It is proved that the CRB for this case is identical to the CRB for the case that no prior knowledge on coherency is exploited, thus establishing a quite surprising result that, asymptotically, the localization errors are not reduced by exploiting this prior knowledge. Also, we prove that in the coherent case the deterministic signals model and the stochastic signals model yield MLEs that are asymptotically identical. Simulation results confirming these theoretical results are included.

On the achievable localization accuracy of multiple sources at high SNR

The Cramer-Rao bound (CRB) for the problem of localizing multiple signal sources by an arbitrary passive sensor array is analyzed for the general case where the array is not necessarily simultaneously sampled and where the signals may a priori be known to be uncorrelated. It is shown that unlike in the case where the number of samples grows, wherein the CRB for the localization error always converges to zero, in the case where the number of snapshots is kept fixed and the signal-to-noise ratio (SNR) grows, the CRB converges to zero only if the number of sensors simultaneously sampled exceeds the signal subspace dimension.

Localization of correlated and uncorrelated signals in colored noise via generalized least squares

A new method for localizing multiple signals in spatially-colored background noise using an arbitrary passive sensor array is presented. The method enables also to exploit prior knowledge that the signals are uncorrelated, in case such information is available, so as to improve the performance and allow localization even if the number of signals exceeds the number of sensors. The estimation is based on the generalized least squares criterion, and is both consistent and efficient. Simulation results confirming the theoretical results are included.

Localization of multiple signals using subarrays data

A new technique for localisation of multiple signals is presented. Unlike existing techniques which require that the whole array be sampled simultaneously and consequently require many receivers, our technique allows us to sample arbitrary subarrays sequentially and consequently significantly reduces the required number of receivers. The estimation method we use in conjunction with this sampling scheme is based on approximating the corresponding maximum likelihood estimator by a computationally simpler generalized least squares (GLS) estimator that is proved to be both consistent and efficient.