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... more
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 ...
Research Interests: Signal Processing, Algorithm, Sensor Arrays, Parameter estimation, Antenna arrays, and 15 moreNavigation, Calibration, Data acquisition, Prior Knowledge, Eigenvalues, Wave propagation, Phase, Antenna Array, Gain, Mutual Coupling, Electrical And Electronic Engineering, Direction Finding, Multiple Signal Classification, Direction of arrival, and Electromagnetic coupling
Research Interests:
Research Interests:
An eigenstructure-based method for direction finding in the presence of sensor gain and phase uncertainties is presented. The method provides estimates of the Directions of Arrival (DOA) of all the radiating sources as well as calibration... more
An eigenstructure-based method for direction finding in the presence of sensor gain and phase uncertainties is presented. The method provides estimates of the Directions of Arrival (DOA) of all the radiating sources as well as calibration of the gain and phase of each sensor in the observing array. The technique is not limited to a specific array configuration and can be implemented in a'ny eigenstructure-based DOA system to improve its performance.
Research Interests:
Research Interests:
We consider the problem of direction finding in the presence of colored noise whose covariance matrix is unknown. We show that the ambient noise covariance matrix can be modeled by a sum of Hermitian matrices known up to a multiplicative... more
We consider the problem of direction finding in the presence of colored noise whose covariance matrix is unknown. We show that the ambient noise covariance matrix can be modeled by a sum of Hermitian matrices known up to a multiplicative scalar. Using this model, we estimate jointly the directions of arrival of the signals and the noise model parameters. We show that under certain conditions, it is possible to obtain unbiased and efficient estimates of the signal direction. The Cramer-Rao bound is used as the principal analysis tool. Computer simulations using the maximum likelihood estimator provide a validation of the analytical results
Research Interests: Multidisciplinary, Linear Matrix Inequalities, Noise, Computer Simulation, Cramer Rao Lower Bound, and 10 moreMaximum Likelihood, Colored Noise, Radio Frequency, Maximum Likelihood Estimation, Covariance Matrix, Direction Finding, Multiple Signal Classification, Direction of arrival, Ambient Noise, and maximum likelihood estimate
A variety of equalizers have been proposed to improve the bit-error rate (BER) of optical fiber communications by reducing the effects of chromatic dispersion (CD), polarization-mode dispersion (PMD), and other fiber impairments.... more
A variety of equalizers have been proposed to improve the bit-error rate (BER) of optical fiber communications by reducing the effects of chromatic dispersion (CD), polarization-mode dispersion (PMD), and other fiber impairments. Therefore, it is of interest to establish the ultimate performance of electrical equalizers under different conditions. The results presented here are based on the fact that maximum-likelihood sequence
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The Ziv-Zakai Lower Bound (ZZLB) on the mean square error (m.s.e.) of time delay estimators is compared with theoretical and computer simulation results for time delay estimation via cross-correlation. Comparisons are made for both... more
The Ziv-Zakai Lower Bound (ZZLB) on the mean square error (m.s.e.) of time delay estimators is compared with theoretical and computer simulation results for time delay estimation via cross-correlation. Comparisons are made for both lowpass and narrowband signal spectra. For both signal spectra it is shown that for sufficiently large time-bandwidth product the correlator performance is very close to the
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ABSTRACT