Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Time series: data analysis and theory
Time series: data analysis and theory
Rank estimation in reduced-rank regression
Journal of Multivariate Analysis
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Minimum bias multiple taper spectral estimation
IEEE Transactions on Signal Processing
Finite-length MIMO equalization using canonical correlationanalysis
IEEE Transactions on Signal Processing
Two algorithms for adaptive retrieval of slowly time-varyingmultiple cisoids in noise
IEEE Transactions on Signal Processing
Statistical analysis of subspace-based estimation of reduced-ranklinear regressions
IEEE Transactions on Signal Processing
Statistics on exponential averaging of periodograms
IEEE Transactions on Signal Processing
A multiwindow method for spectrum estimation and sinusoid detectionin an array environment
IEEE Transactions on Signal Processing
Performance degradation of DOA estimators due to unknown noisefields
IEEE Transactions on Signal Processing
Maximum likelihood parameter and rank estimation in reduced-rankmultivariate linear regressions
IEEE Transactions on Signal Processing
Parameter Estimation and Extraction of Helicopter Signals Observed with a Wide-Band Interference
IEEE Transactions on Signal Processing
Optimal reduced-rank estimation and filtering
IEEE Transactions on Signal Processing
Analysis and performance evaluation of an adaptive notch filter
IEEE Transactions on Information Theory
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The enhancement of sinusoids in adverse noisy environments finds applications in many fields. Among the numerous methods that have been proposed to perform this task, few can actually operate without any information on neither the sinusoids nor the noise characteristics. In this paper, we propose a non-parametric spectral approach inspired by the adaptive line enhancer (ALE) principle, which has the capability of detecting and enhancing an arbitrary large number of tones in additive noise of unknown characteristics. Into addition and contrary to the classical ALE, it can take advantage of the extra information provided by multiple sensors so as to significantly increase its performance. The idea essentially consists in a reduced-rank regression between the measured signals and their delayed versions at each frequency of interest, and its implementation is carried out within a general ''spectral estimation'' framework which encompasses classical estimators such as the WOSA, the multi-taper, and the lag-window. This allows the design of enhancement filters with improved frequency selectivity. The rationale of the method is supported by a statistical performance analysis, and its efficiency is eventually demonstrated on actual industrial signals.