Title :
Approximated stochastic realization and model reduction methods applied to array processing by means of state space models
Author :
Cadre, Jean-Pierre Le ; Ravazzola, Patrice
Author_Institution :
IRISA, Rennes, France
Abstract :
The aim of this study is to present novel methods for passive array processing. The basic idea consists in using state-space modeling of the sensors´ output. The authors deal with basic problems such as unknown noise correlations, approximation by a Toeplitz matrix of lower rank, and detection of small sources. The methods presented represent considerable improvements with respect to the usual methods and furthermore are quite feasible. Some statistical results illustrate these claims
Keywords :
signal detection; signal processing; state-space methods; stochastic processes; Toeplitz matrix; model reduction; passive array processing; source detection; state space models; statistical results; stochastic realization; unknown noise correlations; Additive white noise; Array signal processing; Covariance matrix; Observability; Power system modeling; Reduced order systems; Sensor arrays; State-space methods; Stochastic processes; Stochastic resonance;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on
Conference_Location :
Glasgow
DOI :
10.1109/ICASSP.1989.267000
