Neural network approach to spectral estimation of harmonic processes

Author

Martinelli, G. ; Perfetti, R.

Author_Institution

Dept. of Inf. Commun., Rome Univ., Italy

Volume

140

Issue

2

fYear

1993

fDate

4/1/1993 12:00:00 AM

Firstpage

95

Lastpage

100

Abstract

A neural network approach is presented for the spectral estimation of random processes composed of closely spaced sinusoids in white noise. A linear programming formulation is adopted, determining the minimum L₁-norm solution of a set of linear constraints. Then, the optimisation problem is solved by a dedicated electrical neural network whose input is the estimated autocorrelation of the process, and whose output is the power spectrum. The time response is very fast since the network is analogue and has parallel architecture. Moreover the lack of a learning phase makes it suited both to real-time signal processing and to VLSI implementation. Results of SPICE simulations are presented

Keywords

VLSI; neural nets; parallel architectures; signal detection; spectral analysis; white noise; VLSI; autocorrelation; closely spaced sinusoids; electrical neural network; harmonic processes; linear constraints; linear programming; minimum L₁-norm solution; optimisation problem; parallel architecture; power spectrum; random processes; real-time signal processing; spectral estimation; white noise;

fLanguage

English

Journal_Title

Circuits, Devices and Systems, IEE Proceedings G

Publisher

iet

ISSN

0956-3768

Type

jour

Filename

212636