Title :
A new method for multiple source detection and identification from array data using cumulants and its application to shock waves propagation
Author :
Gaeta, Michael ; Nikias, Chrysostomos L.
Author_Institution :
Signal &Image Processing Inst., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
The problem of multiple component signal estimation is addressed in both frequency and time domains using higher order statistics. A multiple component signal is defined as a superposition of independent non-Gaussian linear processes. Two algorithms are proposed to estimate the transfer function characteristics of the individual component filters: the first approach is based on an eigen-decomposition of the trispectrum matrix whereas the second on an adaptive inverse filter estimation procedure. It is shown that both techniques have the capability to resolve more input signal components than the number of sensors.
Keywords :
adaptive filters; digital filters; filtering and prediction theory; identification; matrix algebra; shock waves; signal detection; statistical analysis; adaptive inverse filter estimation; algorithms; array data; component filters; eigen-decomposition; frequency domain; higher order statistics; multiple component signal estimation; multiple source detection; multiple source identification; nonGaussian linear processes; shock waves propagation; time domain; transfer function characteristics; trispectrum matrix; Filters; Frequency domain analysis; Random processes; Random variables; Sensor arrays; Shock waves; Signal processing; Spectral analysis; System identification; Tensile stress;
Conference_Titel :
Higher-Order Statistics, 1993., IEEE Signal Processing Workshop on
Conference_Location :
South Lake Tahoe, CA, USA
Print_ISBN :
0-7803-1238-4
DOI :
10.1109/HOST.1993.264560