Higher-Order Cumulants in System Identification and Signal Decomposition

Abstract

: The paper surveys the basic concepts of system identification using the higher-order cumulants. Theoretical background for the single 1-D C(q,k) identification formula is described, whereas the simulation results based on it are compared to much more complex and sophisticated polycepstral identification. In the second part, we develop an idea of the superimposed signal decomposition relying upon the multichannel MISO and MIMO models, and the corresponding multichannel system identification via higher-order cumulants. Concluding simulation results confirm the validity of the presented signal decomposition schemes. 1 INTRODUCTION Higher-order statistics based approaches have recently gained wide attention. There are several reasons for that: higher-order statistics have enabled new solutions and improved known ones in the field of the inverse problems, in particular concerning so called blind system identification, they are applicable to the deterministic as well as the stochastic mod..