Complementary sequences for multivariable system identification

Abstract

Efficient generation and correlation algorithms, combined with ideal aperiodic correlation properties, make complementary sequences a useful alternative to binary maximal length sequences (b.m.l.s) in multivariable system identification. Techniques for reducing the effects of errors on the response estimates are described and tested by simulation.