Q-Markov cover identification using pseudo random binary signals

Abstract

The original q-Markov covariance equivalent realization (q-Markov cover) method for identification required noise test signals, which can not be generated exactly. The paper replaces the unrealizable noise signal with a realizable signal (the pseudo random binary signal (PRBS)), and proves that when the period of the PRBS approaches infinity the q-Markov cover algorithm, operating with PRBS, matches the first q Markov and covariance parameters (as in the original theory with noise test signals). The existing q-Markov cover algorithm will fail to match covariance and Markov parameters exactly due to non-white test signals. The new algorithm will fail to match covariance and Markov parameters exactly due to the finite period of PRBSs. The authors demonstrate however that the results with PRBSs are far superior to results with white noise signals. Apparently, the non-whiteness of the test signal degrades the identification performance worse than the non-infinite period of the PRBS.