Q-MARKOV COVER IDENTIFICATION USING PSEUDORANDOM BINARY SIGNALS

The original q-Markov COVariance Equivalent Realization (q-Markov Cove r) method for identification required white noise test signals, which cannot be generated exactly. This paper replaces the unrealizable whit e-noise signal with a realizable signal (the pseudo-random binary sign al, PRBS), and proves that when the period of the PRBS approaches infi nity the q-Markov Cover algorithm, operating with PRBS, matches the fi rst q Markov and covariance parameters (as in the original theory with white-noise test signals). The existing q-Markov Cover algorithm will fail to match covariance and Markov parameters exactly due to non-whi te test signals. The new algorithm will fail to match covariance and M arkov parameters exactly due to the finite period of PRBSs. We demonst rate however that the results with PRBSs are far superior to results w ith 'white' noise signals. Apparently, the 'non-whiteness' of the test signal degrades the identification performance worse than the 'non-in finite' period of the PRBS.