Abstract
Time series with systematic misses occur often in practice and can be modeled as amplitude modulated ARMA processes. With this as a motivating application, modeling of cyclostationary amplitude modulated time series is addressed in this paper. Assuming that the modulating sequence is (almost) periodic, parameter estimation algorithms are developed based on second- and higher order cumulants of the resulting cyclostationary observations, which may be corrupted by any additive stationary noise of unknown covariance. If unknown, the modulating sequence can be recovered even in the presence of additive (perhaps nonstationary and colored) Gaussian, or any symmetrically distributed, noise. If the ARMA process is nonGaussian, cyclic cumulants of order greater than three can identify (non)causal and (non)minimum phase models from partial noisy data. Simulation experiments corroborate the theoretical results.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2408-2419 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Signal Processing |
| Volume | 42 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 1994 |
Bibliographical note
Funding Information:Manuscript received May 28, 1993; revised January 3, 1994. This paper was presented at the Third ISSPA-HOSSPA Conference, Gold Coast, Australia, August 18-21, 1992. This work was supported by ONR Grant no. N00014-93-1-0485. The associate editor coordinating the review of this paper and approving it for publication was Prof. John Goutsias. The authors are with the Department of Electrical Engineering, University of Virginia, Charlottesville, VA 22903-2442 USA. IEEE Log Number 940327 1.