A computationally efficient approach to stochastic ARMA (autoregressive moving-average) modeling of wide-sense stationary signals is proposed. The discrete estimator minimizes a modified version of the likelihood function by using exclusively linear techniques and circumventing the high computational complexity of the maximum-likelihood (ML) method. The proposed approach is thus easy to implement, requires no second-order statistical information, and is shown to produce high-quality estimates at a very modest computational cost. A recursive version of the algorithm, suitable for online implementation, is also developed, and, certain modeling strategy issues discussed. The effectiveness of the proposed approach is established by numerical simulations and comparisons with other suboptimum schemes.
|Original language||English (US)|
|Number of pages||8|
|Journal||Proceedings of the American Control Conference|
|Volume||88 pt 1-3|
|State||Published - 1988|
ASJC Scopus subject areas
- Electrical and Electronic Engineering