Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers the problem of estimating the parameters of two-dimensional moving average random fields.  first address the problem of expressing the covariance matrix of a moving average random field, in terms of the model parameters. Assuming the random field is Gaussian,  derive a closed form expression for the Cramer-Rao lor bound on the error variance in jointly estimating the model parameters. A computationally efficient algorithm for estimating the parameters of the moving average model is developed. The algorithm initially fits a two-dimensional autoregressive model to the observed field, then uses the estimated parameters to compute the moving average model.
['Joseph M. Francos', 'Benjamin Friedlander']
I am writing a research paper on a idea Parameter estimation of two-dimensional moving average random fields: algorithms and bounds
[{"from": "human", "value": "I am writing a research paper on a idea Parameter estimation of two-dimensional moving average random fields: algorithms and bounds"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  considers the problem of estimating the parameters of two-dimensional moving average random fields.  first address the problem of expressing the covariance matrix of a moving average random field, in terms of the model parameters. Assuming the random field is Gaussian,  derive a closed form expression for the Cramer-Rao lor bound on the error variance in jointly estimating the model parameters. A computationally efficient algorithm for estimating the parameters of the moving average model is developed. The algorithm initially fits a two-dimensional autoregressive model to the observed field, then uses the estimated parameters to compute the moving average model."}]