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 time-symmetric, noncausal impulse response of a linear time-invariant system from measurements of the response of the system to an unknown input signal, which is assumed to be a realization of a white random process. The symmetric impulse response is modeled by a two-sided AR or ARMA system model. The two-sided AR coefficients are estimated using a two-step procedure. First, an estimate of an unconstrained parameter vector is computed by solving a close-to-Toeplitz-plus-Hankel system of equations using previously developed fast algorithms. Then, the polynomial square root of the result is obtained by solving a constrained least-squares problem which has a simple solution. Unlike previous methods, this approach requires no iterative procedure. Hover, it may lead to an unstable model in some extreme cases. Simulation results illustrate the performance of the proposed methods. >
['Jin-Jen Hsue', 'Andrew E. Yagle']
I am writing a research paper on a idea Blind deconvolution of symmetric noncausal impulse responses using two-sided linear prediction
[{"from": "human", "value": "I am writing a research paper on a idea Blind deconvolution of symmetric noncausal impulse responses using two-sided linear prediction"}, {"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 time-symmetric, noncausal impulse response of a linear time-invariant system from measurements of the response of the system to an unknown input signal, which is assumed to be a realization of a white random process. The symmetric impulse response is modeled by a two-sided AR or ARMA system model. The two-sided AR coefficients are estimated using a two-step procedure. First, an estimate of an unconstrained parameter vector is computed by solving a close-to-Toeplitz-plus-Hankel system of equations using previously developed fast algorithms. Then, the polynomial square root of the result is obtained by solving a constrained least-squares problem which has a simple solution. Unlike previous methods, this approach requires no iterative procedure. Hover, it may lead to an unstable model in some extreme cases. Simulation results illustrate the performance of the proposed methods. >"}]