Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals subject to white measurement noise. It is shown that the corrupting noise variance, which determines the bias in the standard least-squares (LS) parameter estimator, can be estimated by simply using the expected LS errors when the ratio beten the driving noise variance and the corrupting noise variance is known or obtainable in some way. Then an LS based algorithm is established via the principle of bias compensation. Compared with the other LS based algorithms recently developed, the introduced algorithm produces better parameter estimates, requires fer computations and has a simpler algorithmic structure.
['Wei Xing Zheng']
I am writing a research paper on a idea Unbiased parameter identification for noisy autoregressive signals
[{"from": "human", "value": "I am writing a research paper on a idea Unbiased parameter identification for noisy autoregressive signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals subject to white measurement noise. It is shown that the corrupting noise variance, which determines the bias in the standard least-squares (LS) parameter estimator, can be estimated by simply using the expected LS errors when the ratio beten the driving noise variance and the corrupting noise variance is known or obtainable in some way. Then an LS based algorithm is established via the principle of bias compensation. Compared with the other LS based algorithms recently developed, the introduced algorithm produces better parameter estimates, requires fer computations and has a simpler algorithmic structure."}]