Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The authors consider the performance of the class of signal subspace fitting algorithms for signal parameter estimation using narrowband sensor array data. The principle sources of estimation error in such applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics. The covariance matrix of the estimation error when all of these error sources are present is found to be the sum of the individual contributions from each component separately. This simplifying fact allows for the derivation of an overall optimal subspace ighting for a particular array and noise covariance error model. In fact, the resulting algorithm yields the lost possible asymptotic estimation error variance of any method for the model in question. >
['Mats Viberg', 'A.L. Swindlehurst']
I am writing a research paper on a idea Analysis of the combined effects of finite samples and model errors on array processing performance
[{"from": "human", "value": "I am writing a research paper on a idea Analysis of the combined effects of finite samples and model errors on array processing performance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The authors consider the performance of the class of signal subspace fitting algorithms for signal parameter estimation using narrowband sensor array data. The principle sources of estimation error in such applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics. The covariance matrix of the estimation error when all of these error sources are present is found to be the sum of the individual contributions from each component separately. This simplifying fact allows for the derivation of an overall optimal subspace ighting for a particular array and noise covariance error model. In fact, the resulting algorithm yields the lost possible asymptotic estimation error variance of any method for the model in question. >"}]