Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  treat the problem of beamforming for signal estimation in the presence of steering vector uncertainties, where the goal is to estimate a signal amplitude from a set of array observations. Conventional beamforming methods typically aim at maximizing the signal-to-interference-plus-noise ratio (SINR). Recently, a maximum likelihood (ML) approach was introduced that leads to an iterative beamformer. Here  suggest an expected least-squares (LS) strategy that results in a simple linear beamformer.  then demonstrate through simulations that the LS beamformer often performs similarly to the ML method in terms of mean-squared error and outperforms conventional SINR-based approaches.
['Yonina C. Eldar', 'A. Nehorai', 'P. La Rosa']
I am writing a research paper on a idea An expected least-squares beamforming approach to signal estimation with steering vector uncertainties
[{"from": "human", "value": "I am writing a research paper on a idea An expected least-squares beamforming approach to signal estimation with steering vector uncertainties"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  treat the problem of beamforming for signal estimation in the presence of steering vector uncertainties, where the goal is to estimate a signal amplitude from a set of array observations. Conventional beamforming methods typically aim at maximizing the signal-to-interference-plus-noise ratio (SINR). Recently, a maximum likelihood (ML) approach was introduced that leads to an iterative beamformer. Here  suggest an expected least-squares (LS) strategy that results in a simple linear beamformer.  then demonstrate through simulations that the LS beamformer often performs similarly to the ML method in terms of mean-squared error and outperforms conventional SINR-based approaches."}]