Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Among all direction finding methods, eigenspace-based spectral methods are of interest because they are known to yield asymptotically unbiased and efficient estimates. These algorithms require knowledge of the array geometry and its elements' characteristics. In real systems, only approximate knowledge of the required array characterization is available. There is valid concern that deviation of nominal sensor gain and phase values can result in degradation of DOA estimates.  analyze the effects of sensor gain and phase perturbation on ighted eigenspace methods (WEMs).  quantify the sensitivity of WEMs by first- and second-order statistical analyses. Simulation results are included to validate our analytical results. >
['Ridha Hamza', 'Kevin M. Buckley']
I am writing a research paper on a idea An analysis of weighted eigenspace methods in the presence of sensor errors
[{"from": "human", "value": "I am writing a research paper on a idea An analysis of weighted eigenspace methods in the presence of sensor errors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Among all direction finding methods, eigenspace-based spectral methods are of interest because they are known to yield asymptotically unbiased and efficient estimates. These algorithms require knowledge of the array geometry and its elements' characteristics. In real systems, only approximate knowledge of the required array characterization is available. There is valid concern that deviation of nominal sensor gain and phase values can result in degradation of DOA estimates.  analyze the effects of sensor gain and phase perturbation on ighted eigenspace methods (WEMs).  quantify the sensitivity of WEMs by first- and second-order statistical analyses. Simulation results are included to validate our analytical results. >"}]