Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An adaptive matched subspace CFAR detector of Gaussian distributed signals is analyzed. The detector is an extension of the one developed in F. Gini and A. Farina (1999) to the case of unknown disturbance covariance matrix. This matrix is estimated from secondary data which can be non homogenous. In this latter case  use a robust estimate of the covariance matrix based on the sample spatial sign covariance matrix (SSCM). The performance of the adaptive scheme, specifically, the impact on the false alarm rate is studied by means of Monte Carlo simulations. The results show that the adaptive detector using the SSCM maintains the false alarm approximately constant in non homogenous situations
['Arezki Younsi', 'Abdelhak M. Zoubir', 'Abdelaziz Ouldali']
I am writing a research paper on a idea Robust Adaptive Matched Subspace Cfar Detector for Gaussian Signals
[{"from": "human", "value": "I am writing a research paper on a idea Robust Adaptive Matched Subspace Cfar Detector for Gaussian Signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An adaptive matched subspace CFAR detector of Gaussian distributed signals is analyzed. The detector is an extension of the one developed in F. Gini and A. Farina (1999) to the case of unknown disturbance covariance matrix. This matrix is estimated from secondary data which can be non homogenous. In this latter case  use a robust estimate of the covariance matrix based on the sample spatial sign covariance matrix (SSCM). The performance of the adaptive scheme, specifically, the impact on the false alarm rate is studied by means of Monte Carlo simulations. The results show that the adaptive detector using the SSCM maintains the false alarm approximately constant in non homogenous situations"}]