Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The classical higher order MUSIC-like methods based on a simultaneous search for all directions of arrival (DOA's) show: i) a capacity for processing underdetermined mixtures of sources; ii) a high robustness with respect to both a Gaussian noise with unknown spatial coherence and modeling errors; and iii) a better resolution than algorithms based on second order statistics. Hover, these methods have some limits: for a finite number of samples, they show poor performance for sources exhibiting quasi-colinear DOA's. In order to overcome this drawback, two new sequential MUSIC-like algorithms are proposed in this paper, namely the  2q -D-MUSIC and the  2q  -RAP-MUSIC  (q  ≥ 2) algorithms. These methods are based on a sequential optimization of proposed generalized noise and signal  2q -MUSIC metrics, respectively. That allows us to learn and then to take into account the level of correlation beten sources. A comparative study, both in terms of performance and numerical complexity, is performed showing the interest of the proposed techniques when some sources are angularly close. Eventually, an upper bound of the maximum number of sources which can be processed by the  2q -MUSIC-like techniques is given for all  q . This improves recent work on the  2q th-order virtual arrays.
['Gwénaël Birot', 'Laurent Albera', 'Pascal Chevalier']
I am writing a research paper on a idea Sequential High-Resolution Direction Finding From Higher Order Statistics
[{"from": "human", "value": "I am writing a research paper on a idea Sequential High-Resolution Direction Finding From Higher Order Statistics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The classical higher order MUSIC-like methods based on a simultaneous search for all directions of arrival (DOA's) show: i) a capacity for processing underdetermined mixtures of sources; ii) a high robustness with respect to both a Gaussian noise with unknown spatial coherence and modeling errors; and iii) a better resolution than algorithms based on second order statistics. Hover, these methods have some limits: for a finite number of samples, they show poor performance for sources exhibiting quasi-colinear DOA's. In order to overcome this drawback, two new sequential MUSIC-like algorithms are proposed in this paper, namely the  2q -D-MUSIC and the  2q  -RAP-MUSIC  (q  \u2265 2) algorithms. These methods are based on a sequential optimization of proposed generalized noise and signal  2q -MUSIC metrics, respectively. That allows us to learn and then to take into account the level of correlation beten sources. A comparative study, both in terms of performance and numerical complexity, is performed showing the interest of the proposed techniques when some sources are angularly close. Eventually, an upper bound of the maximum number of sources which can be processed by the  2q -MUSIC-like techniques is given for all  q . This improves recent work on the  2q th-order virtual arrays."}]