Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  consider problem about two-dimension (2-D) direction estimation of cyclostationary sources when the received signal is corrupted by both multiplicative noise and additive noise. A 2-D direction finding algorithm based on third-order cyclic moment is proposed to detect nonGaussian cyclostationary sources in the presence of complex noises. Under the cases of existing interference and different distributed noises, the simulation results indicate that the proposed method can remove cyclostationary interference with different cyclic frequency from the desired signals. Furthermore, it can effectively suppress additive and nonzero multiplicative stationary noises with any distribution, no matter they are Gaussian or nonGaussian, white or colored.
['Hong Jiang', 'Shuxun Wang']
I am writing a research paper on a idea 2-D direction finding of cyclostationary signals in the presence of both multiplicative noise and additive noise
[{"from": "human", "value": "I am writing a research paper on a idea 2-D direction finding of cyclostationary signals in the presence of both multiplicative noise and additive noise"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  consider problem about two-dimension (2-D) direction estimation of cyclostationary sources when the received signal is corrupted by both multiplicative noise and additive noise. A 2-D direction finding algorithm based on third-order cyclic moment is proposed to detect nonGaussian cyclostationary sources in the presence of complex noises. Under the cases of existing interference and different distributed noises, the simulation results indicate that the proposed method can remove cyclostationary interference with different cyclic frequency from the desired signals. Furthermore, it can effectively suppress additive and nonzero multiplicative stationary noises with any distribution, no matter they are Gaussian or nonGaussian, white or colored."}]