Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  study the “stability” of Regularized modified CS(noisy) for recursive reconstruction of sparse signal sequences from noisy measurements. By “stability”  mean that the number of misses from the current support estimate; the number of extras in it; and the l 2  norm of the reconstruction error remain bounded by a time-invariant value at all times. The concept is meaningful only if the support error bounds are small compared to the signal support size. Regularized modified CS(noisy) is the noisy relaxation of regularized modified CS. The key assumption that reg-mod-CSN uses is that both the sparse signals support and its nonzero signal values change slowly over time. Denote the support estimate from the previous time by T. Modified-CS tries to find a signal that is sparsest outside of T and satisfies the data constraint. Denote the signal estimate from the previous time by μT . Reg-mod-CSN augments mod-CS by also putting the l 2  distance of the current solution from μT as a constraint.
['Fardad Raisali', 'Namrata Vaswani']
I am writing a research paper on a idea Stability (over time) of Regularized modified CS(noisy) for recursive causal sparse reconstruction
[{"from": "human", "value": "I am writing a research paper on a idea Stability (over time) of Regularized modified CS(noisy) for recursive causal sparse reconstruction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work,  study the \u201cstability\u201d of Regularized modified CS(noisy) for recursive reconstruction of sparse signal sequences from noisy measurements. By \u201cstability\u201d  mean that the number of misses from the current support estimate; the number of extras in it; and the l 2  norm of the reconstruction error remain bounded by a time-invariant value at all times. The concept is meaningful only if the support error bounds are small compared to the signal support size. Regularized modified CS(noisy) is the noisy relaxation of regularized modified CS. The key assumption that reg-mod-CSN uses is that both the sparse signals support and its nonzero signal values change slowly over time. Denote the support estimate from the previous time by T. Modified-CS tries to find a signal that is sparsest outside of T and satisfies the data constraint. Denote the signal estimate from the previous time by \u03bcT . Reg-mod-CSN augments mod-CS by also putting the l 2  distance of the current solution from \u03bcT as a constraint."}]