PROJECT TITLE :
Reduced Interference Sparse Time-Frequency Distributions for Compressed Observations
Traditional quadratic time-frequency distributions are not designed to deal with randomly undersampled signals or knowledge with missing samples. The compressed knowledge measurements introduce noise-like artifacts in the ambiguity domain, compounding the matter of separating the signal auto-terms and cross-terms. During this paper, we tend to propose a multi-task kernel design for suppressing each the artifacts and therefore the cross-terms, while preserving the signal desirable auto-terms. The proposed approach results in highly focused time-frequency signature. We evaluate our approach using various polynomial part signals and show its advantages, particularly within the case of sturdy artifacts.
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