Missing samples analysis in signals for applications to L-estimation and compressive sensing
This paper provides statistical analysis for efficient detection of signal components when missing data samples are present. This analysis is important for both the areas of L-statistics and compressive sensing. In both cases, few samples are available due to either noisy sample elimination or random undersampling signal strategies. The analysis enables the determination of the sufficient number of observation and as such the minimum number of missing samples which still allow proper signal detection. Both single component and multicomponent signals are considered. The results are verified by computer simulations using different component frequencies and under various missing-available samples scenarios.
missing samples l-estimation compressive sensing detection fourier transform noisy signal spectral analysis