Abstract:
Compressive Sensing is practical and implemented into many areas. For these applications the conventional sensing noise (e.g. AWGN) with low energy on measurements could ...Show MoreMetadata
Abstract:
Compressive Sensing is practical and implemented into many areas. For these applications the conventional sensing noise (e.g. AWGN) with low energy on measurements could be reduced by the robustness of Compressive Sensing. Parallel, there exist some errors, which would strongly noise or remove some parts of measurements. In this case, the recovered information would contain much noise, since the robustness is broken. In order to reduce the influence of those errors, we propose an optimization mechanism with help of the sensed redundancy in measurements, instead of employing denoising methods or increasing extra measurements. This method can target the error locations, then contribute a better recovery quality in an acceptable range.
Date of Conference: 05-07 July 2017
Date Added to IEEE Xplore: 23 October 2017
ISBN Information: