Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals
Hao Shan, J. P. Yuan, Na Wang, Zhen Wang
Abstract
Abstract In pulsar signal processing, two primary difficulties are (1) radio-frequency interference (RFI) mitigation and (2) information loss due to preprocessing and mitigation itself. Linear mitigation methods have a difficulty in RFI modeling, and accommodate a limited range of RFI morphologies. Thresholding methods suffer from manual factors and adaptability. There is also a distinct lack of methods dedicated to information loss. In this paper, a novel method “CS-Pulsar” is proposed. It carries out compressed sensing (CS) on time-frequency signals to accomplish RFI mitigation and signal restoration simultaneously. Curvelets allow an optimal sparse representation for multichannel pulsar signals containing the time-of-arrival dispersion relationship. CS-Pulsar mitigation is implemented using a regularized least-squares framework that does not require the statistics of RFI to be known beforehand. CS-Pulsar implements channel restoration, and useful signal contents are retrieved from the measurement error by a morphological component analysis aided by the root-mean-square envelope. These two steps allow CS-Pulsar to provide key signal details for special astrophysical purposes. Experiments of signal restoration for pulsar data from the Nanshan 26 m radio telescope reveal the advantage of CS-Pulsar. The method successfully removes false peaks due to on-pulse RFI in multipeaked pulsar profiles. CS-Pulsar also increases the timing accuracy and signal-to-noise ratio proving its feasibilities and prospects in astrophysical measurements.