Wideband radio frequency signal recording systems are useful to determine the causes of anomalies during satellite operations. However, wideband signal recording requires expensive hardware such as high-speed analog-to-digital converters. This requirement is mainly due to the use of the traditional Shannon-Nyquist sampling theory, which states that the sampling rate must be at least two times higher than the signal bandwidth. Alternatively, a recent theory known as compressed sensing provides a means by which significantly to reduce the number of samples needed for signal reconstruction. Owing to this benefit, CS has been extensively studied in various fields of signal processing, including imaging applications and channel estimations. This paper builds upon previous work on the modulated wideband converter, which efficiently records and reconstructs wideband signals that include multiple channels at a sub-Nyquist rate. The modulated wideband converter uses compressed sensing to find the frequency support of the band of interest. The present work identifies the exact support set within the neighborhood to improve the accuracy of support detection. Experimental results involving simulated multi-band RF signals show that the proposed method significantly reduces the required rate for wideband recording and successfully restores the band of interest through improved support detection.
KSP Keywords
Analog to digital converter(ADC), Channel estimation(CE), Compressed sensing, Frequency signal, Frequency support, High Speed, Multiple channels, Nyquist sampling, RF signal, Radio Frequency(RF), Sampling rate
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