A major challenge in UWB signal processing is the requirement for very high sampling rate under Shannon-Nyquist sampling theorem which exceeds the current ADC capacity. Radar signal is essentially a delayed and scaled version of the transmitted pulse, determined by sparse parameters such as time delays and amplitudes. A system for sampling UWB radar signal at an ultra-low sampling rate based on the Finite Rate of Innovation (FRI) and the estimation of time delays and amplitudes to detect UWB radar signal is presented in the paper. This sampling scheme which acquires the Fourier series coefficients often results in sparse parameter extraction for UWB radar signal detection. The parameters such as time-delays and amplitudes are estimated using the total variation norm minimization. With this system, the UWB radar signal can be accurately reconstructed and detected with overwhelming probability at the rate much lower than Nyquist rate. The simulation results show that the proposed approach offers very good recovery performances for noisy UWB radar signal using very small number of samples, which is effective for sampling and detecting UWB radar signal.
"A Compressive Sensing Signal Detection for UWB Radar," Progress In Electromagnetics Research,
Vol. 141, 479-495, 2013. doi:10.2528/PIER13061714
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