Abstract
This paper investigates the compression detection problem using sub-Nyquist radars, which is well suited to the scenario of high bandwidths in real-time processing because it would significantly reduce the computational burden and save power consumption and computation time. A compressive generalized likelihood ratio test (GLRT) detector for sparse signals is proposed for sub-Nyquist radars without ever reconstructing the signal involved. The performance of the compressive GLRT detector is analyzed and the theoretical bounds are presented. The compressive GLRT detection performance of sub-Nyquist radars is also compared to the traditional GLRT detection performance of conventional radars, which employ traditional analog-to-digital conversion (ADC) at Nyquist sampling rates. Simulation results demonstrate that the former can perform almost as well as the latter with a very small fraction of the number of measurements required by traditional detection in relatively high signal-to-noise ratio (SNR) cases.
Highlights
Application of compressed sensing/sampling (CS) [1,2,3,4] in radar signal processing has recently caught the attention of many researchers
It is observed that the receiver operating characteristics (ROC) curves obtained by Monte Carlo simulation are tightly concentrated around the expected performance curve described in (26) with high probability in all these cases
As new approaches for radar sensing based on the finite rate of innovation (FRI) and Xampling frameworks, sub-Nyquist radar system allows sampling of radar signals at rates much lower than Nyquist, which has been demonstrated by real-time analog experiments in hardware
Summary
Application of compressed sensing/sampling (CS) [1,2,3,4] in radar signal processing has recently caught the attention of many researchers. Current reconstruction methods include using greedy algorithms such as orthogonal matching pursuit (OMP) [16] and solving the convex problem such as the basis pursuit (BP) [1, 2]. These algorithms involve iterative optimization procedures and are computationally expensive for long signals. For this reason, the recent research report [17] studied a novel signal processing approach directly based on the compressive measurements. Paper [18] proposed jointly compressive signal target detection and parameters estimator in radar without signal reconstruction. The work in [20] is an extension of the former, which applies
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