Hardware prototype demonstration of a cognitive sub-Nyquist automotive radar

Hardware prototype demonstration of a cognitive sub-Nyquist automotive radar

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Automotive radar (AR) plays a key role in autonomous vehicles. A modern-day AR requires cognition to adapt to its dynamically changing environment. In an automotive environment, the number of ARs needed to operate simultaneously varies rapidly and these ARs should transceive without mutual interference. Most widely used AR systems use frequency-modulated continuous-wave radar due to their smaller bandwidth and cost compared with pulse Doppler radar (PDR). By using sub-Nyquist based techniques, such as Xampling, the authors show here that the targets could be estimated from low rate samples even with a PDR. In this study, they demonstrate a hardware prototype demonstrating the applicability of sub-Nyquist PDR as a cognitive AR. They consider a scenario where a number of ARs are mounted on a vehicle and need to look simultaneously into different directions without interfering with each other. The available bandwidth is divided into several non-overlapping subbands, which depend on the number of ARs required. Each AR is assigned a set of randomly spaced subbands for its transceiver to operate. Through simulations, they show that the ARs could detect targets simultaneously without interference. Furthermore, the noise robustness of their sub-Nyquist reconstruction method is better than the standard matched-filtering approach.

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