Millimeter-wave is widely used for consumer radar applications like driver assistance systems in automated vehicles and gesture recognition in touch-free interfaces.To cope with the increased hardware complexity, higher costs and power consumption of wideband systems at millimeter-wave frequencies, we propose a fully digital architecture with low-resolution analog-to-digital converters (ADCs) miracle academy clothing on each radio-frequency chain.The effect of the low-resolution ADCs on radar parameter estimation is characterized by the Cramér-Rao bound (CRB) under the proposed hardware constraints.Prior work has shown that at low signal-to-noise ratio, a radar system with 1-bit ADCs suffers a performance loss of 2 dB in parameter estimation compared to a system with ideal infinite resolution ADCs.
In this paper, we design an analog preprocessing unit that beamforms in a particular direction and improves the system performance in terms of the achievable CRB.We optimize the proposed preprocessing architecture and show that the optimized network click here is realizable through low-cost low-resolution phase-shifters.With the optimized preprocessor network in the system, we reduce the gap to 1.16 dB compared to a system with ideal ADCs.
We demonstrate the potential of the proposed architecture to meet the requirements of high-resolution sensing through analytical derivation and numerical computation of an improved CRB and show its achievability through a correlation-based estimator.