| Abstrak/Abstract |
Frequency-modulated continuous wave (FMCW) radars are known to accurately estimate the
parameters of targets with low-cost and low-power transceiver systems. This work shows that some features
associated with the spectrum of estimated parameters allow one to integrate the compressive sampling (CS)
theory into the FMCW signal processing. To this end, we establish a new analytical framework through
a tensor format to facilitate a systematic and convenient FMCW signal processing model. By observing
the sparsity feature in the tensor of FMCW spectrum, we develop an extensive theoretical analysis to
justify the use of CS theory in the FMCW radars, and it enabled us to propose a novel scheme, namely
compressive FMCW signal processing, for estimating range, velocity, and angle-of-arrival (AoA) of the
targets. The addition of CS theory allows our proposed scheme to use sampling-rates below Nyquist criterion,
thus minimizing the number of sampled data and mitigating the issues related to high-rate and power-
hungry analog-to-digital converter (ADC) in extremely high-frequency radar applications. Furthermore, the
proposed compressive FMCW signal processing also significantly reduces the number of radio-frequency
(RF) front-end elements necessary for estimating AoA, leading to a further saving in cost and power
consumption of the FMCW radars. Despite having sub-Nyquist sampling-rates and reduced RF front-end
elements, the performance evaluations show that our CS-based approach maintains the estimation capability
and accuracy of conventional FMCW signal processing. |
