  In this paper, we investigate a trade-off between the number of radar
observations (or measurements) and their resolution in the context of radar
range estimation. To this end, we introduce a novel estimation scheme that can
deal with strongly quantized received signals, going as low as 1-bit per signal
sample. We leverage for this a dithered quantized compressive sensing framework
that can be applied to classic radar processing and hardware. This allows us to
remove ambiguous scenarios prohibiting correct range estimation from
(undithered) quantized base-band radar signal. Two range estimation algorithms
are studied: Projected Back Projection (PBP) and Quantized Iterative Hard
Thresholding (QIHT). The effectiveness of the reconstruction methods combined
with the dithering strategy is shown through Monte Carlo simulations.
Furthermore we show that: (i), in dithered quantization, the accuracy of target
range estimation improves when the bit-rate (i.e., the total number of measured
bits) increases, whereas the accuracy of other undithered schemes saturate in
this case; and (ii), for fixed, low bit-rate scenarios, severely quantized
dithered schemes exhibit better performances than their full resolution
counterparts. These observations are confirmed using real measurements obtained
in a controlled environment, demonstrating the feasibility of the method in
real ranging applications.
