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

Quantity over Quality: Dithered Quantization for Compressive Radar Systems