Super-resolution radar imaging via convex optimization
Reinhard Heckel

TL;DR
This paper demonstrates that super-resolution radar imaging is achievable through convex optimization, surpassing traditional resolution limits dictated by bandwidth, observation time, and antenna count, by leveraging compressive sensing techniques.
Contribution
The authors introduce a convex optimization approach that enables super-resolution in radar imaging, overcoming classical resolution constraints related to bandwidth, observation duration, and antenna configuration.
Findings
Achieves finer resolution than traditional limits using convex optimization.
Resolves angle-delay-Doppler triplets separated by smaller factors.
Utilizes compressive sensing to enhance radar imaging resolution.
Abstract
A radar system emits probing signals and records the reflections. Estimating the relative angles, delays, and Doppler shifts from the received signals allows to determine the locations and velocities of objects. However, due to practical constraints, the probing signals have finite bandwidth B, the received signals are observed over a finite time interval of length T only, and a radar typically has only one or a few transmit and receive antennas. These constraints fundamentally limit the resolution up to which objects can be distinguished. Specifically, a radar can not distinguish objects with delay and Doppler shifts much closer than 1/B and 1/T, respectively, and a radar system with N_T transmit and N_R receive antennas cannot distinguish objects with angels closer than 1/(N_T N_R). As a consequence, the delay, Doppler, and angular resolution of standard radars is proportional to 1/B…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Microwave Imaging and Scattering Analysis · Advanced SAR Imaging Techniques
