There and back again: learning to simulate radar data for real-world applications
Simulating realistic radar data has the potential to significantly accelerate the development of data-driven approaches to radar processing. However, it is fraught with difficulty due to the notoriously complex image formation process. Here we propose to learn a radar sensor model capable of synthes...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2021
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| Summary: | Simulating realistic radar data has the potential to significantly accelerate the development of data-driven
approaches to radar processing. However, it is fraught with
difficulty due to the notoriously complex image formation
process. Here we propose to learn a radar sensor model
capable of synthesising faithful radar observations based on
simulated elevation maps. In particular, we adopt an adversarial
approach to learning a forward sensor model from unaligned
radar examples. In addition, modelling the backward model
encourages the output to remain aligned to the world state
through a cyclical consistency criterion. The backward model
is further constrained to predict elevation maps from real radar
data that are grounded by partial measurements obtained from
corresponding lidar scans. Both models are trained in a joint
optimisation. We demonstrate the efficacy of our approach by
evaluating a down-stream segmentation model trained purely
on simulated data in a real-world deployment. This achieves
performance within four percentage points of the same model
trained entirely on real data. |
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