Recurrent deep learning applied to radar data
room
TNO
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Den Haag, Netherlands, Europe

The goal of this assignment is to investigate whether this temporal structure can be exploited to improve the suppression of radar clutter and interference, for instance by predicting the clutter or interference for the next radar observation. Then the next question would be: is it also possible to adapt to (slow) changes of the temporal structure? For example, the blades of a wind turbine tend to rotate faster when the wind increases, can this change be taken into account in the subsequent predictions? One potential line of investigation is the use of deep learning techniques with a recurrent structure. You will perform this assignment in the Department of Radar Technology. We are a passionate and creative group of professionals (60 people) dedicated to the specification, development and evaluation of innovative, high-performance MMICs, miniaturised and integrated RF subsystems, antennas and front-ends. The department is at the heart of novel, game-changing radar system and signal processing concepts for the military, space and civil domains.

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