Stefan Brüggenwirth

David Mateos-Núñez, María A. González-Huici, Renato Simoni et al.

We present adaptive strategies for antenna selection for Direction of Arrival (DoA) estimation of a far-field source using TDM MIMO radar with linear arrays. Our treatment is formulated within a general adaptive sensing framework that uses one-step ahead predictions of the Bayesian MSE using a parametric family of Weiss-Weinstein bounds that depend on previous measurements. We compare in simulations our strategy with adaptive policies that optimize the Bobrovsky- Zakaı bound and the Expected Cramér-Rao bound, and show the performance for different levels of measurement noise.

Sebastian Durst, Stefan Brüggenwirth

An intelligent radar resource management is an essential milestone in the development of a cognitive radar system. The quality of service based resource allocation model (Q-RAM) is a framework allowing for intelligent decision making but classical solutions seem insufficient for real-time application in a modern radar system. In this paper, we present a solution for the Q-RAM radar resource management problem using deep reinforcement learning considerably improving on runtime performance.

Stefan Brüggenwirth, Fernando Rial

In the article, we describe a trajectory planning problem for a 6-DOF robotic manipulator arm that carries an ultra-wideband (UWB) radar sensor with synthetic aperture (SAR). The resolution depends on the trajectory and velocity profile of the sensor head. The constraints can be modelled as an optimization problem to obtain a feasible, collision-free target trajectory of the end-effector of the manipulator arm in Cartesian coordinates that minimizes observation time. For 3D-reconstruction, the target is observed in multiple height slices. For Through-the-Wall radar the sensor can be operated in sliding mode for scanning larger areas. For IED inspection the spot-light mode is preferred, constantly pointing the antennas towards the target to obtain maximum azimuth resolution.