责任导师 : Amal EL FALLAH SEGHROUCHNI

助理责任导师 : Vincent CORRUBLE

Reinforcement Learning for Multi-Function Radar Resource Management

In the wake of recent advances in the field of machine learning, much progress has been accomplished in one of its sub-fields, reinforcement learning, whose aim is to solve sequential decision problems under uncertainty. Radar resource management seems to represent an ideal application case for this type of technique. Indeed, a radar emits signals, called dwells, whose echoes are used to measure the state of surrounding objects; these dwells vary according to numerous parameters (duration, beam width...) and must be executed sequentially. The surveillance strategy of a multi-function radar thus consists in continuously selecting the dwells to perform, with the aim of searching the surrounding space while tracking already detected targets. The methods currently used to address this problem are largely heuristic, and are likely to run into difficulties in a range of complex situations involving hyper-velocity or hyper-maneuvering targets.
First, we propose applications of reinforcement learning techniques adapted to the current architecture of multi-function radars. These contributions focus on two aspects: dwell scheduling on the antenna using model-based methods, and active tracking dwell optimization using model-free methods. Secondly, we highlight the limitations of current resource management architectures, which leads us to consider an alternative architecture for which we propose new reinforcement learning algorithms designed to address the problems it raises. These contributions focus both on the multi-objective aspect, which is useful in multi-function radars to reflect the trade-offs to be made between different functions, and on the combinatorial aspect, which is due to the large number of tasks that the radar must carry out in parallel.

2021-2023 刊物