Learning Based Energy Efficient Radar Power Control Against Deceptive Jamming

Multiple-input and multiple-output (MIMO) radars are vulnerable to deceptive jamming launched by false target generators that send jamming signals with the goal of pretending that the radar echo signals are reflected by faked targets. In this paper, we present a reinforcement learning based energy efficient power control scheme to detect deceptive jamming for frequency diverse array MIMO radars. This scheme enables a radar to choose the transmit power over the antennas without relying on the known deceptive jamming model. Instead, based on the emergency level, the battery level, the echo signal quality, the antenna phase differences, the received jamming power and the previous detection error rate, this scheme improves the detection accuracy and the energy efficiency, and uses a Dyna architecture to train the learning parameters with the simulated jamming detection experiences for faster optimization in the dynamic game against deceptive jamming. Simulation results show that this scheme effectively improves the deceptive jamming detection accuracy and saves the radar energy.