Towards cognitive IRST
Cognitive radar systems adapt processing, receiver and transmitted waveform parameters by continuously learning and interacting with the operative environment. IRST systems are passive; as such no RF emission is involved. Nevertheless, the cognitive paradigm can be applied to passive sensors in order to optimize operational modes choice, platform and processing parameters on the fly. A cognitive based IRST, while enhancing the overall performance of the system, would also reduce the crew workload during the mission. In this paper, steps and challenge toward cognitive IRST are described, along with a proof-of-concept example of improved tracking capabilities using reinforcement learning methods.
Leonardo S.p.A. (Italy)
Dr. Conte got his Laura Degree in Telecommunication Engineering and PhD in Information Engineering in 2004 (Federico II, Napoli ) and 2008 (Università degli studi di Salerno) respectively. He has been, then, assistant resercher at the Unisa Electrical Engineering and Applied Mathematics Department, where its main research topics being noise statistical characterization applied to interferometers data as a member of Ligo Scientfic Collaboration (LSC) and signal and image processing theory applied to multispectral imaging systems. In 2012 he joined Leonardo Company (former SELEX ES) as IRST system engineer and algorithms designer. He is now Project Engineer Manager of the Self Protection and Sensor Fusion Integrated Project Team within the Leonardo Electronics Division, Electo-Optical Engineering group.