Deep Learning Techniques for Missile Seeker Automatic Target Recognition

Modern infrared missiles use sophisticated computer vision techniques, in conjunction with imaging seekers, to automatically detect and localise targets. However, simulations show that soft-kill countermeasures remain an effective defence against such systems. This research explores the feasibility of deep learning algorithms for Automatic Target Recognition (ATR) and aim to significantly improve seeker performance in the presence of soft-kill countermeasures and clutter. State-of-the-art neural network architectures were benchmarked using both simulated and real-world infrared data. Their performance was also analysed to inform tailored and novel developments. In addition to this improvement to existing capabilities, these algorithms established additional capabilities of target recognition and identification. This effectively enables target prioritisation and safeguarding of friendly assets.