In this paper, we focus on developing an algorithm for infrared-imaging guidance that enables the aircraft to be reliably tracked in the event of interference. The key challenge is to track the aircraft with occlusion caused by decoys and drastic appearance changes resulting from a diversity of attacking angles. To address this challenge, an aircraft-tracking algorithm was proposed, which provides robustness in tracking the aircraft against the decoys. We reveal the inherent structure and infrared signature of the aircraft, which are used as discriminative features to track the aircraft. The anti-interference method was developed based on simulated images but validate the effectiveness on both real infrared image sequences without decoys and simulated infrared imagery. For frequent occlusion caused by the decoys, the mechanism of occlusion detection is exploited according to the variation of the model distance in tracking process. To have a comprehensive evaluation of tracking performance, infrared-image sequences with different attack angles were simulated, and experiments on benchmark trackers were performed to quantitatively evaluate tracking performance. The experiment results demonstrate that our aircraft-tracking method performs favorably against state-of-the-art trackers.

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Airborne target tracking in infrared imagery remains a challenging task. The airborne target usually has a low signal-to-noise ratio and shows different visual patterns. The features adopted in the visual tracking algorithm are usually deep features pre-trained on ImageNet, which are not tightly coupled with the current video domain and therefore might not be optimal for infrared target tracking. To this end, we propose a new approach to learn the domain-specific features, which can be adapted to the current video online without pre-training on a large datasets. Considering that only a few samples of the initial frame can be used for online training, general feature representations are encoded to the network for a better initialization. The feature learning module is flexible and can be integrated into tracking frameworks based on correlation filters to improve the baseline method. Experiments on airborne infrared imagery are conducted to demonstrate the effectiveness of our tracking algorithm.

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Airborne target tracking is a crucial part of infrared imaging guidance. In contrast to visual tracking tasks, the target in infrared imagery shows different visual patterns. Moreover, severe background clutter and frequent occlusion caused by infrared interference make it a challenging task. Recently, discriminative correlation filter (DCF)‐based trackers have shown impressive performance. However, the features adopted in DCF‐based trackers are either handcrafted or pre‐trained from a different task, which do not closely intertwine with the domain‐specific video. To settle this problem, it is proposed to make full use of online training to learn domain‐specific features. By integrating the correlation filter layer into the convolutional neural networks, the feature domain and the response maps of the DCF can be optimized iteratively in the initial frame. Meanwhile, utilizing the measurement of the response maps' peak strength, further adjustments to the feature domain can be made to achieve a sharper peak and suppress the interference region during the tracking process. Evaluations are conducted to prove the validity of proposed aircraft‐tracking algorithm.