Visual Navigation Method for Carrier-based Aircraft Landing

Abstract

Carrier landing of fixed-wing aircraft is the most difficult and challenging task in the aerial field. Precise and reliable landing systems are the key to carrier landing. Visual navigation has the advantages of high precision of the relative navigation. Hence, it has become a promising method for landing systems. However, tracking the targets continuously and extracting the coordinates of the targets accurately are challenges for visual navigation due to the complex environment and dramatic distance changes during carrier landing. Therefore, a visual navigation method utilizing an airborne medium-wave infrared camera to track four infrared targets installed on the carrier, which can operate in low visibility weather conditions, is presented in this article. A segmentation detection method was adopted for tracking the targets. The kernel correlation filter was used to detect the targets when the aircraft was far from the carrier stern, which could quickly track targets with relatively constant dimensions. A convolutional neural network classifier based on transfer learning was used to detect the targets when nearing the carrier, thereby reducing the risk of losing targets due to severe dimensional changes. Finally, the relative position and attitude information could be obtained by the POSIT (pose from orthography and scaling with iterations) pose estimation algorithm. On-vehicle experiments were performed, and the results verified the effectiveness of the method.