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In the actual engineering applications, the loitering missile seeker has a larger velocity to height ratio (V/H), the sensor imaging scene changes quickly, and the flight environment is more complex, which poses a challenge to the end of the target tracking and guidance. Starting from the tracker algorithm itself and the tracking strategy of the seeker system to improve the performance of the tracking function in engineering applications. Based on the traditional Kernelized Correlation Filter (KCF), the image pyramid pooling idea is used to extract multi-dimensional data features, and the scale estimation is added to the KCF tracking algorithm.so as to improve its robustness to rotation and scale.Improve the tracking timing from the perspective of system management strategy, and optimize human-machine interaction. In order to verify the effectiveness of the method, this paper verifies the tracking performance of the seeker in both laboratory and outfield environments, comparing the tracking results with the traditional KCF on three sets of partial frames with obvious scale transformations, and evaluating the Overlap Precision(OP) and the Center Location Error (CLE), the CLE was reduced by 9.5% and the OP was increased by 34%. In the outfield flight, the tracking of moving and static targets in multi-scene and multi-morphology by the missile-mounted seeker was fully verified, which fully verified the change of target scale and the tracking reliability and accuracy under the extreme environment in the outfield.
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