To correct installation errors of star sensors, we propose an error calibration method based on the measurement information from fine guidance sensors (FGS). This work established an extended Kalman filter model by using the measurement information of FGS as the observation to estimate position errors. Simulation experiments are presented to assess the effectiveness of the proposed star sensor calibration technique, and the results demonstrate the improved error estimation accuracy.

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Limited by the characteristics of underwater acoustic channels, the video transmission applications targeting deep-sea detection and operation tasks are facing severe challenges such as network failure and high delay, resulting in loss of video details, color distortion, blurring, and even bit errors, which seriously affect decoding quality of the video transmission and reception. In order to solve the problems of deep-sea long-distance wireless communication, this paper proposes an improved Wyner-Ziv coding scheme (UnderWater-WZ) for video transmission through acoustic channels. The implementation process includes controlling error range by using MJPEG coding, combining motion compensation time interpolation with calibration information to generate high-quality side information. And intraframe quantization matrix is designed to weaken the change of video scene. The experimental results show that under the highest packet loss rate of 20%, this scheme can achieve 2.6~3.5 dB improvements in terms of video reconstruction compared to the previous methods, which is close to the error-free level.

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The purpose of this paper is to improve the efficiency of video recognition process and save computational data. According to the encoding and decoding of video, we can know that not every part of the information of each frame is valid in video classification. There are a lot of invalid information, which takes up computing space. Therefore, this paper proposes an efficient video classification and prediction method based on reinforcement learning intra prediction. The process of eliminating time redundancy is further added, and then the video frames with low value are ignored, which further improves the computational efficiency. This method extracts the key frames from the marine biological video, and then focuses on the key image areas of the key frames, so as to reduce the network computing overhead.