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Unmanned vehicles represent a significant technical improvement for ocean and atmospheric monitoring. With the increasing number of sensors mounted on the unmanned mobile platforms, the data volume and its rapid growth introduce a new challenge relative to the limited transmission bandwidth. Data compression provides an effective approach. However, installing a lossless compression algorithm in an embedded system, which is in fact limited in computing resources, scale, and energy consumption, is a challenging task. To address this issue, a novel self-adaptive lossless compression algorithm (SALCA) that is focused on the dynamic characteristics of multidisciplinary ocean and atmospheric observation data is proposed that is the extended work of two-model transmission theory. The proposed method uses a second-order linear predictor that can be changed as the input data vary and can achieve better lossless compression performance for dynamic ocean data. More than 200 groups of conductivity–temperature–depth (CTD) profile data from underwater gliders are used as the standard input, and the results show that compared to two state-of-the-art compression methods, the proposed compression algorithm performs better in terms of compression ratio and comprehensive power consumption in an embedded system.

Lossless Data Compression Based on Adaptive Linear Predictor for Embedded System of Unmanned Vehicles