Lossless data compression based on adaptive linear predictor for embedded system of unmanned vehicles

Abstract

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.