Key result
The CCSDS 124.0-B-1 standard achieved competitive average compression ratios between 4 and 16 depending on the dataset, while offering flexible resiliency mechanisms for data loss events.
The CCSDS 124.0-B-1 standard provides competitive lossless compression for space telemetry with flexible resiliency mechanisms.
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Hypothesis-generating for telemetry efficiency; leaves open validation before cardiovascular monitoring use.
The Consultative Committee for Space Data Systems (CCSDS) recently published a lossless compression standard for housekeeping and telemetry. These data are critical for the safe and productive operation of virtually all remote sensing missions, including Earth observation. The new standard, CCSDS 124.0-B-1 “Robust Compression of Fixed-Length Housekeeping Data,” allows lossless, real-time telemetry packet transmission with low energy and memory requirements. Furthermore, it offers flexible resiliency mechanisms to resume decompression after losing one or more data packets. This work provides an accessible introduction to CCSDS 124.0-B-1, paying special attention to its resiliency features. Two novel and complementary decoder resynchronization strategies are also presented, which maximize the amount of correctly decoded data. Comprehensive experiments with real and simulated mission data were conducted for this work to assess the standard’s performance and the impact of user-defined parameters. Results indicate competitive coding performance, with average compression ratios between 4 and 16 depending on the dataset. Smooth trade-offs are also observed between compression performance and the degree of protection against data loss events, which enhances the usability of CCSDS 124.0-B-1 in multiple mission types and operational conditions.
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Hernández-Cabronero et al. (2024) studied this question. CCSDS 124.0-B-1 Telemetry Compression Standard vs. Other compression algorithms (e.g., bzip2, LZMA, Zstandard) was evaluated on Compression ratio and resiliency to data loss. The CCSDS 124.0-B-1 standard achieved competitive average compression ratios between 4 and 16 depending on the dataset, while offering flexible resiliency mechanisms for data loss events.
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