Hatanaka compression is a specialized data compression algorithm designed specifically for GNSS observation files in RINEX format, achieving significant file size reductions while preserving all original data with perfect fidelity. Named after its developer, Dr. Yuki Hatanaka of the Geographical Survey Institute of Japan, this compression method has become the de facto standard for archiving and distributing GNSS observation data within the geodetic and surveying communities.
The Hatanaka compression algorithm exploits the mathematical structure inherent in GNSS observations to achieve compression ratios typically between 3:1 and 5:1 compared to standard RINEX text files. The technique applies differential encoding to observation sequences, since consecutive measurements from the same satellite change slowly and predictably, storing differences rather than absolute values dramatically reduces data volume. The algorithm preserves full numerical precision, making it a lossless compression method suitable for scientific applications where data integrity is paramount.
Hatanaka-compressed files are conventionally identified by a ‘d’ in the file extension (e.g., ‘.21d’ for a compressed RINEX file from 2021, versus ‘.21o’ for uncompressed). These compact files are frequently further compressed using general-purpose algorithms like gzip or ZIP, achieving combined compression ratios of 10:1 or better. This double compression significantly reduces storage requirements and download times when transferring observation data from CORS networks, IGS stations, or other data archives.
Working with Hatanaka-compressed data requires appropriate decompression software, typically the CRX2RNX utility or equivalent functionality built into GNSS processing packages. Most professional GNSS software can read Hatanaka-compressed files directly or includes integrated decompression. For organizations managing large GNSS data archives or routinely downloading observation data from reference station networks, understanding Hatanaka compression is essential for efficient data handling and storage management.
