Sidyakin

pp.254-269

The paper presents main outcome of lossless compression of telemetry information study. Proposed algorithm for lossless telemetry compression is divided in two main steps: decorrelation and entropy coding. We use Normalized Least Mean square (NLMS) adaptive filter as decorrelator and experimented with two different adaptive decorrelation strategies. A comparison of the effectiveness of these different adaptive strategies are performed based on variances and entropy of the resulting prediction errors after decorrelation. In the experiments, data from automatic control system with different parameters are examined, including temperature, pressure and positioning data. These data form a stream of telemetry information conform to IRIG-106 standard. The standard is widely used in the aerospace industry. Different frame structures of that standard (with or without sub-frames) are studied. During the experiments, several algorithms for entropy coding, including Arithmetic, Huffman and Rice codes are also studied as core algorithms at the second step of lossless compression. Comparison of algorithms’ effectiveness and recommendations for development of lossless telemetry compression systems are provided based on acquired results of experiments.

This paper concerns simulation of telemetry information streams transmitted in noise channel. Description of simulator software implementation is provided. The simulator software is designed primarily for R&D aimed to improve properties of telemetry data processing algorithms for frame synchronization data compression end error correction of telemetry data. Introduced telemetry stream simulation technology can be used also in various telemetry processing applications, including testing of telemetry registration systems that support IRIG-06 telemetry frame format, testing and troubleshooting of the software and hardware components of telemetry systems. General interface for creation of telemetry frame structure is introduced. Description of simulated telemetry signals is provided, including test patterns, harmonic signals, arbitrary digital sequences, recorded data, and filtered signals. White Gaussian noise channel model is used to simulate bit errors. This model is applied to individual multiplexer channels of data acquisition system and to telemetry communication channel that is used to transfer stream of telemetry frames.

Telemetric information recording systems are usually designed for display, preprocessing and record of incoming data stream to any kind of storage media in real time  In case a computer is part of a system its HDD or flash drive could serve as a storage device. In that case, the data stream is stored as an ordinary file. Telemetry data recording process can take for a long time, while the amount of recorded data could grow up to tens of gigabytes or even more. It looks reasonable to compress these data while saving them to a storage media and keep them there in archived files. Later when post-processing is applied to the recorded data, it is often interesting to work with particular fragments of a record, while fast moving of the “read pointer” within a compressed file and extracting of particular data blocks is required. In other words we need to provide fast read-only random access to the compressed data. OS “built-in” and/or well-known compression software utilities (such as Zip) generally have the same disadvantage: they have to decompress archive file from the very beginning up to a specified point. It is impossible to start unpacking a file from an arbitrary position due to the fact that the deflate algorithm that is in the core of these utilities always needs to know some prehistory in the sequence of uncompressed bytes to proceed with decompression. It is of no importance for small files, because unpacking routines are fast and highly optimized, plus unpacked data can be easily cached. In the meantime, for large telemetry records the problem remains. In this paper the author proposes an effective method that allows to make read-only random access to the packed data faster. This is achieved by inserting some supplementary information into the archive file while it is being recorded. This extra information allows starting decompression from a set of "reference points" within the archived file, but not only from zero index byte. Required modifications in the famous zlib compression library are described.