Lossless Compression of Telemetry Information using Adaptive Linear Prediction

Normal requirement for telemetry data compression algorithms is an ability to recover initial data “as is” without loss of information. This feature is very important in various telemetry processing applications. Precise recovery of the telemetry data as it is acquired from the original source of information is necessary for the analysis of any kind of abnormal events, recovery of bad sites within the telemetry data stream and for other types of post- or real-time data processing [1,2]. The effectiveness of methods of lossless compression is largely determined by the properties of the data under compression [3]. Compression algorithms show better compression ratios if they can adapt to the characteristics of the input data, which are in most cases rapidly change. In this paper we present the results of studies conducted to develop an efficient method of reversible telemetry data compression based on adaptive linear prediction of telemetry data packed according to IRIG-106 format. IRIG-106 is an open standard, developed specifically for aerospace industry, but now used in wide range of telemetry registration applications [4]. Data is packed to frames of fixed length and predefined internal structure. Frame can carry different sources of information: digitized samples of analog signals, as well as pure digital data. For each source a channel of the recording system is provided. The source sample in each channel is introduced by telemetry word. All words in the frame have the same bit width. Telemetry frame contains additional service information in purpose of detecting bit errors, frame synchronization, etc.Lossless data compression algorithm can be divided into two stages; the first stage - decorrelation stage, which exploits the redundancy between the neighboring samples in the data sequence, the second stage - entropy coding, which takes advantage from decreasing variance and lowering entropy of the data made on the first stage [5,6,7].