OPTIMUM AND SUB-OPTIMUM COMPRESSION OF SECOND ORDER MARKOV DATA,
Abstract
A new and more realistic model for telemetry data has been developed and analyzed. The model is Markovian in nature due to each data sample's statistical dependence on previous sample values and activity state. The conditional mode and conditional mean predictors are derived from the data model and shown to be optimum compression techniques. For the optimum techniques, expressions are developed for the error probability distribution and the redundant sample run length distribution as a function of aperture width. Monte Carlo techniques are employed to generate several varieties of data from the proposed model. Conventional compression methods such as the fan interpolator and the zero-order predictor are then used to process the generated samples. Experimental results confirm the superiority of the optimum techniques and also indicate that of the two conventional methods, the fan technique is preferred for small apertures whereas the zero-order predictor is more efficient for large aperture processing. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 10, 1969
- Accession Number
- AD0692379
Entities
People
- Arthur J. Bruckheim
Organizations
- Naval Ordnance Laboratory