Multi-Order Differencing for Lossless GPS Ephemeris Compression
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Solution Overview
Problem
Existing data compression methods for GPS long-term Ephemeris (LTE) data are inefficient, particularly for client devices with limited resources, as they often result in high computing resource requirements and may incur information loss, necessitating the development of a lossless and efficient compression and decompression scheme.
Innovation Solution
A multi-order differencing scheme is employed to compress GPS LTE data by grouping data values based on their distribution patterns, where differences of varying orders are calculated and encoded, allowing for efficient storage and retrieval of original data values, adaptable to different data types and distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression methods are used for GPS LTE data, then data size is reduced, but computing resource requirements increase and information loss occurs
Solution Approach 1:
The patent applies parameter changes by transforming the data representation from original values to difference values of various orders. This transformation changes the statistical parameters of the data, allowing for more efficient compression while maintaining lossless reconstruction capability through inverse differencing operations.
Solution Approach 2:
The patent segments the compression process into multiple orders of differencing (first-order, second-order, etc.), where each order captures different levels of data variation. This segmentation allows selective application of compression techniques to different data characteristics without losing information.
2Quantity of substance
If conventional data compression methods are used for GPS LTE data, then data size is reduced, but computing resource requirements increase
Solution Approach 1:
The patent performs preliminary differencing operations on the data before compression, transforming it into a form that requires fewer computing resources during decompression. The difference values are calculated and stored in an encoded format that reduces the computational burden on client devices with limited resources.
Solution Approach 2:
By changing the data parameters from original values to difference values, the patent reduces the dynamic range and variability of the data, which in turn reduces the computing resources needed for processing while maintaining complete information for reconstruction.
3Productivity
If multi-order differencing is applied to compress data, then computing efficiency improves, but compression scheme complexity increases
Solution Approach 1:
The patent implements a dynamic multi-order differencing scheme where the order of differencing is selected based on the characteristics of the input data. This dynamic adaptation optimizes compression efficiency for different data patterns while managing complexity through conditional logic rather than fixed complex structures.
Solution Approach 2:
The patent applies partial differencing by selecting only the necessary order of differencing required for each data set. Instead of always applying maximum-order differencing, it uses just enough transformation to achieve efficient compression, reducing unnecessary computational complexity.
Data Source
AI summary
Embodiments of the present invention enable compression and decompression of data. Applications of the present invention are its use in embodiments of systems for compression and decompression of GPS long-term Ephemeris (LTE) data, although the present invention is not limited to such applications. In embodiments, the LTE data may be grouped into a set of data values associated with a parameter. In embodiments, a data set may be compressed by using a multi-order differencing scheme. In such a scheme, a set of the differences between values may be compressed because the differences have smaller magnitudes than the values. In embodiments, a multi-order differencing scheme determines how many levels (orders) of differencing may be applied to an original data set before it is compressed. In embodiments, the original data may be recovered from a compressed data set based on the type of multi-order differencing scheme used to generate the compressed data.