Generalized Sensor Data Compression for Satellite Bandwidth Bottlenecks
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Solution Overview
Problem
The limited wireless transmission bandwidth in satellite systems leads to delays in transmitting large amounts of sensor data collected by satellites, as existing compression methods are inefficient in prioritizing and compressing data effectively.
Innovation Solution
A method and system that analyze sensor data to identify high and low interest data, using a multi-stage lossless compression algorithm for high interest data and progressive or lossy compression for low interest data, prioritizing the transmission of high interest data to avoid bandwidth bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is transmitted without compression, then complete data is transmitted, but transmission time increases and bandwidth is exceeded
Solution Approach 1:
The patent segments sensor data into high-interest and low-interest categories based on analysis of data characteristics and mission requirements. This segmentation allows differential compression strategies to be applied: lossless compression for high-interest data and lossy compression for low-interest data, thereby resolving the contradiction between maintaining data completeness and reducing transmission time.
Solution Approach 2:
The patent applies different compression qualities to different portions of the data based on their importance. High-interest data receives lossless compression to maintain complete accuracy, while low-interest data receives lossy compression to reduce size. This local quality differentiation allows the system to prioritize critical information while accepting some loss in less critical areas, solving the time-completeness tradeoff.
2Measurement precision
If lossless compression is applied to all data, then data accuracy is maintained, but compression efficiency decreases
Solution Approach 1:
The patent divides the data stream into high-interest and low-interest segments, applying lossless compression only to the high-interest portion where accuracy is critical. This segmentation enables the system to maintain measurement precision for important data while improving overall compression efficiency by using more aggressive compression on less critical data.
Solution Approach 2:
The patent applies different compression qualities locally based on data importance. High-interest data receives lossless compression to preserve accuracy, while low-interest data receives lossy compression to maximize compression efficiency. This local quality approach resolves the contradiction by ensuring accuracy where needed while optimizing overall compression performance.
3Productivity
If data is prioritized by interest level, then transmission efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements a segmentation-based approach that categorizes data into high-interest and low-interest groups based on analysis of sensor data characteristics and mission requirements. This segmentation enables prioritized transmission where high-interest data is compressed losslessly and transmitted first, improving transmission efficiency while keeping the complexity manageable through systematic classification.
Solution Approach 2:
The patent performs preliminary analysis and classification of sensor data before compression and transmission. By identifying high-interest and low-interest data in advance, the system can apply appropriate compression strategies and prioritize transmission accordingly. This preliminary action improves transmission efficiency by preparing data in advance, while the automated classification process manages system complexity through systematic pre-processing.
Data Source
AI summary
Methods and apparatus for compression of generalized sensor data are described. One example method for use in compression of generalized sensor data at a first location for transmission to a second location includes analyzing the sensor data to identify high interest data and low interest data, and compressing the high interest data with a lossless compression algorithm.