Autonomous Data Prioritization Module for Spacecraft Downlinking
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Solution Overview
Problem
Current spacecraft data transmission systems lack an intelligent decision-making strategy to prioritize scientifically significant data for downlinking over limited bandwidth, resulting in potential loss of valuable observations.
Innovation Solution
An autonomous decision-making module is developed to prioritize data transmission by using an expert-guided rule-based system that assigns a relevance score to data based on scientific value, novelty, and anomalous characteristics, incorporating domain knowledge to emulate expert-like identification of relevant datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is transmitted over limited bandwidth, then transmission capacity is constrained, but scientific value of downlinked data is reduced
Solution Approach 1:
The system extracts and transmits only the most scientifically valuable data subsets from the complete acquired dataset. By applying expert-guided rules and machine learning models, the system identifies and extracts high-value observations (such as novel geological features, anomalous events, or scientifically significant phenomena) and prioritizes their transmission over limited bandwidth, thereby maximizing the scientific return per unit of downlink volume.
Solution Approach 2:
The system dynamically changes the selection parameters for data downlinking based on scientific criteria. Instead of transmitting data uniformly or based on simple metrics, the system adjusts selection parameters to prioritize data with high scientific value, novelty, and significance. This parameter change enables the system to adapt the downlinked subset to maximize scientific insights within bandwidth constraints.
2Productivity
If automated prioritization is implemented, then transmission efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer between data acquisition and transmission that applies expert-guided rules and machine learning models to prioritize data. This intermediary prioritization module acts as a mediator that automatically evaluates acquired data against scientific criteria, assigns priority scores, and determines the optimal downlink subset. This intermediary approach improves transmission efficiency by automating the selection process while managing complexity through modular architecture and pre-defined expert rules.
3Measurement precision
If expert-guided rules are applied, then data selection accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary processing and prioritization of data onboard the spacecraft using expert-guided rules and machine learning models before transmission. By applying these intelligent algorithms in situ, the system accurately identifies scientifically significant data subsets and prepares them for prioritized downlinking. This preliminary action ensures high accuracy in scientific value identification while reducing the energy burden of ground-based processing and enabling efficient use of limited bandwidth.
Data Source
AI summary
Various embodiments disclosed herein relate to systems and methods for an intelligent autonomous decision making module that maximizes the return of the most scientifically relevant dataset over the low bandwidth for experts to analyze further. A rule based knowledge extraction methodology is disclosed, guided by expert knowledge for all scientifically relevant geological landforms with respect to expert selected attributes. The datasets are subsequently prioritized based on how novel the instances are with respect to its rule and is used to update the rules. The effectiveness of the proposed approach is then determined by evaluating how acceptable the prioritization order is to experts and explaining the decisions to increase the interpretability of the assigned priority.