Non-Resolved Target Detection Modeling for Low-SNR Space Tracking
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Solution Overview
Problem
Existing trajectory estimation techniques for satellites and space debris require complex sensor performance modeling and high-frequency data processing, making it difficult to accurately predict and avoid conjunctions in cislunar orbits.
Innovation Solution
A system and method for rapidly modeling non-resolved object detection performance using Electro-Optical (EOIR), RADAR, and Quantum RADAR sensors, incorporating physics-based interactions and sensor models to estimate signal-to-noise ratios (SNR) and probability of detection, enabling fast and accurate trajectory prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex sensor performance modeling and high-frequency data processing are used for trajectory estimation, then measurement precision and reliability are improved, but device complexity and computational requirements increase significantly
Solution Approach 1:
The patent transforms the complex sensor performance modeling problem into a parameter estimation problem by changing the approach from direct physical modeling to statistical parameter inference. The system estimates detection probability parameters and SNR values without requiring complex physics-based sensor models, thereby reducing computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent replaces complex mechanical/physical sensor modeling with statistical and probabilistic methods. Instead of simulating detailed sensor physics and environmental interactions, the system uses detection probability models and signal-to-noise ratio calculations to achieve accurate trajectory estimation with reduced computational burden.
2Measurement precision
If millions of measurements from multiple sensor platforms at high measurement rates are collected, then trajectory prediction accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential information needed for trajectory prediction from the vast sensor data stream. Instead of processing all raw measurements, the system extracts detection probability parameters and SNR values that capture the critical information while discarding redundant data, thereby reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent uses a selective processing approach where not all sensor measurements are processed in full detail. The system processes data at different levels of fidelity, using simplified models for most measurements and reserving complex processing only for critical detection events, thereby reducing overall computational time while maintaining sufficient accuracy.
3Reliability
If quantum illumination probability of detection models are implemented, then detection performance in low-SNR environments is improved, but computational complexity and model sophistication increase
Solution Approach 1:
The patent uses simplified probability of detection models that replicate the essential behavior of complex quantum illumination models without requiring full quantum mechanical simulations. The system creates approximate models that capture the key detection characteristics in low-SNR environments while reducing computational complexity to practical levels.
4Ease of operation
If user interfaces and user information are provided for understanding predicted trajectories, then ease of operation is improved, but system complexity and information processing requirements increase
Solution Approach 1:
The patent extracts only the most critical trajectory information for presentation to users, omitting unnecessary technical details. The user interface displays essential parameters such as detection probability, SNR values, and key trajectory predictions, allowing operators to understand and act on predictions without being overwhelmed by system complexity.
Data Source
AI summary
Systems and methods are provided for modeling non-resolved object detection performance of sensor systems. An exemplary method includes: receiving a first data from or associated with a plurality of sensors; creating, using the first data, a plurality of models for each of the sensors; calculating, using the models and the first data, signal-to-ratio (SNR) values for the sensors against a target; creating, using the SNR values, a plurality of probability of detection models for each of the sensors, wherein one of the probability of detection models is a quantum illumination probability of detection model; and determining, using the detection models, non-resolved target detection performance of each of the sensors.