Multi-Sensor Fusion for SAR Motion Compensation
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Solution Overview
Problem
Existing GPS-based motion compensation systems for Synthetic Aperture Radar (SAR) imaging are vulnerable to jamming, leading to position errors and reduced image clarity, and rely on pre-stored geo-registered features which increase complexity and reduce reliability.
Innovation Solution
A system that uses multiple sensors to estimate motion by sharing target location errors via a data link, combining these errors using a Kalman filter to update sensor positions without relying on pre-registered features or GPS, ensuring accurate motion compensation even in GPS-unavailable conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based motion compensation is used for SAR imaging, then position information is obtained, but the system becomes vulnerable to jamming causing position errors and reduced image clarity
Solution Approach 1:
The patent introduces an intermediary system consisting of multiple independent sensors (INS, GPS, SAR) that share measurements through a data link. When GPS is jammed, the system uses SAR imagery of geo-registered features as an intermediary reference to compute position updates, thereby maintaining reliability without sacrificing measurement precision.
Solution Approach 2:
The system dynamically changes operational parameters by switching between different sensor fusion strategies based on GPS availability. When GPS is available, standard INS/GPS integration is used; when jammed, the system transitions to using SAR-derived position updates from geo-registered features, thereby maintaining position accuracy under varying conditions.
2Measurement precision
If pre-stored geo-registered features are used for position updates instead of GPS, then position accuracy is maintained during GPS unavailability, but device complexity increases and reliability decreases
Solution Approach 1:
The SAR sensor serves multiple functions: it performs its primary imaging function and simultaneously provides position updates by detecting geo-registered features. This multi-functionality eliminates the need for separate position reference systems, thereby maintaining position accuracy without significantly increasing device complexity.
Solution Approach 2:
The system uses its own SAR sensor to provide position updates by automatically detecting and matching geo-registered features in the imagery. This self-service capability eliminates the need for external position reference systems or additional specialized equipment, thereby maintaining position accuracy while minimizing added complexity.
3Measurement precision
If multiple sensors are used to share target location errors, then motion compensation accuracy is enhanced, but the quantity of sensors and data processing increases
Solution Approach 1:
The patent merges measurements from multiple sensors (INS, GPS, SAR) into a unified navigation solution through sensor fusion. By combining the complementary strengths of each sensor and sharing target location errors through a data link, the system achieves enhanced motion compensation accuracy without requiring a proportional increase in the number of sensors.
Data Source
AI summary
The need for geo-registered features is avoided by a system for estimating a motion of a first sensor during a time interval. The system has a second sensor where the first sensor and the second sensor sense the same geo-location during the time interval. The first sensor computes a first target location error from sensing the geo-location. The second sensor also computes a second target location error from sensing the geo-location. A data link interconnects the first sensor and the second sensor, the data link transmitting the second target location error computed by the second sensor to the first sensor during the time interval. A processor at the first sensor combines the first target location error and the second target location error in a first sensor observation model, where the sensor observation model is descriptive of the motion of the first sensor. The observation model is used with a Kalman filter to update the position of the first sensor. This combination of the first sensor observation model and the second sensor observation model generates a more accurate target location error at the first sensor. The principle is extended to a plurality of platforms.