Electro-Optical Tracker Fused Track Construction
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Solution Overview
Problem
Existing tracking systems from multiple electro-optical (EO) sources struggle to construct high-quality fused tracks of moving targets.
Innovation Solution
A tracking system that includes an EO sensor providing EO detections and an EO tracker configured to construct tracks. The EO tracker comprises a pre-processing system for geo-referencing, a candidate track selection system, a track initializing module, an assigning system, a data updating system, and a picture providing system to generate and score track pictures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple EO sources are used for tracking, then the quantity and coverage of target detections increase, but the complexity of constructing accurate fused tracks increases
Solution Approach 1:
The patent segments the track construction process into distinct functional modules: detection collection, candidate track generation, track association, and fused track construction. Each module handles specific aspects of processing detections from multiple EO sources, breaking down the complex overall task into manageable segments that can be processed independently and systematically.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between raw detections and final fused tracks. Candidate tracks serve as intermediaries that aggregate detections before final association, and the system uses intermediate confidence scores and quality metrics to mediate the fusion process, reducing direct complexity between multiple sources and final track output.
2Reliability
If traditional tracking methods are used, then the system is simpler to implement, but the quality of fused tracks is insufficient
Solution Approach 1:
The patent implements feedback mechanisms where track quality metrics and association confidence scores are continuously evaluated and used to adjust the tracking process. The system provides feedback on detection quality, track consistency, and association reliability, using this information to refine candidate selection and improve fused track quality while managing system complexity through adaptive control.
Solution Approach 2:
The patent changes key parameters such as detection confidence thresholds, track association weights, and quality metric criteria to optimize fused track reliability. By adjusting these parameters based on operational conditions and data quality, the system achieves high-quality track fusion without requiring fundamentally more complex system architecture.
3Measurement precision
If all detection data is processed in detail, then tracking accuracy improves, but processing time and computational load increase
Solution Approach 1:
The patent applies partial processing by focusing computational resources on the most relevant detections and candidate tracks. Instead of processing all detection data with equal detail, the system identifies and processes high-priority candidates with higher accuracy while using simplified processing for lower-priority cases, achieving good tracking accuracy with reduced overall processing time.
Solution Approach 2:
The patent performs preliminary filtering and candidate selection before detailed track association. By pre-processing detections to identify promising candidates and pre-establishing association rules, the system reduces the computational burden of detailed processing while maintaining tracking accuracy for the most important target relationships.
Data Source
Figure 1
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Figure 3C~3E
AI summary
A tracking system 10 and method for constructing pictures of tracks of moving targets on a scene from electro-optical (EO) detections are described. The system includes EO sensors 11, and an EO tracker 12 constructing pictures of the tracks of the targets. The EO tracker 12 includes a pre-processing system 120 for receiving EO detections of the targets and locating the targets on a world map, a candidate track selection system 130 for finding candidate tracks, a track initializing module for receiving the EO detections and initializing a new track, an assigning system for assigning the EO detections to the candidate tracks, a data updating system 170 for updating the candidate tracks after the assigning, and for generating an ambiguity set of tracks and an exclusion set of tracks, a picture providing system for receiving the ambiguity set and the exclusion set to generate a picture, and a tracker database 150 for storing the updated tracks.