Sensor Fusion Track Selection Using Error Ellipsoid Volume Ratios
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Solution Overview
Problem
Sensor fusion technologies face challenges in reducing track error and minimizing system error due to inconsistent covariance matrices, which can lead to increased average state vector errors when combining data from disparate sensors.
Innovation Solution
The method involves a processor-based algorithm that selects the best track from multiple sensors by calculating error ellipsoid volumes and ratios to determine the fused track, which minimizes system error by choosing between the fused track and the best track based on containment criteria and pre-determined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor fusion combines data from multiple sensors, then information completeness improves, but system error increases due to inconsistent covariance matrices
Solution Approach 1:
The patent changes the selection criterion from traditional covariance-based metrics to error ellipsoid volume ratios. By calculating the ratio of intersection volume to individual ellipsoid volumes, the system adapts to inconsistent covariance matrices and selects tracks that minimize system error while maintaining information completeness from multiple sensors.
Solution Approach 2:
The patent performs preliminary calculations of error ellipsoid volumes and ratios before final track selection. By pre-computing these geometric parameters and establishing containment criteria, the system prepares selection thresholds in advance, enabling efficient real-time decision-making that balances information fusion with error minimization.
2Device complexity
If traditional sensor fusion is used with inconsistent covariance matrices, then data combination is simplified, but track error increases
Solution Approach 1:
The patent replaces traditional covariance matrix-based fusion mechanics with a geometric error ellipsoid approach. Instead of relying on covariance consistency assumptions, the system uses volume ratio calculations and containment criteria to select tracks, substituting the mechanical fusion process with a geometric selection mechanism that is more robust to inconsistent matrices.
3Measurement precision
If error ellipsoid volume calculations are performed, then track selection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the track selection process into distinct stages: calculating individual error ellipsoid volumes, computing intersection volumes, determining volume ratios, and applying containment criteria. This segmentation allows the system to manage computational complexity by breaking down the complex selection problem into manageable geometric calculations that can be performed efficiently.
Data Source
AI summary
Aspects of the disclosure provide a method and an apparatus for sensor fusion. The method includes receiving, by a processor, track reports of at least a first sensor and a second sensor for sensor fusion, selecting a best track from at least the first sensor and the second sensor to reduce track error, determining a fused track that combines at least the first sensor and the second sensor and selecting one of the fused track and the best track as a system track of sensor fusion to minimize a system error.