Sensor Uncertainty Modeling for Central-Level Tracking Fusion
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Solution Overview
Problem
Existing methods for modeling sensor uncertainty in central-level tracking architectures are expensive and memory-intensive, requiring extensive data campaigns and full statistical information from sensor manufacturers, which is often not available, leading to inefficiencies in data association and tracking performance.
Innovation Solution
A method for modeling sensor uncertainty involves acquiring sensor detections, setting association gate thresholds, modeling detection errors based on distance, computing covariance matrices, and storing them for fusion, without needing full statistical sensor data, thus reducing costs and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data driven modeling of sensor error is used, then measurement precision is improved, but device complexity and memory requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for uncertainty modeling from sensor data, specifically the relationship between detection error and distance. This selective extraction avoids the need to store and process complete sensor error distributions, reducing memory requirements while maintaining sufficient accuracy for data association.
Solution Approach 2:
The patent transforms the complex sensor error characteristics into a simplified parameter model where uncertainty is expressed as a function of distance. By changing from storing full error distributions to using distance-based parameterization, the system achieves reduced complexity while maintaining measurement precision for tracking applications.
2Measurement precision
If full statistical information from sensor manufacturers is used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies partial action by using only the portion of sensor information that is actually needed for tracking - specifically the distance-dependent error characteristics. This avoids the excessive action of processing complete statistical information from manufacturers, reducing processing complexity while maintaining sufficient precision for the application.
3Measurement precision
If data campaigns are conducted to extract sensor error data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing the relationship between detection error and distance based on sensor characteristics. This allows the system to use simple lookup tables or formulas during actual tracking, avoiding the need for time-consuming data campaigns during system operation while maintaining accurate uncertainty estimation.
Data Source
AI summary
A method of modelling the uncertainty of sensors in a central-level tracking architecture system for fusing a plurality of the sensor detections in respect to at least one sensor object. The detections acquired from the sensors correspond to at least one dimension of the at least one sensor object. For each sensor it is set a predetermined association gate minimum threshold for each dimension by setting a predetermined amount of sensor detection error for each dimension based on which an increase of each sensor detection error is modeled as a function of distance. An association gate is computed by increasing according to the modeling each sensor detection error above the predetermined association gate minimum threshold. A covariance matrix is computed for each sensor associated to each detection. A plurality of covariance matrices corresponding to all sensors is stored and made available to the central-level tracking system for fusing the plurality of sensor detections.