Radar Track Association Using Extended Field-of-View Filtering
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Solution Overview
Problem
Automated systems for object tracking in industries like aviation and automotive face challenges in accurately associating sensor measurements with object tracks, leading to excessive data storage and computational burdens due to the use of multiple sensors, which often do not improve accuracy and overwhelm onboard vehicle systems with voluminous data.
Innovation Solution
The method involves receiving radar data from a vehicle's radar sensor, determining an extended field of view, and associating measurement data with object tracks to identify the most relevant tracks for navigation, thereby reducing the track representation space and optimizing data processing by pre-eliminating unnecessary computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are utilized to improve object detection likelihood, then detection reliability is improved, but data volume and computational burden increase exponentially
Solution Approach 1:
The patent extracts only the relevant subset of tracks that lie within the extended field of view of the current sensor, rather than processing all tracks from multiple sensors. This selective extraction reduces data volume while maintaining detection reliability by focusing computational resources on relevant objects.
Solution Approach 2:
The patent segments the track set into multiple subsets based on spatial relationships with sensor fields of view. By dividing tracks into groups associated with different sensors and time periods, the system processes smaller manageable subsets rather than one monolithic data set, reducing computational burden while maintaining comprehensive coverage.
2Reliability
If multiple sensors are utilized to provide redundant detection, then detection reliability is improved, but onboard computational resources are overwhelmed
Solution Approach 1:
The patent applies partial action by performing data association only for tracks within the extended field of view rather than all tracks. This selective processing reduces computational complexity while maintaining sufficient detection reliability by focusing on the most relevant objects that the current sensor is likely to observe.
Solution Approach 2:
The patent performs preliminary actions by pre-computing extended fields of view and pre-identifying relevant track subsets before actual data association occurs. This advance preparation reduces the complexity of real-time processing by organizing data structures and filtering candidates beforehand.
3Loss of information
If all sensor data are statically stored as tracks, then complete object history is maintained, but data storage requirements become expensive and cumbersome
Solution Approach 1:
The patent applies local quality by maintaining detailed track information only for objects within or near the current sensor's field of view, while using coarser or summarized representations for distant objects. This spatially-varying data retention strategy preserves necessary tracking information locally while reducing overall storage requirements.
4Measurement precision
If data association is performed with all tracks, then association accuracy is maximized, but processing time increases significantly
Solution Approach 1:
The patent performs data association only with a partial subset of tracks that lie within the extended field of view, rather than all tracks. This selective association maintains sufficient accuracy by focusing on relevant objects while significantly reducing processing time by excluding obviously irrelevant tracks from the association computation.
Data Source
AI summary
Systems and methods for detecting and tracking objects using radar data are disclosed. The methods include receiving measurement data corresponding to an environment of a vehicle from a radar sensor associated with the vehicle, and identifying one or more object tracks of a plurality of object tracks that lie within an extended field of view (FOV) of the radar sensor. The extended FOV may be determined based on an original FOV of the radar sensor. The methods further include performing association of the measurement data with the one or more object tracks to identify an associated object track, and outputting the associated object track and the measurement data to a navigation system of the vehicle.