Radar Point Cloud Feature Extraction Under Bandwidth Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional radar sensor systems discard energy values from bins not identified as corresponding to objects, limiting the information transmitted to processing devices and reducing the accuracy of object detection and tracking, especially in scenarios where data tensors are impractical to transmit.
Innovation Solution
A radar system that generates point clouds by identifying peak energy values, computing feature values from neighboring bins, and transmitting updated point clouds to processing devices, which can include feature values computed using neural networks, enhancing object detection and tracking capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If only peak energy bins are selected for point cloud generation, then data transmission bandwidth is reduced, but information useful for object detection is lost
Solution Approach 1:
The patent extracts only the most relevant features (peak energy bins and selected neighboring bins) from the complete radar tensor, creating a condensed point cloud that transmits essential object detection information while discarding redundant data. This extraction approach reduces bandwidth requirements while maintaining detection accuracy.
Solution Approach 2:
The patent applies different selection criteria to different regions of the radar tensor: peak bins are selected based on energy thresholds, while neighboring bins are selectively included based on their spatial and spectral relationship to detected peaks. This local quality approach ensures that regions containing object information are preserved while other regions are compressed.
2Measurement precision
If all energy bins are transmitted to the processing device, then object detection accuracy is improved, but transmission bandwidth requirements become impractical
Solution Approach 1:
The system extracts a representative subset of bins from the complete radar tensor, selecting peak bins that contain object information and supplementary neighboring bins that provide contextual information. This extraction creates a condensed data representation that maintains detection accuracy while dramatically reducing transmission volume.
Solution Approach 2:
The patent transmits slightly more data than the absolute minimum by including neighboring bins around detected peaks. This partial excess action ensures that contextual information is preserved, improving object detection accuracy while still maintaining transmission volumes that are practical for radar systems.
3Measurement precision
If neighboring bins are selected based on predefined neighborhoods, then feature computation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the radar tensor into discrete bins organized by spatial and spectral dimensions. By defining predefined neighborhoods around peak bins, the system creates localized processing regions that can be independently analyzed. This segmentation approach improves feature computation accuracy while managing processing complexity through structured organization.
Solution Approach 2:
The system applies enhanced processing only to localized regions (neighboring bins) around detected peaks, rather than processing the entire tensor uniformly. This local quality approach concentrates computational resources on regions containing object information, improving feature accuracy while limiting overall processing complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The technologies described herein relate to a radar system that is configured to generate point clouds based upon radar tensors generated by the radar system. More specifically, the radar system is configured to identify bins in radar tensors that correspond to objects in an environment of the radar system, and to use energy values in other bins to construct a point cloud. A computing system detects objects in an environment of the radar system based upon the point clouds.