Linear Prediction Bistatic Detector for Automotive Radar
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Solution Overview
Problem
Automotive radar systems face challenges in crowded environments due to multi-path detections, which can lead to erroneous radar returns and unsafe driving behavior, as they struggle to distinguish between bistatic and static conditions, causing errors in object localization and resource saturation.
Innovation Solution
A linear prediction-based bistatic detector is implemented in automotive radar systems to identify bistatic conditions, allowing the system to discard unreliable radar returns and focus on processing static object detections, thereby enhancing the accuracy and efficiency of object localization and vehicle control in autonomous or semi-autonomous modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If radar systems process all radar returns in crowded environments, then detection coverage is improved, but measurement precision deteriorates due to multi-path detections causing erroneous object localization
Solution Approach 1:
The system performs preliminary classification of radar returns into bistatic and static categories before processing. By using a linear prediction-based bistatic detector to identify and separate bistatic returns in advance, the system prevents erroneous localization data from contaminating the final object detection results, thereby maintaining measurement precision while preserving detection coverage.
2Measurement precision
If radar systems discard radar returns to eliminate multi-path detections, then measurement precision is improved, but detection coverage deteriorates due to loss of valid static object detections
Solution Approach 1:
The system performs preliminary classification of radar returns into bistatic and static categories before processing. By using a linear prediction-based bistatic detector to identify and separate bistatic returns in advance, the system prevents erroneous localization data from contaminating the final object detection results, thereby maintaining measurement precision while preserving detection coverage.
3Area of stationary object
If radar systems process all radar returns including multi-path detections, then detection coverage is improved, but resource consumption deteriorates due to resource saturation from processing erroneous returns
Solution Approach 1:
The system extracts and removes bistatic radar returns from the processing pipeline using a linear prediction-based detector. By separating problematic bistatic returns before they enter the main processing chain, the system reduces computational resource consumption while maintaining detection coverage for valid static objects.
Solution Approach 2:
The system performs preliminary classification of radar returns into bistatic and static categories before processing. By using a linear prediction-based bistatic detector to identify and separate bistatic returns in advance, the system prevents erroneous localization data from contaminating the final object detection results, thereby maintaining measurement precision while preserving detection coverage.
4Device complexity
If radar systems use traditional detection methods without bistatic classification, then device complexity is reduced, but reliability deteriorates due to unsafe driving behavior from erroneous detections
Solution Approach 1:
The system performs preliminary classification of radar returns into bistatic and static categories before processing. By using a linear prediction-based bistatic detector to identify and separate bistatic returns in advance, the system prevents erroneous localization data from contaminating the final object detection results, thereby maintaining measurement precision while preserving detection coverage.
Data Source
AI summary
The disclosure provides systems, apparatuses, and techniques for operating automotive MIMO radars in crowded multi-path environments to obtain reliable detections by linearly predicting whether a bistatic condition occurred. To avoid saturating computing resources processing bistatic detections, the described techniques enable a radar system to quickly identify and discard from the field-of-view radar detections that are likely a result of bistatic conditions. By ignoring unusable radar returns that are likely a result of bistatic conditions, an example radar system can focus on processing radar returns from static conditions, for example, in providing radar-based detections as output to an automotive system that is driving a vehicle in an autonomous or a semi-autonomous mode. In so doing, the example radar system provides a highly accurate static object detector that is sufficiently quick in detecting bistatic conditions for use in vehicle-safety systems as well as autonomous and semi-autonomous control.