Dual Radar Processing Channels for Moving and Stationary Object Detection
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Solution Overview
Problem
Current radar systems face challenges in optimally processing moving and stationary objects due to differing optimal processing time intervals and algorithms, leading to suboptimal results, especially in applications like autonomous driving where high update rates and accurate angular measurements are required.
Innovation Solution
Implementing a dual processing method using two processing channels, where one channel is optimized for moving objects with fixed duration processing and the other for stationary objects with processing time intervals during which the radar travels a fixed distance, utilizing specific clustering algorithms and noise mitigation techniques to generate comprehensive range/Doppler/angle object detections and tracks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single processing channel is used for both moving and stationary objects, then the device complexity is reduced, but the measurement precision and update rates for both object types deteriorate
Solution Approach 1:
The radar processing system is divided into two separate processing channels: a first processing channel optimized for detecting and tracking moving objects, and a second processing channel optimized for detecting and mapping stationary objects. Each channel uses tailored processing parameters and algorithms suited to its specific object type, thereby improving measurement precision without requiring excessive complexity in a single unified system.
Solution Approach 2:
The system dynamically selects which processing channel to use based on the detected object's motion characteristics. The radar system can adaptively switch between moving object processing and stationary object processing modes, optimizing performance for the current operational context while maintaining manageable system complexity.
2Measurement precision
If different processing time intervals are used for moving and stationary objects, then the measurement precision for both object types is improved, but the device complexity increases
Solution Approach 1:
The processing time interval parameter is segmented and optimized separately for each object type within its dedicated processing channel. The first processing channel uses time intervals suited for capturing moving object dynamics, while the second processing channel uses longer intervals appropriate for stationary object mapping, thereby achieving high precision for both without requiring a single overly complex adaptive system.
3Productivity
If fixed duration processing is used for moving objects, then the update rate is improved, but the processing accuracy for stationary objects deteriorates
Solution Approach 1:
The processing system segments stationary object detection into a separate second processing channel that uses extended processing time intervals. This allows the first processing channel to maintain high update rates for moving objects using fixed duration processing, while the second channel accumulates data over longer periods to achieve high precision for stationary objects without compromising either performance metric.
4Measurement precision
If processing time intervals are extended for stationary objects, then the measurement precision is improved, but the update rate deteriorates
Solution Approach 1:
The system segments the processing workload so that stationary object detection with extended time intervals for high precision angular measurements occurs in the second processing channel, while the first processing channel handles moving objects with faster update rates. This segmentation allows each channel to optimize its update rate according to the specific requirements of its object type without compromising the other.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides enhanced accuracy and update rates for both moving and stationary object detection and tracking, meeting the demands of applications like autonomous driving by synergistically combining the outputs from multiple processing channels.
Implementation Method 1
Radars, for example, automotive radars, may be used to process two generic types of objects, moving and stationary
Implementation Method 2
generating a first set of range/Doppler images and a second set of range/Doppler images from a radar system
Data Source
AI summary
Aspects of the disclosure are directed to dual processing. Accordingly disclosed are, an apparatus and a method for dual processing for stationary objects and moving objects including generating a first set of range/Doppler images and a second set of range/Doppler images from a radar system, wherein the first set of range/Doppler images is processed over a first processing time and the second set of range/Doppler images is processed over a second processing time; using a first clustering algorithm to generate a first set of range/Doppler/angle object detections based on the first set of range/Doppler images; using a second clustering algorithm to generate a second set of range/Doppler/angle object detections based on the second set of range/Doppler images; and generating a set of range/Doppler/angle object tracks for stationary and moving objects from the first set of range/Doppler/angle object detections and the second set of range/Doppler/angle object detections.