Beamvector Filtering for Radar Ego-Motion Precision
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Solution Overview
Problem
Radar data often contains inherent errors and outlier measurements, leading to ambiguities in ego-motion estimation due to moving targets and Doppler frequency exceeding the pulse repetition frequency, which degrades the precision of calculated ego-motion information.
Innovation Solution
A method for processing radar data that involves acquiring and filtering beamvectors to remove features corresponding to moving targets and ambiguous range rate data, allowing for the determination of vehicle and environmental characteristics such as velocity and yaw rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar data is used for ego-motion estimation, then velocity and yaw rate information can be obtained, but measurement errors from moving targets and Doppler ambiguities degrade estimation precision
Solution Approach 1:
The patent extracts and removes problematic features from the radar data. Specifically, it identifies and removes beamvector features corresponding to moving targets and ambiguous range rate data, leaving only features from stationary targets for ego-motion estimation. This extraction process directly addresses the reliability issue by eliminating corrupted measurements while preserving useful information.
Solution Approach 2:
The patent introduces an intermediary filtering mechanism that processes radar data before ego-motion estimation. The filter uses velocity profile patterns and spatial frequency analysis as intermediary steps to identify and remove contaminated beamvectors. This intermediary processing layer protects the final estimation from being degraded by unreliable radar measurements.
2Quantity of substance
If all radar measurements are used for ego-motion estimation, then more data is available, but outlier measurements from moving targets degrade the accuracy of stationary target detection
Solution Approach 1:
The patent segments the radar data into distinct categories based on their suitability for ego-motion estimation. It divides beamvectors into those corresponding to stationary targets (useful) and those to moving targets or ambiguous measurements (useless or harmful). This segmentation allows selective use of only the relevant data portion, maintaining accuracy while utilizing available radar data.
Solution Approach 2:
The patent extracts and removes outlier measurements from the radar data stream. By identifying beamvectors with characteristics indicative of moving targets or Doppler ambiguities and removing them, the system preserves the accuracy of stationary target detection while still utilizing the full radar data quantity for estimation.
3Speed
If Doppler frequency exceeds pulse repetition frequency, then fast moving targets can be detected, but range rate measurements become ambiguous and degrade ego-motion estimation
Solution Approach 1:
The patent extracts and removes beamvector features corresponding to ambiguous range rate data. By identifying and removing these contaminated measurements, the system maintains the ability to detect fast moving targets while eliminating the ambiguity that would otherwise degrade ego-motion estimation precision.
Solution Approach 2:
The patent changes the processing parameters by analyzing velocity profile patterns and spatial frequency characteristics to identify ambiguous measurements. This parameter transformation allows the system to recognize and filter out Doppler ambiguous data while preserving valid high-velocity target information.
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 filtered radar data for accurate ego-motion estimation by distinguishing between stationary and moving targets and resolving Doppler ambiguities, enhancing the precision of ego-motion information.
Implementation Method 1
use the Doppler effect of the reflected signal to determine a velocity (range rate) of the target
Data Source
Figure 1A~1B
Figure 2
Figure 3
AI summary
A method for processing radar data of a vehicle, the method comprising: acquiring radar data from each of one or more radar antennas, the acquired radar data comprising range rate (Doppler) data; determining beamvectors from the acquired radar data; filtering the beamvectors, wherein the filtering comprises removing, from the beamvectors, features corresponding to one or more moving targets and/or features corresponding to ambiguous range rate (Doppler) data, to determine filtered beamvectors; and determining a characteristic of an environment of the vehicle and/or of the vehicle itself based on the filtered beamvectors.