Vehicle Radar Kinetic Estimation With Spatial and Kalman Filtering
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Solution Overview
Problem
Existing methods for determining kinetic information related to a vehicle using a single radar device face challenges in accuracy, particularly in distinguishing between stationary and moving objects, leading to errors in estimating longitudinal speed and yaw rate.
Innovation Solution
A method involving spatial filtering to select candidate kinetic information from stationary objects, combined with a kinetic model for predicting and correcting estimated kinetic information using a weighted mean and Kalman filter, to enhance accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single radar device is used to estimate kinetic information, then the device complexity is reduced, but the measurement precision of longitudinal speed and yaw rate deteriorates due to difficulty in distinguishing stationary and moving objects
Solution Approach 1:
The patent segments the kinetic information estimation process into multiple independent stages: candidate kinetic information generation from radar data, spatial filtering to select valid candidates, temporal filtering to resolve conflicts between candidates, and kinetic model-based correction. This segmentation allows each stage to focus on a specific aspect of the problem, improving overall measurement precision without adding hardware complexity.
Solution Approach 2:
The patent introduces an intermediary kinetic model that acts as a mediator between raw radar measurements and final kinetic information estimation. The kinetic model provides physically consistent predictions that bridge the gap between noisy sensor data and accurate kinetic parameters, resolving the precision issue without requiring additional radar devices.
2Measurement precision
If spatial filtering and kinetic model correction are applied, then the measurement precision of kinetic information is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary spatial filtering to identify and select reliable candidate kinetic information from stationary objects before temporal filtering and kinetic model correction. By pre-processing the data to eliminate obvious outliers and select high-quality candidates early in the pipeline, the computational burden of subsequent processing stages is reduced while maintaining high measurement precision.
Solution Approach 2:
The kinetic model serves itself by using its own previous predictions and the current sensor measurements to correct its estimate through the Kalman filter. This self-correcting mechanism improves precision without requiring external complex processing systems, as the model leverages its inherent structure and previous state information.
3Reliability
If candidate kinetic information from multiple objects is considered, then the reliability of estimation is improved, but the difficulty of detecting and measuring valid kinetic information increases
Solution Approach 1:
The patent extracts and isolates kinetic information from stationary objects by applying spatial filtering that specifically identifies candidates with minimal spatial displacement between time frames. This extraction process separates valid kinetic information from noise and moving object interference, improving reliability while reducing the difficulty of identifying valid candidates through clear discrimination criteria.
Solution Approach 2:
The patent implements feedback through the temporal filtering stage, where candidate kinetic information from multiple objects is continuously evaluated and corrected based on kinetic model predictions. The feedback loop compares multiple candidates against physical constraints and previous estimates, reliably identifying valid information while managing detection difficulty through iterative refinement.
Data Source
AI summary
A method of determining kinetic information may include: receiving a plurality of raw information related to a plurality of objects using a radar device provided in a vehicle; obtaining, by analyzing the plurality of raw information, a plurality of candidate kinetic information related to the vehicle; estimating, through spatial filtering, current first kinetic information related to the vehicle from the plurality of candidate kinetic information; and correcting, using a kinetic model, the estimated current first kinetic information based on current first kinetic information, wherein the current first kinetic information is predicted from previous first kinetic information related to the vehicle using a kinetic model.