Linear Kalman Filter Radar Velocity Estimation
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Solution Overview
Problem
Current radar and lidar systems in autonomous vehicles require different Kalman models for tracking objects, making it challenging to efficiently fuse radar and lidar data, especially in adverse weather conditions.
Innovation Solution
A radar network architecture is implemented using at least two radars with overlapping fields of view, rotated at different angles, to facilitate the estimation of vector velocity components (vx and vy) of objects, allowing for the use of a linear Kalman filter to fuse radar and lidar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If radar and lidar systems use different Kalman models for tracking objects, then each sensor type can be optimized for its specific data characteristics, but the complexity of fusing radar and lidar data increases significantly
Solution Approach 1:
The patent transforms radar measurement data from polar coordinates to Cartesian coordinates, making the measurement model homogeneous with lidar data. This allows both radar and lidar to use the same linear Kalman filter framework, eliminating the need for separate extended Kalman filters for radar and simplifying the data fusion process while maintaining tracking accuracy
2Measurement precision
If radar systems use wide bandwidth signals to achieve fine range resolution, then range measurement precision improves, but the complexity of signal processing and data fusion increases
Solution Approach 1:
The patent changes the coordinate system parameter from polar (range-azimuth) to Cartesian (x-y), transforming the measurement model to match the state space representation used in linear Kalman filters. This parameter transformation simplifies the signal processing and data fusion complexity while preserving the fine range resolution achieved through wide bandwidth signals
3Productivity
If radar data is fused with lidar data later in the processing pipeline, then computational load is reduced, but the accuracy of autonomous vehicle perception decreases
Solution Approach 1:
The patent performs preliminary transformation of radar measurements into Cartesian coordinates and aligns them with the lidar coordinate system before fusion. This preliminary action enables earlier fusion in the processing pipeline without increasing computational load, thereby improving perception accuracy while maintaining processing efficiency
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 enables rapid and accurate estimation of object velocities, improving the prediction of object movement and facilitating earlier fusion of radar and lidar data, which enhances the accuracy and reliability of autonomous vehicle perception systems.
Implementation Method 1
Radar sensors are conventionally used in connection with detecting and classifying objects in an environment
Implementation Method 2
The radar sensor signals reflect off objects in the environment and the radar sensor system then detects the reflected radar signals
Implementation Method 3
Radar sensor systems are able to capture velocity information nearly instantaneously
Data Source
AI summary
A radar sensor system comprises a first radar sensor and at least a second radar sensor and one or more processors configured to perform acts comprising transmitting a first signal from a first transmit antenna in a first radar sensor and transmitting a second signal from a second transmit antenna in a second radar sensor. The acts further comprise detecting an object at the first radar sensor and the second radar sensor and estimating vector velocity information vx and vy for the object. The acts also comprise generating a radar measurement vector z that comprises position information px and py for the object and incorporating the vector velocity information vx and vy into the radar measurement vector z. Additionally, the acts comprise iteratively performing a measurement update using the measurement vector z, with velocity information incorporated therein, and a linear Kalman filter until correct velocity values are determined.