Radar Interference Reduction Training Data Generation
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Solution Overview
Problem
Vehicle radar systems face interference challenges when multiple systems operate in the same frequency band, leading to decreased accuracy in measuring the surrounding environment, particularly in densely populated areas with various radio frequency devices.
Innovation Solution
The development of techniques for autonomously generating large labeled reference datasets to simulate diverse interferer signals, using DRFM devices and machine learning models to differentiate between desired radar reflections and interference signals, allowing for improved interference detection and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle radar systems operate in the same frequency band, then communication efficiency and bandwidth utilization are improved, but interference from other emitters increases
Solution Approach 1:
The patent captures interfering signals from other emitters and uses them as training data to teach the radar system how to distinguish desired reflections from interference. The harmful interference is converted into a beneficial training resource that improves the system's ability to operate in crowded spectral environments.
Solution Approach 2:
A computing device acts as an intermediary between the radar system and the interference problem. It generates synthetic interferer signals, trains machine learning models, and provides interference mitigation capabilities to the radar system, enabling it to operate effectively despite the presence of other emitters.
2Reliability
If radar systems use higher power signals, then detection range and reliability are improved, but interference with other systems increases
Solution Approach 1:
The system continuously monitors for interferer signals in the environment and uses this feedback to adaptively adjust its operation. By detecting the presence and characteristics of other emitters, the radar can modify its signal parameters to reduce interference while maintaining detection reliability.
3Adaptability or versatility
If multiple radar systems operate simultaneously in dense environments, then system availability and coverage are improved, but measurement precision decreases
Solution Approach 1:
The system performs preliminary training using synthetic interferer signals before actual operation in dense environments. By pre-training machine learning models with diverse interferer scenarios, the radar system is prepared to accurately distinguish desired signals from interference when deployed in crowded spectral environments.
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 vehicle radar systems to efficiently and accurately adapt to dynamic environments by distinguishing between desired radar signals and interference, enhancing navigation and safety by reducing interference-related errors.
Implementation Method 1
Radio detection and ranging systems ('radar systems') are used to estimate distances to environmental features by emitting radio signals and detecting returning reflected signals
Implementation Method 2
detecting returning reflected signals. Distances to radio-reflective features in the environment can then be determined according to the time delay between transmission and reception
Implementation Method 3
Some radar systems may also estimate relative motion of reflective objects based on Doppler frequency shifts in the received reflected signals
Implementation Method 4
Directional antennas can be used for the transmission and/or reception of signals to associate each range estimate with a bearing. More generally, directional antennas can also be used to focus radiated energy on a given field of view of interest
Data Source
AI summary
Example embodiments relate to methods and systems for automated generation of radar interference reduction training data for autonomous vehicles. In an example, a computing device causes a radar unit to transmit radar signals in an environment of a vehicle. The computing device may include a model trained based on a labeled interferer dataset that represents interferer signals generated by an emitter located remote from the vehicle. The interferer signals are based on one or more radar signal parameter models. The computing device may use the model to determine whether received electromagnetic energy corresponds to transmitted radar signals or an interferer signal. Based on determining that the electromagnetic energy corresponds to the transmitted radar signals, the computing device may generate a representation of the environment of the vehicle using the electromagnetic energy.