Radar Interference Mitigation via Neural Network Parameter Adjustment
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Solution Overview
Problem
Millimeter-wave radar systems face interference issues due to the coexistence of multiple RF sources in the same frequency range, leading to potential safety hazards such as ghost target detection and failure to detect real targets.
Innovation Solution
A method utilizing a neural network to dynamically adjust radar signal parameters based on a continuous reward function, maximizing the signal-to-interference-plus-noise ratio (SINR) by modifying waveform shape, chirp time, and other parameters to mitigate interference, employing reinforcement learning for autonomous decision-making in a decentralized multi-agent setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple radar systems operate in the same frequency range, then radar coverage and detection capability are improved, but interference between radars increases causing ghost target detection and failure to detect real targets
Solution Approach 1:
The patent applies dynamics by enabling radar systems to dynamically adjust their operating parameters (frequency, modulation index, chirp rate) in real-time based on detected interference levels. The reinforcement learning agent continuously adapts the radar signal characteristics to maximize SINR while maintaining detection coverage, transforming the static radar operation into a dynamic, interference-aware system that can shift parameters to avoid interference hotspots.
Solution Approach 2:
The patent implements parameter changes by modifying key radar signal parameters including carrier frequency, modulation index, and chirp rate according to the learned policy. The neural network outputs adjusted parameter values that optimize the radar performance in the presence of interference, allowing the system to change physical parameters of the transmitted signal to achieve better detection while minimizing interference with other radar systems.
2Device complexity
If radar signal parameters are fixed, then system simplicity is maintained, but interference mitigation capability is reduced
Solution Approach 1:
The patent applies self-service by implementing an autonomous reinforcement learning system that automatically learns optimal parameter adjustment strategies without human intervention. The radar system monitors its own performance through SINR metrics and autonomously adjusts parameters based on the learned policy, eliminating the need for complex manual control systems while achieving reliable interference mitigation through self-optimized operation.
Solution Approach 2:
The patent implements feedback by continuously monitoring the signal-to-interference-plus-noise ratio (SINR) and using this information to update the reinforcement learning agent's policy. The feedback loop closes when the agent receives reward signals based on detection performance and interference levels, allowing it to learn from actual system behavior and refine its parameter adjustment strategy over time, transforming fixed parameters into adaptive, feedback-driven controls.
3Use of energy by moving object
If traditional radar processing is used, then computational requirements are lower, but detection accuracy in high interference environments deteriorates
Solution Approach 1:
The patent applies mechanics substitution by replacing traditional deterministic signal processing algorithms with a machine learning-based approach. Instead of relying on fixed processing chains that may fail under interference, the system uses a neural network trained to recognize and filter interference patterns, substituting mechanical/computational processing with intelligent pattern recognition that adapts to complex interference environments and maintains high detection accuracy.
Data Source
AI summary
In an embodiment, a method for radar interference mitigation includes: transmitting a first plurality of radar signals having a first set of radar signal parameter values; receiving a first plurality of reflected radar signals; generating a radar image based on the first plurality of reflected radar signals; using a continuous reward function to generate a reward value based on the radar image; using a neural network to generate a second set of radar signal parameter values based on the reward value; and transmitting a second plurality of radar signals having the second set of radar signal parameter values.