Vehicle Radar Interference Prognosis Using Neural Networks
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Solution Overview
Problem
Radar systems in vehicles often experience interference from other radar systems, which are difficult to reliably detect and eliminate.
Innovation Solution
A method utilizing a neural network, particularly a recurrent neural network (RNN) and optionally a convolutional neural network (CNN), to evaluate detection information from radar signals, enabling the detection and prognosis of interference by identifying recurring patterns and temporal correlations, and adjusting frequency ranges to minimize interference impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar systems are used to detect vehicle surroundings, then detection capability is improved, but interference from other radar systems occurs
Solution Approach 1:
The patent applies preliminary action by training a neural network in advance to recognize interference patterns. The system performs evaluation of incoming signals using this pre-trained neural network, enabling early detection of interferences before they significantly degrade detection performance. This allows the radar system to proactively identify and potentially mitigate interference effects.
2Reliability
If neural network evaluation is used to detect interference, then detection reliability is improved, but processing complexity increases
Solution Approach 1:
The patent employs copying by using a neural network that has been trained offline on copies or representative samples of interference patterns. During actual operation, the pre-trained neural network evaluates incoming signals without requiring real-time retraining or complex adaptive algorithms, thus maintaining high detection reliability while reducing real-time processing complexity.
3Object-affected harmful factors
If frequency range adjustment is performed to reduce interference, then interference impact is reduced, but detection accuracy may be affected
Solution Approach 1:
The patent implements feedback by using the neural network's evaluation results to dynamically adjust the radar system's frequency range. The system continuously monitors incoming signals, identifies interference through the neural network, and adjusts the frequency range accordingly. This closed-loop approach allows the system to reduce interference impact while maintaining detection accuracy by adapting to changing environmental conditions.
Data Source
AI summary
A method for identifying interference in a radar system of a vehicle, wherein the following steps are carried out: receiving at least one incoming signal of the radar system; determining detection information from the incoming signal; performing an evaluation of the detection information by at least one neural network; and using a result of the evaluation as a prognosis of interference with the incoming signal.