FMCW Radar Interference Suppression via Complex-Valued CNN
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Solution Overview
Problem
Radar sensors in vehicles face interference issues due to overlapping radar signals from nearby vehicles, which degrade their operational accuracy in applications like adaptive cruise control and autonomous driving.
Innovation Solution
A method using a convolutional neural network to filter out interfering signals from received radar signals by generating datasets from frequency-modulated chirps and processing them to reduce interference, enabling effective signal suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If radar sensors are deployed in multiple vehicles, then the coverage and functionality of radar systems are improved, but interference between radar signals from different vehicles increases
Solution Approach 1:
The patent applies neural network-based signal processing to convert the harmful interference from other radar sensors into usable information. The neural network is trained to distinguish between valid target echoes and interference signals, effectively transforming the harmful multi-sensor interference into a solvable pattern recognition problem that enhances overall system performance
Solution Approach 2:
The patent introduces neural network processing as an intermediary layer between the raw radar signals and the target detection algorithms. This intermediary processes the mixed signals containing both valid echoes and interference, separating and identifying genuine targets while filtering out interference from other radar systems
2Power
If radar signals are transmitted with higher power, then the detection range is improved, but the interference caused to other radar sensors increases
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously analyzes received signals, identifies interference patterns, and adjusts signal processing parameters accordingly. This feedback loop enables the system to maintain high transmission power for extended range while dynamically compensating for interference effects through adaptive signal processing
3Measurement precision
If neural network filtering is applied to reduce interference, then the measurement accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network offline with labeled data containing various interference scenarios. This pre-training establishes the network's ability to recognize interference patterns before actual radar operation, reducing the computational burden during real-time signal processing while maintaining high measurement accuracy
Data Source
AI summary
A radar device may include a radar transmitter to output a radio frequency (RF) transmission signal including a plurality of frequency-modulated chirps. The radar device may include a radar receiver to receive an RF radar signal, and generate, based on the RF radar signal, a dataset including a set of digital values, the dataset being associated with a chirp or a sequence of successive chirps. The radar device may include a neural network to filter the dataset to reduce an interfering signal included in the dataset, the neural network being a convolutional neural network. At least one layer of the neural network may be a complex-valued neural network layer includes complex-valued weighting factor, where the complex-valued neural network layer is configured to perform one or more operations according to a complex-valued computation.