Signal Processing and Speech Communication Laboratory
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Quantized Neural Networks for Radar Interference Mitigation

Tue, Dec 01, 2020

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous vehicles. Key performance factors are weather resistance and the possibility to directly measure velocity. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt with. Algorithms and models operating on radar data in early processing stages are required to run directly on specialized hardware, i.e. the radar sensor. This specialized hardware typically has strict resource-constraints, i.e. a low memory capacity and low computational power.

Convolutional Neural Network (CNN)-based approaches for denoising and interference mitigation yield promising results for radar processing in terms of performance. However, these models typically contain millions of parameters, stored in hundreds of megabytes of memory, and require additional memory during execution.

In this paper we investigate quantization techniques for CNN-based denoising and interference mitigation of radar signals. We analyze the quantization potential of different CNN-based model architectures and sizes by considering (i) quantized weights and (ii) piecewise constant activation functions, which results in reduced memory requirements for model storage and during the inference step respectively.

Figure: F1-Score and memory requirements for models with multiple bits per weight and activation. This plot shows, that it is possible to use quantization with multiple bits for weights and activations. 8 bit quantization reduces the memory requirements during the inference step by around 75 % without having a substantial impact on the performance.

The full version of this paper can be found on and there is a video presentation on