Range and Angle Estimation with Spiking Neural Resonators for FMCW Radar

• URL: http://arxiv.org/abs/2503.00898v1
• Date: Sun, 02 Mar 2025 13:51:03 GMT
• Title: Range and Angle Estimation with Spiking Neural Resonators for FMCW Radar
• Authors: Nico Reeb, Javier Lopez-Randulfe, Robin Dietrich, Alois C. Knoll,
• Abstract summary: Automotive radar systems face the challenge of managing high sampling rates and large data bandwidth.<n>Neuromorphic computing offers promising solutions because of its inherent energy efficiency and parallel processing capacity.<n>This research presents a novel spiking neuron model for signal processing of frequency-modulated continuous wave (FMCW) radars.
• Score: 16.91912935835324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automotive radar systems face the challenge of managing high sampling rates and large data bandwidth while complying with stringent real-time and energy efficiency requirements. The growing complexity of autonomous vehicles further intensifies these requirements. Neuromorphic computing offers promising solutions because of its inherent energy efficiency and parallel processing capacity. This research presents a novel spiking neuron model for signal processing of frequency-modulated continuous wave (FMCW) radars that outperforms the state-of-the-art spectrum analysis algorithms in latency and data bandwidth. These spiking neural resonators are based on the resonate-and-fire neuron model and optimized to dynamically process raw radar data while simultaneously emitting an output in the form of spikes. We designed the first neuromorphic neural network consisting of these spiking neural resonators that estimates range and angle from FMCW radar data. We evaluated the range-angle maps on simulated datasets covering multiple scenarios and compared the results with a state-of-the-art pipeline for radar processing. The proposed neuron model significantly reduces the processing latency compared to traditional frequency analysis algorithms, such as the Fourier transformation (FT), which needs to sample and store entire data frames before processing. The evaluations demonstrate that these spiking neural resonators achieve state-of-the-art detection accuracy while emitting spikes simultaneously to processing and transmitting only 0.02 % of the data compared to a float-32 FT. The results showcase the potential for neuromorphic signal processing for FMCW radar systems and pave the way for designing neuromorphic radar sensors.

Related papers

• Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework [57.994965436344195]
  Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity. multimodal sensing-aided beam prediction has gained significant attention, using various sensing data to predict user locations or network conditions. Despite its promising potential, the adoption of multimodal sensing-aided beam prediction is hindered by high computational complexity, high costs, and limited datasets.
  arXiv  Detail & Related papers  (2025-04-07T15:38:25Z)
• SpINR: Neural Volumetric Reconstruction for FMCW Radars [0.15193212081459279]
  We introduce SpINR, a novel framework for volumetric reconstruction using Frequency-Modulated Continuous-Wave (FMCW) radar data. We demonstrate that SpINR significantly outperforms classical backprojection methods and existing learning-based approaches.
  arXiv  Detail & Related papers  (2025-03-30T04:44:57Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation [9.865041274657823]
  We propose a new method for training neural networks (NNs) for frequency modulated continuous wave (WFMC) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous work, we train NNs directly on object detection maps. We do so by performing a continuous relaxation of the cell-averaging constant false alarm rate (CA-CFAR) peak detector, which is a well-established algorithm for object detection using radar.
  arXiv  Detail & Related papers  (2023-12-15T13:47:16Z)
• Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition [14.18507067281377]
  This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided blockage Dynamic Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model.
  arXiv  Detail & Related papers  (2023-06-22T10:10:25Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement [0.0]
  Optical neural networks (ONNs) are promising accelerators with ultra-low latency and energy consumption. We introduce our multiplicative analog frequency transform ONN (MAFT-ONN) that encodes the data in the frequency domain. We experimentally demonstrate the first hardware accelerator that computes fully-analog deep learning on raw RF signals.
  arXiv  Detail & Related papers  (2022-07-08T16:37:13Z)
• Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks [5.199765487172328]
  We propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. We show that the proposed FF-CNN technique outperforms the current state-of-the-art alternatives.
  arXiv  Detail & Related papers  (2022-05-19T20:18:17Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks [76.830358429947]
  Impulse response estimation in high noise and in-the-wild settings is a challenging problem. We propose a novel framework for parameterizing and estimating impulse responses based on recent advances in neural representation learning.
  arXiv  Detail & Related papers  (2022-02-07T18:57:23Z)
• A SAR speckle filter based on Residual Convolutional Neural Networks [68.8204255655161]
  This work aims to present a novel method for filtering the speckle noise from Sentinel-1 data by applying Deep Learning (DL) algorithms, based on Convolutional Neural Networks (CNNs) The obtained results, if compared with the state of the art, show a clear improvement in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM)
  arXiv  Detail & Related papers  (2021-04-19T14:43:07Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Quantized Neural Networks for Radar Interference Mitigation [14.540226579203207]
  CNN-based approaches for denoising and interference mitigation yield promising results for radar processing. We investigate quantization techniques for CNN-based denoising and interference mitigation of radar signals.
  arXiv  Detail & Related papers  (2020-11-25T13:18:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.