Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

Abstract

The development of advanced autonomous driving applications is hindered by the complex temporal structure of sensory data, as well as by the limited computational and energy resources of their on-board systems. Currently, neuromorphic engineering is a rapidly growing field that aims to design information processing systems similar to the human brain by leveraging novel algorithms based on spiking neural networks (SNNs). These systems are well-suited to recognize temporal patterns in data while maintaining a low energy consumption and offering highly parallel architectures for fast computation. However, the lack of effective algorithms for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem of radar signal processing by introducing a novel SNN that substitutes the discrete Fourier transform and constant false-alarm rate algorithm for raw radar data, where the weights and architecture of the SNN are derived from the original algorithms. We demonstrate that our proposed SNN can achieve competitive results compared to that of the original algorithms in simulated driving scenarios while retaining its spike-based nature.

Time-coded Spiking Fourier Transform in Neuromorphic Hardware

“…This study proposes an alternative and novel spike-based FT (S-FT), which is suitable for neuromorphic hardware. This study is a major extension of the work presented in [23]. The main novel aspects are listed below:…”

“…We exploited this property for reproducing the linear combinations of an FT that operates in more than one dimension, which is explained in more detail in [23].…”

A review of non-cognitive applications for neuromorphic computing

“…Grzesiack and Meganck, have developed a mathematical formulation for carrying out spike signal processing and employ it in control systems and linear dynamical systems [89]. Some of the core signal processing algorithms such as Fourier transforms, have been demonstrated with SNN, and have been employed as a pre-processing step in object detection applications [92]. Orchard et al, have demonstrated performing equivalent of short time Fourier transforms, with resonate and fire (RF) neuron models, where different output neurons become active for different frequency components in the input signal [93].…”

“…Few works have also explored spike signal processing from the point of view of novel materials' research, e.g., Ganguly et al, have explored the use of a stochastic analog neuron based on spintronic materials in signal processing tasks such as channel equalization [95]. Neuromorphic approaches are also increasingly being used in assisted and autonomous driving applications to pre-process signals acquired from RADAR and LiDAR sensors [92,[96][97][98]. A great opportunity exists to take inspiration from the signal processing mechanisms in biology such as echolocation to design neuromorphic systems that are highly performant and accurate.…”

High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images

“…Synthetic aperture radar (SAR) is a high-resolution microwave imaging and detection system (Xie et al, 2016(Xie et al, , 2017(Xie et al, , 2020Zhang et al, 2022), which has the advantage of all-weather, all-day, and harsh environment work; thus, it can observe the land and ocean in real time for a long time (López-Randulfe et al, 2021;Yu et al, 2022). In recent years, related technology research on ship identification using SAR images has received great attention in the field of marine remote sensing (Lang et al, 2022;Wang et al, 2022).…”