General spiking neural network framework for the learning trajectory from a noisy mmWave radar

doi:10.1088/2634-4386/ac889b

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	General spiking neural network framework for the learning trajectory from a noisy mmWave radar
	Liu,Xin 4,5; Yan,Mingyu 4,5; Deng,Lei 3; Wu,Yujie 2; Han,De 4,5; Li,Guoqi 1,4; Ye,Xiaochun 4,5; Fan,Dongrui 4,5
刊名	Neuromorphic Computing and Engineering
	2022-09-01
卷号	2 期号:3
关键词	millimeter wave radar spiking neural networks attention mechanism model robustness trajectory estimation
DOI	10.1088/2634-4386/ac889b
通讯作者	Yan,Mingyu() ; Deng,Lei()
英文摘要	Abstract Emerging usages for millimeter wave (mmWave) radar have drawn extensive attention and inspired the exploration of learning mmWave radar data. To be effective, instead of using conventional approaches, recent works have employed modern neural network models to process mmWave radar data. However, due to some inevitable obstacles, e.g., noise and sparsity issues in data, the existing approaches are generally customized for specific scenarios. In this paper, we propose a general neuromorphic framework, termed mm-SNN, to process mmWave radar data with spiking neural networks (SNNs), leveraging the intrinsic advantages of SNNs in processing noisy and sparse data. Specifically, we first present the overall design of mm-SNN, which is adaptive and easily expanded for multi-sensor systems. Second, we introduce general and straightforward attention-based improvements into the mm-SNN to enhance the data representation, helping promote performance. Moreover, we conduct explorative experiments to certify the robustness and effectiveness of the mm-SNN. To the best of our knowledge, mm-SNN is the first SNN-based framework that processes mmWave radar data without using extra modules to alleviate the noise and sparsity issues, and at the same time, achieve considerable performance in the task of trajectory estimation.
语种	英语
出版者	IOP Publishing
WOS记录号	IOP:NCE_2_3_034013
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/51807]
专题	数字内容技术与服务研究中心_听觉模型与认知计算
通讯作者	Yan,Mingyu; Deng,Lei
作者单位	1.Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China 2.Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria 3.Department of Precision Instrument, Center for Brain Inspired Computing Research (CBICR), Tsinghua University, Beijing, People’s Republic of China 4.University of Chinese Academy of Sciences, Beijing, People’s Republic of China 5.SKLP, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People’s Republic of China
推荐引用方式 GB/T 7714	Liu,Xin,Yan,Mingyu,Deng,Lei,et al. General spiking neural network framework for the learning trajectory from a noisy mmWave radar[J]. Neuromorphic Computing and Engineering,2022,2(3).
APA	Liu,Xin.,Yan,Mingyu.,Deng,Lei.,Wu,Yujie.,Han,De.,...&Fan,Dongrui.(2022).General spiking neural network framework for the learning trajectory from a noisy mmWave radar.Neuromorphic Computing and Engineering,2(3).
MLA	Liu,Xin,et al."General spiking neural network framework for the learning trajectory from a noisy mmWave radar".Neuromorphic Computing and Engineering 2.3(2022).