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Human recognition with the optoelectronic reservoir-computing-based micro-Doppler radar signal processing
Zhejiang University, China.
Zhejiang University, China.
Zhejiang University, Hangzhou, China.
Zhejiang University, China.
Vise andre og tillknytning
2022 (engelsk)Inngår i: Applied Optics, ISSN 1559-128X, E-ISSN 2155-3165, Vol. 61, nr 19, s. 5782-5789Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Current perception and monitoring systems, such as human recognition, are affected by several environmental factors, such as limited light intensity, weather changes, occlusion of targets, and public privacy. Human recognition using radar signals is a promising direction to overcome these defects; however, the low signal-to-noise ratio of radar signals still makes this task challenging. Therefore, it is necessary to use suitable tools that can efficiently deal with radar signals to identify targets. Reservoir computing (RC) is an efficient machine learning scheme that is easy to train and demonstrates excellent performance in processing complex time-series signals. The RC hardware implementation structure based on nonlinear nodes and delay feedback loops endows it with the potential for real-time fast signal processing. In this paper, we numerically study the performance of the optoelectronic RC composed of optical and electrical components in the task of human recognition with noisy micro-Doppler radar signals. A single-loop optoelectronic RC is employed to verify the application of RC in this field, and a parallel dual-loop optoelectronic RC scheme with a dual-polarization Mach–Zehnder modulator (DPol-MZM) is also used for performance comparison. The result is verified to be comparable with other machine learning tools, which demonstrates the ability of the optoelectronic RC in capturing gait information and dealing with noisy radar signals; it also indicates that optoelectronic RC is a powerful tool in the field of human target recognition based on micro-Doppler radar signals. 

sted, utgiver, år, opplag, sider
Optica Publishing Group (formerly OSA) , 2022. Vol. 61, nr 19, s. 5782-5789
Emneord [en]
Learning algorithms, Machine learning, Radar signal processing, Signal to noise ratio, Current monitoring, Current perception, Human recognition, Machine-learning, Micro-Doppler, Monitoring system, Perception systems, Performance, Radar signals, Reservoir Computing, Doppler radar
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Identifikatorer
URN: urn:nbn:se:ri:diva-59830DOI: 10.1364/AO.462299Scopus ID: 2-s2.0-85133401802OAI: oai:DiVA.org:ri-59830DiVA, id: diva2:1685504
Merknad

Funding details: 2020LC0AD01; Funding details: National Natural Science Foundation of China, NSFC, 62101483; Funding details: Vetenskapsrådet, VR, 2022-04798; Funding details: Natural Science Foundation of Zhejiang Province, ZJNSF, LQ21F010015; Funding details: National Key Research and Development Program of China, NKRDPC, 2020YFB1805700; Funding text 1: Funding. National Key Research and Development Program of China (2020YFB1805700); National Natural Science Foundation of China (62101483); Natural Science Foundation of Zhejiang Province (LQ21F010015); Zhejiang Lab (2020LC0AD01); Vetenskapsrådet (2022-04798).

Tilgjengelig fra: 2022-08-03 Laget: 2022-08-03 Sist oppdatert: 2024-03-04bibliografisk kontrollert

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