This article concerns the issue of how to combine the multidomainradar information, including range–Doppler, time–Doppler, and time–range, for human activity recognition. Specifically, to fully make use of radar information, instead of using a single-domain spectrum as inputs, a novel hybrid neural network model is developed for exploring multidomain fusion of radar information. In doing this, three kinds of 2-D domain spectra are used in a fashion of supplementing each other with a hybrid framework that combines three models: 1-D convolution neural network, recurrent neural network, and 2-D convolution network. It is advantageous to use such a hybrid model to capture much rich features through multidomain feature fusion, so as to improve the accuracy of human activity recognition effectively. Experimental results validate the proposed method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Radar-Based Human Activity Recognition Using Hybrid Neural Network Model With Multidomain Fusion


    Contributors:
    Ding, Wen (author) / Guo, Xuemei (author) / Wang, Guoli (author)


    Publication date :

    2021-10-01


    Size :

    2082814 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Hybrid Control and Reservation Architecture for Multidomain Burst Switched Network

    Raza, M.A. / Mahmood, W. / Ali, A. | British Library Online Contents | 2008


    Human activity recognition for static and dynamic activity using convolutional neural network

    Minarno, Agus Eko / Kusuma, Wahyu Andhyka / Kurniawan, Yoga Anggi | BASE | 2021

    Free access

    Radar HRRP Target Recognition Based on Hybrid Quantum Neural Networks

    Liu, Xin / Zhou, Daiying / Huang, Qiuyan | IEEE | 2025