Browsing by Author "Wang, Yuhao"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    A Novel Feature-Level Data Fusion Method for Indoor Autonomous Localization
    (2013-07-14) Liu, Minxiang; Wang, Yuhao; Leung, Henry; Yu, Jiangnan
    We present a novel feature-level data fusion method for autonomous localization in an inactive multiple reference unknown indoor environment. Since monocular sensors cannot provide the depth information directly, the proposed method incorporates the edge information of images from a camera with homologous depth information received from an infrared sensor. Real-time experimental results demonstrate that the accuracies of position and orientation are greatly improved by using the proposed fusion method in an unknown complex indoor environment. Compared to monocular localization, the proposed method is found to have up to 70 percent improvement in accuracy.
  • Thumbnail Image
    ItemOpen Access
    Sparse Representation Based Range-Doppler Processing for Integrated OFDM Radar-Communication Networks
    (2017-04-20) Kong, Bo; Wang, Yuhao; Leung, Henry; Deng, Xiaohua; Zhou, Huilin; Zhou, Fuhui
    In an integrated radar-communication network, multiuser access techniques with minimal performance degradation and without range-Doppler ambiguities are required, especially in a dense user environment. In this paper, a multiuser access scheme with random subcarrier allocation mechanism is proposed for orthogonal frequency division multiplexing (OFDM) based integrated radar-communication networks. The expression of modulation Symbol-Domain method combined with sparse representation (SR) for range-Doppler estimation is introduced and a parallel reconstruction algorithm is employed. The radar target detection performance is improved with less spectrum occupation. Additionally, a Doppler frequency detector is exploited to decrease the computational complexity. Numerical simulations show that the proposed method outperforms the traditional modulation Symbol-Domain method under ideal and realistic nonideal scenarios.