Deep factor analysis for weather varied sense-through-foliage target detection

EURASIP Journal on Wireless Communications and Networking

Xue Ding¹  Ting Jiang¹  Xuebin Sun¹  Wenling Xue²  Xiaokun Zheng² 

[1] Key Laboratory of Universal Wireless Communication, BUPT, Xitucheng Road 10, 100876, Beijing, China;Key Laboratory of Universal Wireless Communication, BUPT, Xitucheng Road 10, 100876, Beijing, China;The College of Electronic Information Engineering, Hebei University, Qiyi dong Road 2666, 071002, Baoding, Hebei Province, China;

关键词: Impulse-radio ultra-wideband (IR-UWB);    Deep factor analysis;    Target detection;   

DOI  :  10.1186/s13638-021-01921-7

来源: Springer

【 摘 要 】

In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis (DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in hazy and snowy days, which can be stable at about 93%.

【 授权许可】

CC BY