Abstract: Detecting stationary human targets in dense jungle holds significant importance for applications such as wilderness rescues, counterterrorism and stability operations, and military reconnaissances. However, there are still challenges regarding the low signal-to-clutter-noise ratio and the difficulty in distinguishing stationary human targets from diverse, time-varying and perturbed foliage clutter in the time-frequency domain. To improve detection performances, a detection algorithm for stationary human targets hidden in jungle environments based on micromotion order is proposed. The impact of horizontal and vertical polarization on the detection performances of obscured stationary human targets is also analyzed. Data from detecting stationary human targets hidden in two typical jungle environments are collected using a foliage-penetrating, ultra-wideband, multi-transmitter and multi-receiver radar under both horizontal and vertical polarization conditions, and experimental results show that the proposed algorithm achieves an accuracy rate of 83.48%, a false alarm rate of 13.94%, and a miss rate of 20.27% respectively in detecting stationary human targets hidden in jungle environments, all being superior to those of the traditional two-dimensional ordered statistical constant false alarm rate detection algorithm. When penetrating through shrubs and trees separately, the performances of using horizontal and vertical polarization scheme respectively in detecting human targets are better. The experimental validation confirms the effectiveness and feasibility of the proposed algorithm.