Publications

2025

1. 

   Nurf: Nudging the particle filter in radiance fields for robot visual localization

   Wugang Meng, Tianfu Wu, Huan Yin, and 1 more author

   IEEE Transactions on Cognitive and Developmental Systems, 2025

   HTML

2024

1. 

   DCAR Subarray Location Method in Complex Environment Based on ISGMD-TSELM

   Zhaolin Zhang, Yinan Zhao, Ming Jin, and 3 more authors

   IEEE Transactions on Vehicular Technology, 2024

   HTML

2. 

   A Hybrid Controller Design for Human-Assistive Piloting of an Underactuated Blimp

   Wugang Meng, Tianfu Wu, Qiuyang Tao, and 1 more author

   In 2024 36th Chinese Control and Decision Conference (CCDC), 2024

   HTML

3. 

   Identification of Time Delay in Pointing Tasks for Human-Blimp Interactions

   Tianfu Wu, Wugang Meng, Sungjin Cho, and 1 more author

   In 2024 9th International Conference on Automation, Control and Robotics Engineering (CACRE), 2024

   HTML

4. 

   Application of Symplectic Geometry Mode Decomposition Based on Gaussian Process Space Angle in DOA Estimation

   Zhaolin Zhang, Yinan Zhao, Wugang Meng, and 2 more authors

   IEEE Transactions on Instrumentation and Measurement, 2024

   HTML

2023

1. 

   Multi-domain Extended ELM-DOA Subarray Localization Method Based on Distributed Coherent Aperture Radar

   Zhaolin Zhang, Wugang Meng, Mingqi Song, and 4 more authors

   IEEE Transactions on Vehicular Technology, 2023

   Abs HTML

   In distributed coherent aperture radar (DCAR), the fundamental problem in ensuring the full coherence of the radar system is how to realize the precise positioning of the subarray. The subarray positioning scheme is designed based on the multi-domain extended extreme learning machine (ELM)-direction of arrival (DOA) estimation for the mobile DCAR system. The multi-domain partition scheme improves the ELM model, making the algorithm effective and low-complex in the changing electromagnetic environment. Firstly, the array covariance matrix is constrained by L2 regularization to decrease the influence of different feature measurement scales on the model. An airspace extension ELM (AEELM) method, which reduces the original matrix by the spatial partition layer and the sequential process, is proposed. This method diminishes the size of the model’s solving matrix and enhances the DOA estimation accuracy while increasing or refining the samples. Secondly, an extended ELM method based on partition projection (PPEELM) is proposed to unravel the problems of high dimensions, small samples, and complex electromagnetic environments in radar array features. The radar covariance matrix is analyzed by partitioned multi-space projection according to the changes in the scene’s electromagnetic environment. The operation can ameliorate the model’s ability to solve nonlinear problems in complex environments. Therefore, the fully coherent system’s position estimation accuracy and signal-to-noise ratio (SNR) gain are ameliorated. Numerical simulations show that AEELM can effectively augment the estimation accuracy and curtail the maximum matrix dimension of a single training. The PPEELM method can ensure the system coherence performance is more excellent than 95% when the subarray signal’s SNR is greater than 0 dB in small scenarios.

2. 

   Application of Symplectic Geometry Mode Decomposition Based on Gaussian Process Space Angle in DOA Estimation

   Zhaolin Zhang, Yinan Zhao, Wugang Meng, and 2 more authors

   IEEE Transactions on Instrumentation and Measurement, 2023

   Abs HTML

   In the presence of noise interference, direction of arrival (DOA) estimation becomes challenging. Particularly, when radar signals are entirely submerged in noise, the spatial information within the signals becomes obscured, thereby affecting the precision of DOA estimation. To address the issue, a symplectic geometry mode decomposition (SGMD) method based on Gaussian process space angle (GPSA) has been introduced to mitigate the impact of noise on target DOA estimation. This method operates within non-constant curvature spaces, constructing subspaces for signals with different signal-to-noise ratios (SNRs). The subspace with the highest SNR is selected as the reference subspace. By quantifying the differences between the various signal subspaces and the reference subspace, a trajectory matrix that aligns with the dynamic characteristics of the signals is formed. Subsequently, the trajectory matrix is employed to reconstruct the signals in the symplectic space, preserving the spatial information while reducing the disturbance caused by noise components on DOA estimation. Simulation and field experiments demonstrate the effectiveness of this approach in diminishing noise components within received signals, enhancing the precision of Music and Capon algorithms for DOA estimation under low SNR conditions.

2022

1. 

   Application of multi-angle millimeter-wave radar detection in human motion behavior and micro-action recognition

   Zhaolin Zhang, Wugang Meng, Mingqi Song, and 4 more authors

   Measurement Science and Technology, Jul 2022

   Abs HTML

   Millimeter-wave radar is widely used in family safety, rehabilitation, and assisted living due to its ability to operate in all weathers and all day. To address the problem whereby the radar detection angle significantly impacts human behavior recognition, a recognition method based on multi-angle radar observation is adopted. We proposed a novel radar selection method called the energy domain ratio method to choose a radar with more sensitive features. Then, local tangent space alignment and an adaptive extreme learning machine (ELM) are presented to enhance the recognition rate of the model in a high-noise environment. A multi-angle entropy feature and an improved ELM are developed to identify human micro-motion in a low-noise indoor environment. The effect of observation distance on the recognition effect was also explored. The experimental results show that the proposed model has a more than 86% recognition rate for human behavior in outdoor scenes and a recognition accuracy of more than 98% for indoor micro-action.

2021

1. 

   Human Behavior Recognition Method Based on CEEMD-ES Radar Selection

   Zhaolin Zhang, Mingqi Song, Wugang Meng, and 4 more authors

   In 2021 CIE International Conference on Radar (Radar), Jul 2021

   HTML