Hengchao Guan
A5075980268- Speech and Audio Processing
- Direction-of-Arrival Estimation Techniques
- Particle physics theoretical and experimental studies
- Dark Matter and Cosmic Phenomena
- Neutrino Physics Research
- Indoor and Outdoor Localization Technologies
- Blind Source Separation Techniques
- Gaussian Processes and Bayesian Inference
Zhejiang Ocean University
2024
In this paper, an innovative cyclic noise reduction method and improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve accuracy reliability of DOA (direction arrival) estimation. By processing eigenvalues obtained from covariance matrix received signal, signal-to-noise ratio (SNR) can be increased by up 5 dB method, which will estimation accuracy. The has a convolution neural network (CNN) structure, whose input is...
We developed a detector signal characterization model based on Bayesian network trained the waveform attributes generated by dual-phase xenon time projection chamber. By performing inference model, we produced quantitative metric of and demonstrate that this can be used to determine whether is sourced from scintillation or an ionization process. describe method its performance electronic-recoil (ER) data taken during first science run XENONnT dark matter experiment. use in waveform-based...
Direction of Arrival (DoA) is an important concept in radio communications, and the MUSIC algorithm for solving DoA problems. Some studies have shown that reducing noise signal matrix can increase accuracy algorithm. This paper proposes a deep learning framework to enables this work. Simulations show proposed has superior estimation when SNR low or targets are close, RMSE estimated be reduced by up 30 degrees.