Deep space exploration navigation requires high accuracy of the Doppler measurement, which is equivalent to a frequency estimation problem. Because of the fence effect and spectrum leakage, the frequency estimation performances, which is based on the FFT spectrum methods, are significantly affected by the signal frequency. In this paper, we propose a novel method that utilizes the mathematical relation of the three Chirp-Z Transform (CZT) coefficients around the peak spectral line. The realization, unbiased performance, and algorithm parameter setting rule of the proposed method are described and analyzed in detail. The Monte Carlo simulation results show that the proposed method has a better anti-noise and unbiased performance compared with some traditional estimator methods. Furthermore, the proposed method is utilized to process the raw data of MEX and Tianwen-1 satellites received by Chinese Deep Space Stations (CDSS). The results show that the Doppler estimation accuracy of MEX and Tianwen-1 are both about 3 millihertz (mHz) in 1-s integration, which is consistent with that of ESA/EVN/CDSN and a little better than that of the Chinese VLBI network (CVN). Generally, this proposed method can be effectively utilized to support Chinese future deep space navigation missions and radio science experiments.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9570964 | PMC |
http://dx.doi.org/10.3390/s22197364 | DOI Listing |
Phys Chem Chem Phys
January 2025
Physics Department, Khalifa University, Abu-Dhabi, United Arab Emirates.
The spectrum of carbon monoxide is important for astrophysical media, such as planetary atmospheres, interstellar space, exoplanetary and stellar atmospheres; it also important in plasma physics, laser physics and combustion. Interpreting its spectral signature requires a deep and thorough understanding of its absorption and emission properties. A new accurate spectroscopic model for the ground and electronically-excited states of the CO molecule computed at the aug-cc-pV5Z CASSCF/MRCI+Q level is reported.
View Article and Find Full Text PDFOver the past decade, there has been a global increase in the incidence of skin cancers. Skin cancer has serious consequences if left untreated, potentially leading to more advanced cancer stages. In recent years, deep learning based convolutional neural network have emerged as powerful tools for skin cancer detection.
View Article and Find Full Text PDFBioData Min
January 2025
Department of Computer Science, Hanyang University, Seoul, Republic of Korea.
Background: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Nephrology, Kanazawa Medical University, 1-1 Daigaku, Uchinada, 920-0293, Ishikawa, Japan.
To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients with and without diabetes. We also used visualizing techniques (gradient-weighted class activation mapping (Grad-CAM) and generative adversarial networks (GAN)) to identify the novel and early pathological changes on light microscopy in diabetic nephropathy.
View Article and Find Full Text PDFPhys Med Biol
January 2025
School of Software Engineering, Xi'an Jiaotong University, Xi 'an Jiaotong University Innovation Port, Xi 'an, Shaanxi Province, Xi'an, Shaanxi, 710049, CHINA.
Deformable registration aims to achieve nonlinear alignment of image space by estimating a dense displacement field. It is commonly used as a preprocessing step in clinical and image analysis applications, such as surgical planning, diagnostic assistance, and surgical navigation. We aim to overcome these challenges: Deep learning-based registration methods often struggle with complex displacements and lack effective interaction between global and local feature information.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!