Ping Feng

Ping Feng


Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 February 2021 in Sensors
Verified
Journal Article
Open access
PDF file
XWXin Wang
Xin Wang
BLBo Li
Bo Li
ZFZhao Fan
Zhao Fan
XLXinyu Luo
Xinyu Luo
LHLuxi Huang
Luxi Huang
XLXiaohui Li
Xiaohui Li
Due to the occlusion of the moon, an annular solar eclipse will have an effect on the ionosphere above the earth. The change of the ionosphere, for the low-frequency time-code signal that relies on it as a reflection medium for long-distance propagation, the signal field strength, and other parameters will also produce corresponding changes, which will affect the normal operation of the low-frequency time-code time service system. This paper selects the solar eclipse that occurred in China on 21 June 2020, and uses the existing measurement equipment to carry out experimental research on the low-frequency time-code signal. We measured and analyzed the signal field strength from 20 June 2020 to 23 June 2020, and combined solar activity data, ionospheric data, and geomagnetic data, and attempted to explore the reasons and rules of the change of signal parameters. The results showed that the field strength of the low-frequency time-code signal changed dramatically within a short time period, the max growth value can reach up to 17 dBμV/m and the variation trend yielded ‘three mutations’. This change in signal field strength is probably due to the occurrence of a solar eclipse that has an effect on the ionosphere. When the signal propagation conditions change, the signal strength will also change accordingly.

16 August 2024 in Remote Sensing
Verified
Journal Article
Open access
PDF file
LCLanglang Cheng
Langlang Cheng
SZShougang Zhang
Shougang Zhang
ZQZhen Qi
Zhen Qi
XWXin Wang
Xin Wang
YCYingming Chen
Yingming Chen
PFPing Feng
Ping Feng
As the eLoran signal propagates, its strength gradually diminishes with increasing distance, making subsequent signal capture and terminal development challenging. To address this phenomenon, this paper proposes an improved method based on wavelet hard thresholding. This method applies hierarchical processing to the coefficients obtained after wavelet decomposition, based on the signal’s center frequency. It effectively addresses issues like the disappearance of trailing edges and the presence of the noise with large coefficients. Simulation results show that the improved method has the largest output signal-to-noise ratio and effectively improves the problem of tailing vanishing and eliminates the noise with large coefficients. In analog source signal testing, the results show that the method can extract signals of 30 dBμv/m and above well. In actual signal testing, the improved method can extract eLoran signals transmitted over a distance of approximately 1000 km. Based on the results, it can be deduced that the input signal-to-noise ratio is −28.8 dB. Therefore, this method is a suitable and effective solution for extracting weak eLoran signals, providing strong support for signal monitoring in areas at the coverage boundaries of eLoran signals.

26 November 2024 in Atmosphere
Verified
Journal Article
Open access
PDF file
ZFZhao Fan
Zhao Fan
PFPing Feng
Ping Feng
ZQZhen Qi
Zhen Qi
LCLanglang Cheng
Langlang Cheng
XWXin Wang
Xin Wang
YHYu Hua
Yu Hua
Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky waves propagate over long distances by reflecting off the ionosphere. This paper utilizes the raw observation data received by the Low-Frequency Time Code dissemination monitoring stations before and after the 6.8 magnitude earthquake in Luding, Sichuan, China on 5 September 2022. A Low-Frequency Time Code time service monitoring system was built in Xi’an to continuously monitor the 68.5 kHz time signal broadcast by the BPC station. The data was then processed and analyzed through visualization. Simultaneously, we analyzed the signal fluctuation for multiple days before and after the earthquake to see the changes in the Low-Frequency Time Code signal during the earthquake. By combining seismic activity, solar activity, and geomagnetic data, this study aims to explore the causes and patterns of signal parameter variations. The results show that the field strength of the Low-Frequency Time Code signal fluctuated significantly within a short period during the earthquake. The value began to decrease about 60 min before the earthquake, dropping by approximately 8.9 dBμV/m, and gradually recovered 2 h after the earthquake. The phase also mutated by 1.36 μs at the time of the earthquake, and the time deviation fluctuated greatly compared to the 2 days before and after. Earthquake occurrences influence ionospheric variations, leading to changes in the sky wave propagation of Low-Frequency Time Code signals. Analysis of the influence of earthquakes on the propagation of Low-Frequency Time Code signals can provide references for research on Low-Frequency Time Code signal propagation models and earthquake prediction.

28 December 2021 in Automatic Control and Computer Sciences
Verified
Journal Article
Closed access
PDF file
XWXin Wang
Xin Wang
LHLuxi Huang
Luxi Huang
SLShuanglin Li
Shuanglin Li
YCYingming Chen
Yingming Chen
FZFan Zhao
Fan Zhao
XLXiaohui Li
Xiaohui Li
Forest environment is a common complex scene in the application of radio time service. When the timing signal enters the fo rest, signal strength atte

31 January 2025 in Atmosphere
Verified
Journal Article
Open access
PDF file
LHLuxi Huang
Luxi Huang
ZQZhen Qi
Zhen Qi
SSShaohua Shi
Shaohua Shi
YCYingming Chen
Yingming Chen
ZFZhao Fan
Zhao Fan
PFPing Feng
Ping Feng
This work investigates the impact of C-class solar flare events (XRA) on ionospheric and low-frequency signal propagation by analyzing the maximum correlation lag time, correlation, and Granger causality between low-frequency time-code signal strength and XRA. The results show that within the lag interval of (−5, 5) minutes, XRA exhibits the highest correlation and causality with signal strength, supporting the theory of a rapid ionospheric response and early warning to XRA. The correlation coefficient increases significantly with flare intensity, indicating that XRA has both linear and nonlinear dual effects on the disturbance of low-frequency signal propagation paths. Granger causality tests further confirm that XRA events have an immediate and sustained direct impact on signal strength. These findings provide a basis for understanding the disturbance mechanism of solar activity on the Earth’s ionosphere and support the use of low-frequency signals in space weather forecasting.

25 October 2020 in Sensors
Verified
Journal Article
Open access
PDF file
FZFeng Zhu
Feng Zhu
HZHuijun ZHANG
Huijun ZHANG
LHLuxi Huang
Luxi Huang
XLXiaohui Li
Xiaohui Li
PFPing Feng
Ping Feng
The receiver delay has a significant impact on global navigation satellite system (GNSS) time measurement. This article comprehensively analyzes the difficulty, composition, principle, and calculation of GNSS receiver delay. A universal method, based on clock-steering characterization, is proposed to absolutely calibrate all types of receivers. We use a hardware simulator to design several experiments to test the performance of GNSS receiver delay for different receiver types, radio frequency (RF) signals, operation status and time-to-phase (TtP). At first, through the receivers of Novatel and Septentrio, the channel delay of Septentrio is 2 ns far lower than 65 ns for Novatel, and for the inter-frequency bias of GLONASS L1, Septentrio tends to increase within 10 ns compared with decreasing of Novatel within 5 ns. Secondly, a representative receiver of UniNav-BDS (BeiDou) is chosen to test the influence of Ttp which may be ignored by users. Under continuous operation, the receiver delay shows a monotone reduction of 10 ns as TtP increased by 10 ns. However, under on-off operation, the receiver delay represents periodic variation. Through a zero-baseline comparison, we verifies the relation between receiver delay and TtP. At last, the article analyzes instrument errors and measurement errors in the experiment, and the combined uncertainty of absolute calibration is calculated with 1.36 ns.