BDS Satellite Clock Prediction Considering Periodic Variations

Abstract

The periodic noise exists in BeiDou navigation satellite system (BDS) clock offsets. As a commonly used satellite clock prediction model, the spectral analysis model (SAM) typically detects and identifies the periodic terms by the Fast Fourier transform (FFT) according to long-term clock offset series. The FFT makes an aggregate assessment in frequency domain but cannot characterize the periodic noise in a time domain. Due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying, and the spectral peaks vary over time, which will affect the prediction accuracy of the SAM. In this paper, we investigate the periodic noise and its variations present in BDS clock offsets, and improve the clock prediction model by considering the periodic variations. The periodic noise and its variations over time are analyzed and quantified by short time Fourier transform (STFT). The results show that both the amplitude and frequency of the main periodic term in BDS clock offsets vary with time. To minimize the impact of periodic variations on clock prediction, a time frequency analysis model (TFAM) based on STFT is constructed, in which the periodic term can be quantified and compensated accurately. The experiment results show that both the fitting and prediction accuracy of TFAM are better than SAM. Compared with SAM, the average improvement of the prediction accuracy using TFAM of the 6 h, 12 h, 18 h and 24 h is in the range of 6.4% to 10% for the GNSS Research Center of Wuhan University (WHU) clock offsets, and 11.1% to 14.4% for the Geo Forschungs Zentrum (GFZ) clock offsets. For the satellites C06, C14, and C32 with marked periodic variations, the prediction accuracy is improved by 26.7%, 16.2%, and 16.3% for WHU clock offsets, and 29.8%, 16.0%, 21.0%, and 9.0% of C06, C14, C28, and C32 for GFZ clock offsets.

Characterization of BDS Multipath Effect Based on AT-Conv-LSTM Network

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Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

“…where 𝑠(𝑡) is the jamming signal to be analyzed, 𝜔(𝑡) is the window function, and 𝑋(𝑓, 𝑡) is the Fourier transform of sub-signal 𝜔(𝑡 − 𝜏)𝑠(𝑡). After the radar active jamming signal is obtained, we convert it into a time-frequency image by short-time Fourier transform (STFT) and take the time-frequency image of active radar jamming as a dataset to provide a data basis for the subsequent jamming classification [26]. The operation process of STFT is shown in Figure 2.…”