Ion method, which can distinguish the gross errors and phase jumps
Ion technique, which can distinguish the gross errors and phase jumps properly. Just after preprocessing, the amplitude spectrums based on different FFT lengths are compared, and also the limitation of FFT in analyzing the time-varying periodic noise is discussed. To completely analyze the periodic variations, the QPM fitting residuals within the time domain, the spectrum analysis results in frequency domain, plus the time-frequency evaluation outcomes in time and frequency domain of 3 constellation satellites are given. By time-frequency evaluation of STFT, the periodic variations of BDS satellite clock offsets are characterized, plus the relationships involving the periodic variations and sun elevation angle above the orbit plane ( angle) are investigated. Immediately after that, the frequency variations of your major periodic term are analyzed in detail. Ultimately, the clock prediction model is modified by taking into account the periodic variations of clock offsets, plus the TFAM is proposed. 2.1. Preprocessing of Clock Offsets The preprocessing of raw clock offsets is of good value for periodic variation evaluation and clock prediction. The gross errors and phase jumps will be the most important anomalies with the clock offsets, which cannot objectively reflect the qualities from the satellite clocks and degrade the overall performance of clock prediction [33]. Such clock anomalies need to be processed just before periodic variation analysis and clock offset modeling. With higher calculation efficiency and anti-error functionality, the MAD method is usually made use of to detect gross errors [34]. When the clock frequency series satisfies Equation (1), the connected clock offsets are regarded abnormal:| yi | k(1)exactly where yi represents the clock frequency series, i is the epoch, and k will be the threshold worth, which can be calculated by k = m + n MAD. m denotes the median in the clock frequency series, MAD = Median, as well as the issue n can be set to three. The regular MAD system is a preprocessing method corresponding to clock frequency series. Both gross errors and phase jumps of clock offsets in time domain will bring outliers in clock frequency series, which means the outliers detected by conventional MAD are brought on by gross errors or phase jumps. If all of the outliers are processed with no distinguishing them, some helpful clock offsets may perhaps be destroyed simultaneously. As a result, it really is essential to identify the outliers triggered by gross errors or phase jumps. In this paper, we use double MAD detection to distinguish the outliers. The PK 11195 manufacturer detailed flowchart on the double MAD detection is shown in Figure 1. As shown in Figure 1, the double MAD detection performs two MAD detection. In the initial MAD detection, the raw clock offsets are converted to clock frequency series, along with the threshold worth k is calculated. The outliers of clock frequency series might be effortlessly detected by MAD detection. Then, store the clock offset outliers identified by the first MAD detection temporarily and eliminate the frequency outliers. Following that, the clock offset series without the need of gross errors are Pinacidil supplier recovered by integral algorithm. Inside the second MAD detection, the new clock frequency series are recalculated, though the threshold k nonetheless uses the worth obtained by the first MAD detection. In this case, the outliers of clock frequency series absolutely correspond to the phase jumps, as well as the clock offsets, which are accidentally removed for the duration of the very first MAD detection, may be recovered. After double MAD detection, the outliers caus.