# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
## 1 Introduction
Due to its high calculation speed and high identification rate, this method has been widely used in power system fault detection. However, this method has the same data length selection problem as the existing satellite telemetry data analysis methods. In the analysis of telemetry data, the fixed period obtained either by the expert experience method, or the periodogram method [16] is used as the basic analytical unit. Since the actual telemetry data are mostly non-stationary, truncation error will occur when using the fixed period analysis method. If each dynamic period is taken as the basic analytical unit, the error caused by the fixed period partition can be effectively avoided, thus providing a more effective guarantee for the accuracy of the subsequent analysis. The pseudo-period has been used to analyze the data stream, and the change management of the pseudo-periodic data stream has been achieved by linear regression and error segmentation [17]. In detail, pseudo-period analysis is mainly used in medical signal processing. [PERSON] [18] presented an algorithm to upsample an insufficiently sampled pseudo-periodic signal with the help of a reference signal \"hyper sampling\". Then, hyper sampling was applied to dynamic magnetic resonance imaging (MRI) data of the human brain; the results showed that the MRI data analysis accuracy was improved [18]. [PERSON] [19] used examples of bilinear and linear time-frequency distribution, and the Cohen class distribution and wavelet transform were discussed and used in the ballistocardiogram pseudo-period segment. The significance and importance of pseudo-periodic analysis were emphasized. [PERSON] [20] proposed a method of subsequence analysis of telemetry data based on the pseudo-period. To detect anomalous telemetry data, a detection sequence was dynamically generated by the subsequence segmentation algorithm based on the extreme values in the maximum period window width of the data and mean sequence. However, the pseudo-periodic partition is still based on the orbit periodic datum. In practice, the classification of different types of telemetry data incurs an extremely large computational cost and shows limited adaptability. In summary, the pseudo-periodic analysis of satellite telemetry data is still in the preliminary stage. Considering the requirement of fast detection and identification, this paper proposed a novel method of detection and identification of satellite anomalous telemetry data based on the pseudo-period. The similar shape of telemetry data was taken as the basic analytical unit for dividing telemetry data in order to extract the pseudo-period of the data. Then, the phase-plane trajectory characteristics of the pseudo-periodic data were extracted. Through the comparison and analysis with the sample data, the anomaly detection and identification in the pseudo-periodic data were achieved. It is worth noting that when the proposed method was used to process different types of data, the specific parameters should be set separately. The main contributions of this work are given below:  
* A method to extract the salient points of the data was proposed to compress the raw satellite telemetry data. * The tilt angle of the adjacent data points was symbolized in the compressed data in order to obtain the corresponding string. Then, the pseudo-periods of the measured data were extracted using the standard pseudo-periodic sequence string. * A fast method based on phase-plane trajectory plots was proposed to identify abnormal satellite telemetry data. In detail, the pseudo-period was taken as the basic analytical unit, and then the phase-plane trajectory plots corresponding to each pseudo-period data were taken as the features for achieving anomaly detection and identification in the satellite telemetry data.