Abstract: During the distribution of telemetry data from the satellite to telemetry stations，due to the influence of weather，magnetic field interference and other accidental factors，the observed data will seriously deviate from the target true value，which we call this part of data as outliers. The accuracy of telemetry data is very important. If there are outliers in telemetry data，it will directly affect the subsequent fault detection and prediction，resulting in abnormal misjudgment，and even false alarm，which will cause trouble for subsequent staff to confirm satellite status and deal with satellite abnormalities. Based on the traditional 53h method，this paper adds a three-point equal weight head and tail smoothing algorithm to avoid the loss of four data points caused by the traditional 53h method; The judgment method of speckle outliers is added，and the disadvantage of weak ability of 53h method to eliminate speckle outliers is improved; The secondary judgment of adjacent normal data of outlier data is added to avoid the wrong elimination of normal data due to the influence of outliers，so as to ensure the integrity of data. In this paper，the telemetry data of a navigation satellite is used for experiments. Under the same experimental conditions，the same simulation data are used to carry out comparative experiments with the Nair criterion and the least square method，and the characteristics of this algorithm and other outlier elimination algorithms are summarized. The experimental results show that the algorithm can eliminate the outlier data accurately and retain the abnormal data due to satellite faults and anomalies. The algorithm proposed in this paper is simple to implement and fast to eliminate. It can be well applied to the data preprocessing of satellite fault detection based on telemetry data.

Key words: 53H method, outlier elimination, telemetry data