# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
## 1 Introduction
Above all, we conduct dynamic orbit determination, which estimates LEO initial conditions and dynamic parameters with real time precise GNSS orbit and clock products. Then, these dynamic parameters and initial conditions are used to do the orbit integration for times in the future, referred as orbit prediction. Here, two issues should be taken into consideration. The first one is the update rate of the precise LEO orbit. The update rate of the LEO orbit depends on the time gap of LEO onboard observation data collection as well as the calculation time of LEO POD. The LEO onboard observations cannot be transferred to the ground stations all the time and the LEO orbit results calculated at an analysis center cannot be always uploaded to the LEO satellites unless the LEO satellites fly over the uplink and downlink stations. It is reasonable to assume that the onboard data can be collected every one hour (more than half LEO orbit revolution, personal communication with [PERSON], who is in charge of Chinese FY3C near real time POD). The second issue is the length of the orbit prediction. An empirical force model with parameters is usually utilized in dynamic LEO precise orbit determination to absorb the unmodeled part of non-gravitational forces, such as atmosphere drag and solar radiation pressure [21; 22; 23; 24]. These parameters are important for the precision of orbit prediction if they fit the real characteristics of the unmodeled part of non-gravitational forces for the prediction arc. Currently, some LEO satellites carry accelerometers to measure non-gravitational accelerations due to the surface forces acting on the LEO satellites, such as STAR instrument on Challenging Minisatellite Payload (CHAMP), SuperSTAR instruments on Gravity Recovery and Climate Experiment (GRACE), and GRADIO instruments on the Gravity field and steady-state Ocean Circulation Explorer (GOCE). These instruments have extreme sensitivity and unprecedented accuracy with up to \(10^{-9}\)m/s\({}^{2}\)/ \(\sqrt{\text{HZ}}\), \(10^{-10}\)m/s\({}^{2}\)/ \(\sqrt{\text{HZ}}\), and \(10^{-12}\)m/s\({}^{2}\)/ \(\sqrt{\text{HZ}}\) for STAR, SuperSTAR, and GRADIO,respectively. The measurement from the accelerometers has to be calibrated when processing the measurement from their missions to produce the gravity field models [25]. Researchers proposed a calibration method, which displays approximately constant scales and slowly changing biases for both GRACE A and B satellites [26]. Numerous acceleration spikes related to the switching activity in the circuits of onboard heaters are identified and analyzed [27]. It is a possibility to use the accelerometer data for LEO POD instead of empirical force models [28], [29]. The calibration parameters of the accelerometer such as scale and bias should be estimated together with the LEO initial position and velocity when using accelerometer data. Inspired by the stability of the calibration parameters of the accelerometer, we try to use accelerometer data to predict the LEO orbit instead of empirical force models. We compare two methods of accelerometer data calibrations with respect to the empirical force models in order to develop the potential LEO orbit prediction process strategy. The article is structured as follows. Different possible procedures of LEO orbit prediction are discussed in the next section. The corresponding methods of LEO orbit prediction are conducted as well as the analysis of calibration parameters of accelerometers in the following sections. Then the extensive LEO orbit prediction experiments are carried out, including the impact of different integration intervals on the LEO orbit precision. Research findings and concluding remarks are given in the following section. Finally, practical issues about widespread use of an accelerometer are discussed.