## 4 Discussion  
Four calibrating cases have been presented in this study, thereby establishing the trade-off between the successful detection of maneuvers and the calibrating parameters of the algorithm. Detection threshold and window length are two important tuning parameters. These two parameters must be set in a manner that increases the maneuver detection reliability while reducing the occurrences of false detection. For example, reducing the detection threshold can result in an increase in the number of false detections, whereas increasing the detection threshold can result in no maneuver detection. Similarly, a reduction in the window length parameter can result in an increase in the number of false detections, whereas increasing the window length can result in no maneuver detection.  
Figure 8: Baseline Case for TDRS-3. The red circles represent potential maneuvers detected by this method.  
Figure 9: TDRS-3’s difference between the propagated and the observed inclination. The horizontal green line is the detection limit; the horizontal green line represents the threshold limit and the red circles represent potential maneuvers detected by this method.  
Therefore, when the two parameters are selected to feed in the algorithm, the trade-off must be considered. The detection performance of the algorithm proposed in this work is relative to the data standard, number, and geometry of the maneuver. When these traits are identified clearly, the maneuver detection algorithm can be calibrated by choosing the suitable values for the two parameters, namely, detection threshold and window length, by comparing the difference between the propagated and the observed parameters. This study shows that by comparing the orbital elements obtained from TLE data and orbital elements propagated from SGP4, the maneuvers of a satellite can be predicted. The same technique can be applied to detect the maneuvers both in-plane and out-of-plane. Future work would include implementing the same method to three-satellite formation flying, which has been studied predominantly [48]. In the future the same algorithm can also be developed to detect the maneuvers in real-time by comparing the propagated Keplerian elements using SGP4 and observed Keplerian elements directly from the TLEs. Discriminating the low thrust maneuver from the other non-conservative perturbation effects could be difficult [49]. Therefore the future research should also include testing the same method with satellites in different orbits including LEOs with low thrust propulsion with different orbital parameters. This will help in detecting the maneuvers as soon as the TLEs are updated. The purpose and orbital parameters of each satellite vary and hence one set of parameters cannot be used to find the maneuvers of all the satellites. Therefore, every satellite requires different sets of threshold and window length. These two parameters must be chosen based on the spikes from the difference between the observed and the estimated parameters. For example, in Figure 9, there has been a spike every 30 days and on average the difference between the observed and the estimated orbital energy is around 4. Thus, these two values have been chosen for this case. Data preprocessing is crucial as it directly impacts the success rate of the project. Data are said to be unclean if they are missing attributes, attribute values, contain noise or outliers and duplicate or wrong data. The presence of any of these will degrade the quality of the results. However, in this study, except for the presence of a single outlier in TDRS-3, no other data-degrading factors were found and hence data preprocessing was not required. However, in the future for other case studies data preprocessing is a necessity.