# The Effect of Space Objects on Ionospheric Observations: Perspective of SYISR
## 3 Preliminary Attempts to Eliminate Space Object Pollution
In the high background, the ionosphere echo is relatively stronger than the normal environmental noise. The detection threshold increases to two times that in the low background. According to the detection threshold and the detection reference unit number used, the theoretical false alarm probability was about 2% in the high background and 10% in the low background. Furthermore, because the number of reference units was set to change with the sampling points of one pulse width, there were some differences when using different waveforms. As for the measurements from different azimuths and elevations, the range was changed to the altitude for the separation of the backgrounds. The method was applied in the same way as when the radar was pointing to the zenith. We performed a space object elimination test on both the LP (11 March 2022) and AC (14 April 2022) signals from the entire-day SYISR observations. Figure 3 shows the RTI before and after elimination, and their difference during the AC experiment. The error of the raw electron density before and after the removal of space objects is also presented. Obvious space objects are successfully deleted from the ionosphere observations while reserving the normal ionospheric echo. The neighboring data at the same range of different pulses in the time domain are used to fill in the lost signal polluted by the space objects. The data used to fill in the missing signal were measured within the pre-integrated time (usually a few seconds). Considering that the incoherent integration of the ionosphere echo was applied under the assumption of a stable ionosphere during the integration time, we regarded the data from the neighboring pulse within this time as the repeated measurements for the ionosphere. Then the data were used as a substitute for the ionosphere echo polluted by the space objects. To demonstrate the effect of the SO-CFAR algorithm for space object elimination on the subsequent data inversion, we compare one specific power profile and the ACF, and the retrieved electron density profile during the entire day of 14 April 2022 before and after space object elimination (Figure 4). As shown in the ACF results, the decoding of the AC signal improves the range resolution to a narrower width (about one baud length) in the ionosphere measurements. The disturbances of space objects were still found over a range extension after decoding. The reason for this was that the power of the space objects would change with time. The range extension of space objects was left over even after the decoding process was finished. When the least squares fitting was applied to obtain the ionosphere parameters from the measured spectrum (FFT from the ACF), space object pollution was present over a range extension in the fitted ionosphere parameters. When the detected space objects were removed from the raw IQ data, we can see that the effect of the space object was successfully eliminated in the power profile, the ACF profile, and the electron density profile. In total, 389 space objects were successfully identified and removed during the entire day. Figure 3: Range-time-intensity (RTI, unit: dB) of the ionosphere observations during 14 April 2022 using the alternating code (AC) signal before (**a**) and after (**b**) eliminating the space object pollution using the SO-CFAR algorithm. Panel (**c**) is the difference between (**a**) and (**b**), which indicates the identified space object occurrences. Panel (**d**) is the error of the raw electron density before and after the removal of space objects. This pollution elimination algorithm is suitable for obvious space objects. However, it has limitations, even when integration is employed. We use the difference level between space object echoes and the background signal to determine the space object pollution. When the difference in power is near the fluctuation level of the ionosphere itself, it is difficult to distinguish them.