# Interference Monitoring from Low Earth Orbit: The OPS-SAT Experiment +
## 5 Experimentation Results  
The OPS-SAT experiment scenario introduces several challenges into the localization task. The uncertainties and errors are not limited to those generated by signal processing but also those that directly affect the geometry. Figure 6 summarizes such a localization scenario, where the following issues are identified:  
* Only four chunks (block 1-4 in Figure 6) of data could have been collected (as explained in Section 2.1), lasting 3 s each. This sparse data collection jeopardizes the observability of the whole Doppler dynamic (orange curve).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Involved Windows** & **Estimated SNR on Involved Windows** & **Effect. InputSNR** & **Effect. Output SNR** \\ \hline Wind. 2/Wind. 1 & \(-\)3.3 dB (W2), \(-\)5.8 dB (W1) & \(-\)8.5 dB & 14.5 dB \\ Wind. 2/Wind. 3 & \(-\)3.3 dB (W2), \(-\)7.2 dB (W3) & \(-\)9.7 dB & 13.3 dB \\ Wind. 2/Wind. 4 & \(-\)3.3 dB (W2), \(-\)6.5 dB (W4) & \(-\)9.1 dB & 13.9 dB \\ \hline \end{tabular}
\end{table}
Table 1: Effective input and output SNR for each CAF computation.  
* Coarse estimation of FOA measurement (green samples) has shown huge biases with respect to the expected Doppler (cyan samples), making FDOA a more robust but potentially less accurate approach.
* The low SNR furthermore reduces the number of usable measurements, requiring some non-coherent combination, leading to 10 FDOA observables for the acquisition window (red samples).
* The failure of the on-board GNSS receiver does not allow the correction of the measurements clock offset rate as per standard FDOA approach [3]. The same GNSS failure introduces uncertainty regarding the OPS-SAT satellite orbit propagation \(\mathbf{r}_{r}(t_{k}),\mathbf{v}_{r}(t_{k})\) only low accuracy TLE files can be used for the purpose and discrepancies in the measurement time stamps affect the synchronization with the spacecraft positioning.  
All those effects shall be considered as worst-case condition for a fine geolocation of the emitter, but they do not make it impossible to provide at least a coarse localization. This is demonstrated by comparing the expected FDOA observations, derived from the known geometry between the transmitter and the satellite, to the raw FDOA extracted from real data processing using CAF. The comparison is presented in the left box-plot of Figure 6, where the estimated and measured values are represented by the red and blue stepwise curves, respectively.  
The final result for the emitter localization over the Norwegian test area is provided in Figure 7. The estimated emitter position is indicated by the cyan circle, while the actual target emission location in Andoya is marked with a large white cross. The solution exhibits an error of approximately 40 km in latitude and 70 km in longitude.  
Figure 7 also shows the localization cost function in terms of orange contour lines over the latitude-longitude region of interest (ROI) \(\lambda,\varphi\in[64^{\circ},73^{\circ}]\times[5^{\circ},25^{\circ}]\). As expected, the error distribution in latitude and longitude reflects the sensing geometry experienced along the satellite's ground track (indicated by the green line).  
Figure 6: OPS-SAT geometry limitations.  
The precision is clearly limited by the unknown residual biases that cannot be calibrated and the very high measurement noise (stepwise plot in Figure 6). However, the result confirms the capability to provide coarse detection and localization of the jamming area with limited hardware resources. This is highlighted by the error ellipsoid evaluated in the target solution point from the correspondent 2D latitude and longitude covariance. The ellipsoid is quite large considering the experienced high uncertainty level, but it provides consistent bounds, including the true emission site.