## 7 Discussion  
We performed a set of DVB-T passive radar measurements on the flight of a Pilatus PC-12 aircraft. We were able to locate the aircraft during its flight and estimate the received power. After correcting for range drop-off, we extracted a parameter proportional to the bistatic RCS of the target. The RCS of the aircraft was simulated along the same flight path, using both a MoM and an SBR+ solver to determine the expected RCS. A detailed investigation of the requirements for these simulations was performed, relating to both the solver type and configuration and to the way in which the simulations were set up. This investigation considered only a single flight of a single aircraft with a single transmitter and receiver, and so the results largely cannot be considered to be truly general. Nevertheless, there are a number of points that will apply in many cases, and they should be considered carefully in any future simulation attempts. In particular, we found that simulating only a single frequency instead of multiple frequencies across the measurement bandwidth introduced a mean error of only 0.29 dB, but saw clear evidence that in some circumstances it could lead to enormous errors in the estimated RCS. In one extreme case, we saw RCS variation of 31 dB within the frequency band with the SBR+ solver. A similar (but likely smaller) effect would also be observed with a MoM solver. Simulating at least a small number of frequency values across the measurement bandwidth will drastically reduce the error in the simulated RCS in such edge cases and is therefore strongly recommended. We also found that for our geometry, the bistatic RCS had a very strong dependence on the incidence angle, most notably on the elevation angle of the transmitter. To ensure that the fractional error in the RCS was less than 0.5 in 90% of cases it was necessary that the incidence elevation error be less than \(-0.015^{\circ}/+0.023^{\circ}\)--a very challenging level of precision to achieve. However, if only the mean fractional error is required to be less than 0.5, the incidence elevation error can be far larger: on the order 0.15\({}^{\circ}\) or greater. In addition, the median fractional error in the RCS is less than 0.5, out to even greater angular errors (beyond the range simulated). The incidence azimuth angle was found to be almost as important, but the receiver azimuth and elevation angles are less so. A high level of precision in the scenario geometry is therefore required for comparisons between simulation and measurement. If the aircraft's motion is large enough within the measurement integration period to lead to large incidence angular variation, then it would become necessary to perform simulations at several steps along the integration path. In our case, where the azimuth angle to the transmitter varied by 0.12\({}^{\circ}\) on average during each integration period, the 90 th percentile fractional error in the RCS was approximately \(\pm\)0.31, but in the general case, this will vary with integration time, distance to the transmitter/receiver and target velocity and should be estimated based on the expected scenario. Other factors that would affect the calculation of incidence angles were also considered. In terms of azimuthal angle, it was important that we work with an ellipsoidal earth--simply treating the Swiss grid coordinate system as Cartesian, was not sufficient (although this issue could also be fixed with the use of the meridian convergence, e.g., [40]). The size of the error will be very much dependent on the local deviation between true north and grid north, but in our case, it was nearly 1\({}^{\circ}\). For elevation angle at the ranges we considered (up to around 65 km), atmospheric effects must be taken into account: failure to do so in our case would result in an error on the order of 0.07\({}^{\circ}\) when a spherical earth model is assumed, and on the order of 0.2\({}^{\circ}\) with a Cartesian approximation.