# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
## 6 Experimental Results  
The experiments were carried out in May 2021 in YiXian, HeBei, China, with a receiver based on software-defined radio (SDR). Two identical parabolic antennas with a gain of 23.5 dBi in the frequency range of 1460 MHz to 1500 MHz were applied to the reference and surveillance channels, for which the 3 dB beamwidth is about 10\({}^{\circ}\). The geostationary DAB satellite, AsiaStar, was selected as the illuminator, the position and transmission parameters of which are listed in Table 4. The downlink channel with the best coverage, with a center frequency of 1469 MHz, was recorded at a sampling frequency of 3.125 MHz. A DJI's amateur quadcopter for aerial photography, Phantom 4, was used as the non-cooperative target. The observation geometry of the PBR system is shown in Figure 7, where an arrow from the reference antenna indicates the orientation towards the DAB satellite. Target echoes from three azimuths were collected to demonstrate the influence of the bistatic angle on the detection performance, and the corresponding surveillance beams are marked with different letters in Figure 7. The height of the UAV relative to the surveillance antenna was kept at 120 m during data acquisition, which lasted for 2 min for each beam. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Longitude & 105\({}^{\circ}\) \\ Inclination & 0.06\({}^{\circ}\) \\ Perigee altitude & 35,776 km \\ Apogee altitude & 35,811 km \\ EIRP & 53 dBw \\ Downlink bandwidth (single channel) & 1.84 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of AsiaStar. Figure 7: The geometry configuration of the experiments: The surveillance and reference antennas are marked in blue and red, respectively. The direction of AsiaStar is indicated with a red arrow. Three surveillance beams, denoted with “Beam a/b/c”, of different azimuths were used during the experiments. Both ECA-LF and ECA-LF-PC were implemented using batch-level parallelism to analyze the efficiency gain brought by precompression. The parameters were set to \(N/B=3125,L=K=16\), and a 300-order symmetric filter was employed for clutter suppression. The process was performed on the same desktop as in Section 6, during which all 375 million samples were prefetched into the memory at the first stage to reduce the impact of file loading. According to Table 1, precompression can reduce the Flops by about 80% with the above settings. In contrast, the profile shows that the overall costs for coefficient evaluation and cancellation of ECA-LF-PC are 0.33 s and 0.01 s, respectively, and those of the non-precompressed version are 1.32 s and 0.40 s, where the slight difference is acceptable considering that Flops is not the only factor determining the time cost. Compared with the benchmark results in Section 6, the time cost of ECA-LF-PC in experiments is reduced to about 40% because of the smaller \(K\) and larger \(N/B\). Due to the limited range resolution and fixed beams, the influence of UAV motion on the echo's envelope is negligible during the analysis. Therefore, a short-time Fourier transform can be performed directly after the clutter suppression to observe the target's varying bistatic velocity, which was applied to the range bins where the UAV locates to achieve an equivalent CPI of 0.5 s. The normalized energy distributions of different beams are shown in Figure 8, where the Doppler notch with the same width exists in all results because of the same filtering coefficients. Compared with the other two beams, an SNR improvement of more than 6 dB can be found in the first beam's data because of the higher RCS brought by forward scattering. In contrast, although the backscattering geometry brings about higher bistatic velocity, the UAV is more challenging to be detected because of the lower echo energy. The CPI was increased to 2 s before detection to reduce the impact of a low SNR, where the high-order Doppler phase caused by the UAV's acceleration was compensated by fractional Fourier transform.