# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 5 Experimental Results
In Figure 16, three trajectories of target can be faintly seen; then, they are intercepted and enlarged, which are shown in Figure 17.  
It can be seen that the energy of the target echo is inversely proportional to its range, and since the PW of the signal is staggered, the energy of different echo signals after pulse compression are different. In addition, it is obvious that target #1 in Figure 17a is not moving, target #2 is a moving target since  
Figure 16: Time-range map after pulse compression and realignment. Figure 12.0 Time-range map of three targets. RCM occurs in Figure 17b, while the SNR of the target #3 is too low to judge whether it is moving. Therefore, the energy of the target echo should be accumulated by using the coherent integration method proposed in Section 4 so as to improve the detection probability of the target. Also, the velocity of targets can be estimated, thereby filtering out the fixed clutter. Figure 18 shows the results of coherent integration of the target echo in Figure 17 using the Radon-NUFFT method. According to Figure 18, the distance and velocity information of the three targets can be obtained as shown in Figure 12.1  
Therefore, the motion parameters of the aircraft target can be effectively obtained by using the coherent integration method proposed in Section 4. After coherent integration and filtering of fixed clutter, CFAR detection is carried out for signals of different beams with a false alarm rate of \(10^{-4}\). After obtaining the target detection result, the target azimuth is obtained by amplitude comparison direction measurement. Finally, the distance between the target and the PBR system can be calculated according to Equation (2) combined with the bistatic range and the azimuth of the target, so the target positioning can be achieved afterwards. The target positioning results processed by PBR system for 120 s and target tracks converted from the real-time location information of flights provided by Automatic Dependent Surveillance-Broadcast \begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Number** & **Distance (km)** & **Velocity (m/s)** \\ \hline \#1 & 42.03 & 0.98 \\ \hline \#2 & 93.06 & 203 \\ \hline \#3 & 128.03 & 164 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Motion parameters of the targets. Figure 18: Coherent integration results of three targets. (ADS-B) at the same time are drawn on the same figure to verify the target detection performance of the PBR system, which is shown in Figure 19.  
It can be seen that the PBR system achieves the detection and localization of three moving aircraft targets effectively. There are some deviations between the target dots and the tracks provided by ADS-B since the error of target's monostatic range depends on the target's bistatic range and azimuth measurement errors at the same time. Moreover, it can be seen that there are some erroneous points far from the actual target tracks, which are false alarm points caused by clutter and noise; since these erroneous points are not continuous in time, they can be filtered out by the target tracking method, which will be explored in our future work. To validate the accuracy of the PBR system, we compare the monostatic distance and azimuth of the target dots near the three target tracks in 120 s with an actual target position, and the root mean square error (RMSE) of the distance and azimuth of the target dots can be calculated to be 984 m and 2.7\({}^{\circ}\), respectively. It can be concluded that the PBR system based on PAR signals is able to locate the aircraft targets in the surveillance airspace.