## 6 Validation in a Semi-Urban Bistatic Radar Scenario
### Array Signal Processing Performance Evaluation
#### 6.2.1 First Array Signal Processing Stage: Target Detection  
Taking into consideration the nulls of the main beam associated with the array radiation pattern (Figure 11(a)) and the azimuth coverage area limited by the Televes 4G NOVA beamwidth, the steering angles for the first stage beamformer are: \(\mathbf{\Phi}=[-18.66^{\circ},0,18.66^{\circ}]\). Figure 17 presents the NULA azimuth radiation pattern of the multiple simultaneous orthogonal beams. Results show that Alcala-Meco road and R2 highway are covered for the different orthogonal beams allowing the tracking of cars' trajectories and traffic monitoring, guaranteeing the SLL requirement for all of the steering angles. Figure 16: Radar scenario located at the roof of the Superior Polytechnic School of Alcala University. The coverage for only one Televes 4G NOVA antenna in the surveillance channel is depicted in Figure 17(a), for \(P_{D}=0.8\) and \(P_{FA}=10^{-6}\), using system parameters summarized in [8]. In [8], only basic propagation losses were considered; in this new study, WinProp (AWE Communications) software was used, including geographical data and taking into consideration that the desired targets were located on the ground. Results presented in Figure 17(b) confirm the range coverage improvement obtained using an array antenna in the surveillance channel. Figure 17: Azimuth radiation pattern of NULA multiple simultaneous orthogonal beams in the azimuth coverage area \(\phi\in[-30^{\circ},-30^{\circ}]\). The \(P_{FA}\) requirement was set to \(10^{-5}\) to determine the adaptive CFAR threshold. The superposition of the detection matrices obtained from the processing of 80 consecutive CPIs is presented in Figure 19. Results provided by only a single radiating element of the array are shown in Figure 19a. Two main groups of detections are identified: a set of trajectories located in the Alcala-Meco road (marked with the green ellipse); a second set associated with R2 highway (marked with the red ellipse). From visual inspection, the number of detected targets and the detection densities of the guessed trajectories are clearly higher for the NULA and the detection stage array processing. For performing a more rigorous detection performance evaluation, three trajectories were selected without loss of generality (Figure 20):  
* Trajectory 1 (T1): a cooperative moving target provided with a GPS receiver, running along Alcala-Meco road, observed from 25 th CPI-49 th CPI. * Trajectory 2 (T2): a moving target in Alcala-Meco road, observed from 28 th CPI-79 th CPI. * Trajectory 3 (T3): a moving target in R2 highway, observed during the whole acquisition interval (from 0 th CPI-79 th CPI). Figure 19: CFAR detection performance using a single radiating element (**a**) and the proposed first array signal processing stage applied to the signals acquired by the designed NULA (**b**). To estimate \(P_{FA}\) and \(P_{D}\), ground-truths at the output of the detector are required, but due to the complex nature of the electromagnetic propagation processes, targets dynamics and radar system, the real ground-truth is not always available. Using the methodology described in [8], a ground-truth was generated for each CFAR detector using GPS data of cooperative vehicles and visual information about non-cooperative targets present on R-2 highway and Meco road during the acquisitions. The \(P_{D}\) was calculated at the plot level due to the errors associated with the pixel detection grouping techniques; \(P_{FA}\) was estimated throughout CAF areas where no targets were expected. Monte Carlo techniques were applied, guaranteeing an estimation error lower than 10%. Table 10 details the estimated \(P_{D}\) and \(P_{FA}\) for the trajectory associated with the cooperative target, \(T1\). Table 11 presents the number of detections at the plot level for the three targets, \(T1\), \(T2\) and \(T3\).