# Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
## 5 Experimental Results
The observed clutter scenario was characterised by a large heterogeneity, associated with the presence of densely vegetated and rural areas. This aspect was exacerbated by the bistatic geometry of the passive radar exploiting a ground-based transmitter, prone to non-uniform illumination and shadowing phenomena, due to the orography of terrain. In this context, the AB-STAP approach represents a suitable solution, since it operates effectively even with a limited sample support. Figure 9b shows the range-Doppler map obtained after applying the full array STAP scheme, which jointly uses the \(N=4\) channels and \(L=3\) Doppler bins. The amount of exploited training data was set to \(K=16\). Notice that this amount was less than twice the number of adaptive DOF for the full array case (\(2 NL=24\)). As expected, the large adaptivity losses resulted in a high residual disturbance, which lowered the achievable SCNR and may hinder the detection of targets. For comparison, Figure 9c reports the corresponding range-Doppler map obtained after applying the AB-STAP scheme, using \(N_{0}=2\) elements for the A and B channels and employing the same amount of training data. In both cases, the spatial steering vector was oriented in a direction (\(\phi_{L}=37^{\circ}\)) such as to include in the main beam the real target Delphin and target T1, which both appear clearly visible in the final maps. Therefore, the SCNR obtained after STAP was indicated in the figure only for these targets. The other targets, whose DOAs were not included in the main beam, are less visible. The theoretical array pattern is shown in Figure 10, with the corresponding target angular positions. Notice that, due to the element spacing \(d\cong 0.83\lambda\), grating lobes arose for steering directions far from the broadside. In the considered case, target T3 is also visible in the map since located in direction of a grating lobe. Figure 10: Theoretical array pattern for steering at \(\phi=37^{\circ}\). Target positions are indicated by red circles. Figure 9: Range-Doppler maps resulting from the experimental data: (**a**) single channel map; (**b**) after full array STAP; (**c**) after AB-STAP. The amount of training data was set to \(K=16\). The spatial steering vector was towards the direction of targets T1 and Delphin. Target positions are indicated by white boxes. SCNR is reported after STAP only for targets included in the resulting main beam. The above result clearly demonstrates the effective clutter suppression and moving target detection capability of the AB-STAP approach, also in the presence of a limited sample support for the adaptive filter estimation. For a more comprehensive analysis and comparison of the detection performance, Figure 11 shows the results obtained with the full array STAP detector in (1) and with the AB-STAP detector in (4) by reporting their test statistics over the range-Doppler map, mapped into the nominal PFA values that would yield a detection. Basically, each range-Doppler bin was scaled to represent the minimum nominal PFA for that bin to be detected by the corresponding detection scheme. Specifically, the two schemes are compared in Figure 11a,b for \(K=24\) training data and, similarly, in Figure 11c,d for \(K=16\). Also in this case, the spatial steering vector was oriented towards the DOA of Delphin and target T1. For a full comparison, Table 3 reports the minimum PFA that allows for the detection of each target when an appropriate spatial steering includes the target in the main beam. Figure 11: Minimum nominal PFA to detect each bin: (**a**) full array STAP with \(K=24\); (**b**) AB-STAP with \(K=24\); (**c**) full array STAP with \(K=16\); (**d**) AB-STAP with \(K=16\). Values are expressed as \(\log_{10}(\mathrm{PFA})\). Spatial steering vector is towards the direction of targets T1 and Delphin. Target positions are indicated by black boxes.