# Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
## 1 Introduction
This is paid in terms of an increased complexity for the resulting system, since it requires (i) the estimation and inversion of a \((NL\times NL)\) space-time disturbance covariance matrix, with \(L\) being the number of temporal DOF; (ii) the availability of an amount of training data greater than \(2 NL\) in order to limit the adaptivity loss, which might be difficult to be guaranteed in the considered bistatic passive radar scenario; and (iii) the implementation of computationally expensive algorithms for the maximisation of the DOA MLE likelihood function, with computational load being dependant on the desired estimation accuracy. In this paper, we address the above limitations and propose an alternative approach based on a dual cancelled channel STAP scheme. This approach takes inspiration from a family of sub-optimal STAP schemes reported in the active radar literature [24; 25; 26; 27], where an adaptive transformation is first applied on the received data to reduce the number of spatial DOF, which are then directly exploited for target detection and DOA estimation. For instance, the generalised monopulse estimator (GME) in [25] relies on the sum and difference channels obtained after an adaptive clutter cancellation. However, in order to also limit the computational cost of the preliminary adaptive transformation, we resort to the AB-STAP technique presented by the authors in [26]. This technique is first integrated into the post-Doppler STAP scheme proposed in [18]. After the first processing stages are applied separately at each receiving channel, the outputs are arranged into two spatially displaced antenna sub-arrays, and a STAP scheme is applied to each of them to obtain two clutter cancelled channels. These channels are then adaptively recombined for the purpose of target detection, while target DOA can be estimated by exploiting their different phase centres. The AB-STAP approach further reduces the computational complexity of the system by lowering the number of the adaptive DOF in the space-time clutter filtering (namely, the size of the covariance matrices to be estimated and inverted). Despite the lower computational cost, the proposed scheme does not suffer from significant performance losses with respect to the conventional full-array solution. Moreover, it is more robust against adaptivity losses, operating effectively even in the presence of a limited sample support. This plays a key role in a real passive radar scenario, where the non-homogeneity of the clutter response may limit the number of relevant training data, affecting the STAP performance. Lastly, the target DOA estimation does not involve a functional maximisation, thanks to the closed form expression of the estimator, thus requiring a lower computational cost, which in turn is independent of the desired estimation accuracy. The moving target detection and localisation performance of the proposed strategy were analysed and compared with those of the equivalent full array solution (assumed as a benchmark), first in a simulated scenario and then against experimental data collected by a DVB-T based multichannel passive radar mounted on a moving platform. The rest of the paper is organised as follows. In Section 2, the processing scheme for passive radar STAP proposed in [18] is recalled. In Section 3, we describe the processing steps of a dual cancelled channel STAP scheme for passive radar. Its performance is then analysed in Section 4, both in terms of target detection and DOA estimation, against a simulated clutter scenario and compared with that of the corresponding full array solution. In Section 5, the effectiveness of the proposed scheme is validated against a set of experimental data. Finally, we draw our conclusions in Section 6.