## 4 Conclusions  
This work presents different solutions based on beamforming techniques, taking advantage of an array architecture designed in previous work to be used for interference suppression in real passive radar data.  
The first solution proposes estimating the direction of arrival of the interferences and generating nulls in those directions in a deterministic way. It has been proved that the interferences power was decreased, but the targets' level also decreased as the beam pattern was eroded. Results showed that this technique does not improve targets' SNR, but the interference rejection will help to detect targets, close to cells containing interferences, which in another case would be masked.  
The second solution proposes applying adaptive beamforming to generate optimum weights employing the MVDR algorithm. Two different techniques were proposed to estimate the interference covariance matrix from RD maps, avoiding the inclusion of targets in training data samples. Both techniques show good performance, suppressing the interferences and improving targets' SNR significantly. These solutions allowed us to detect a target at a very far range: more than 26 km from the PR, which means around 47 km in bistatic range. This target was not appreciated without ABF implementation, and detection at this range in such clutter and interference conditions is a great achievement.  
The problem of beam erosion when generating deterministic nulls may be solved by limiting the proximity of nulls to beam steering or employing some constraints to assure beam steering. The interference suppression performance must be studied if those are employed. However, a more important problem of this proposal regards the implementation  
Figure 16: Estimated angle of each target along CPI and transformation of characteristics extracted from the radar data (range, angle) from them to Google Earth in the trials scenario.  
in real-time systems: estimation of DoA in all the azimuth plane angles is computationally expensive, and if it must be carried out in each CPI, real-time processing may be compromised. The main problem of ABF solution comes from the need to invert the sample matrix, but this can be solved by employing mathematics techniques such as QR decomposition.  
Finally, the improvement in SNR was employed to select the targets and perform DoA estimation, locating them in the AoI of the scenario and validating them as targets of interest.  
Conceptualisation, J.R.-S., M.P.J.-A., D.D.I.M.M. and N.R.-M.; methodology, J.R.-S., M.P.J.A, D.D.I.M.M. and N.R.-M.; software, J.R.-S. and N.R.-M.; validation, J.R.S and N.R.-M.; formal analysis, J.R.-S., M.P.J.A, D.M.M and N.R.-M.; investigation, J.R.-S., M.P.J.-A., D.D.I.M.M. and N.R.-M.; resources, M.P.J.-A. and D.D.I.M.M.; data curation, J.R.S and N.R.-M.; writing--original draft preparation, [PERSON], [PERSON], [PERSON] and N.R.M; writing--review and editing, [PERSON], [PERSON], D.D.I.M.M. and N.R.M; visualisation, [PERSON] and [PERSON]; supervision, [PERSON] and D.D.I.M.M.; project administration, [PERSON]-A., D.D.I.M.M. and [PERSON]; funding acquisition, [PERSON]-A., D.D.I.M.M. and [PERSON]M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Science, Innovation and Universities under project RTI2018101979-B-I00 and PID2021-128898 OB-I00; and the Region of Madrid under project CM/JIN/2021-010.  
Not applicable.  
The authors declare no conflicts of interest.