Array signal processing robust to pointing errors

The objective of this thesis is to design computationally efficient DOA (direction-of- arrival) estimation algorithms and beamformers robust to pointing errors, by harnessing the antenna geometrical information and received signals. Initially, two fast root-MUSIC-type DOA estimation algorithms are developed, which can be applied in arbitrary arrays. Instead of computing all roots, the first proposed iterative algorithm calculates the wanted roots only. The second IDFT-based method obtains the DOAs by scanning a few circles in parallel and thus the rooting is avoided. Both proposed algorithms, with less computational burden, have the asymptotically similar performance to the extended root-MUSIC. The second main contribution in this thesis is concerned with the matched direction beamformer (MDB), without using the interference subspace. The manifold vector of the desired signal is modeled as a vector lying in a known linear subspace, but the associated linear combination vector is otherwise unknown due to pointing errors. This vector can be found by computing the principal eigen-vector of a certain rank-one matrix. Then a MDB is constructed which is robust to both pointing errors and overestimation of the signal subspace dimension. Finally, an interference cancellation beamformer robust to pointing errors is considered. By means of vector space projections, much of the pointing error can be eliminated. A one-step power estimation is derived by using the theory of covariance fitting. Then an estimate-and-subtract interference canceller beamformer is proposed, in which the power inversion problem is avoided and the interferences can be cancelled completely

Signals and Images in Sea Technologies

Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas