Dimension reduced robust beamforming for towed arrays

Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Electrical and Electronics Engineering, Turkey

Approval Date: 2015




Adaptive beamforming methods are used to obtain higher signal to interference plus noise ratio at the array output. However, these methods are very sensitive to steering vector and covariance matrix estimation errors. To overcome this issue, robust methods are usually employed. On the other hand, implementation of these robust methods can be computationally expensive for arrays with large number of sensors. Reduced dimension techniques aim to lower the computational load of adaptive beamforming algorithms with a minor loss of performance. In this thesis, the reduced dimension method is combined with the robust adaptive beamforming technique in order to obtain a rapidly converging, low complexity beamformer which is robust against the steering vector mismatches and small number of training snapshots. Moreover, a dimension reduction matrix that suppresses the known interferences such as the main-ship noise for towed arrays is designed to enhance the performance of the reduced dimension beamformer. The performance of the developed technique is illustrated by using both the simulated data (generated for different types of steering vector mismatches) and the field data obtained by a towed array in actual sea trials.