Experimental results for passive sonar arrays with eigenvector-based adaptive beamformers

In this paper, we compare the performance of several eigenvector-based adaptive beamforming (ABF) algorithms on experimental data collected with a passive sonar array. The first eigenvector-based ABF algorithm considered is dominant mode rejection (DMR) with a fixed, bearing independent diagonal loading level. Then, we look at two robust forms of DMR, DMR with a white noise gain constraint and DMR with eigenvector/beam association and excision. All algorithms are applied to experimental data and performance is evaluated in terms of beamformer output power and broadband detection. These ABF algorithms are also compared to conventional non-adaptive beamforming (CBF). The findings show a significant performance improvement for all ABF algorithms over CBF. Furthermore, the results show that less robust, more aggressive ABF achieves detection performance similar to eigenvector/beam association and excision algorithm, while both algorithms slightly outperform the more robust white noise gain constrained DMR.