Improved snapshot-deficient active target localization using the knowledge-aided covariance of reverberation

Abstract

Underwater reverberation often hinders the effectiveness of adaptive methods in active target localization with snapshot-deficient conditions. To overcome this challenge, a knowledge-aided reverberation covariance-based approach is proposed to maintain high resolution while reducing sidelobe levels. Using the aided reverberation covariance computed from the reverberation model, the knowledge-aided sample covariance matrix is constructed and used to decrease reverberation and compensate for snapshot deficiency. Simulations show that the proposed approach can localize targets with improved resolution and reduce reverberation levels in low signal-to-reverberation ratio situations, manifesting its potential to enhance adaptive processing reliability for active target localization.