Direction-of-arrival estimation using estimator banks in low-angle tracking for S-band radar

Tracking low-angle targets over an uneven surface are challenging because of the highly correlated, complicated, and volatile multipath signals encountered in radar. Especially, in the context of irregular reflector modulated by rough sea, which results in performance depravation of the existing direction-of-arrival (DOA) estimation methods. An effective DOA estimation approach is based on maximum likelihood (ML) estimator which is referred to as optimal synthetic vector maximum likelihood (OSVML) method. The approach is essentially different, multipath signals are present that without known prior information of ideal model. The optimal projection space in estimator banks is spanned with direct subspace and indirect subspace to match the signals received by S-band radar. Then, the performance of the proposed method is evaluated by simulations in terms of the signal-to-noise ratio (SNR) and snapshot. Finally, field data sets acquired from S-band radar are carried out to verify the practicability of the proposed method in a naval environment.

Automated summary

Tracking low-angle targets over an uneven surface is challenging due to the presence of highly correlated, complicated, and volatile multipath signals encountered in radar. An effective direction-of-arrival (DOA) estimation approach based on maximum likelihood (ML) estimator, known as optimal synthetic vector maximum likelihood (OSVML) method, has been proposed to address this challenge. The performance of this method has been evaluated through simulations and field data sets acquired from S-band radar, showing promising results in naval environments.