A low computational complexity DOA estimation using sum/difference pattern based on DNN

Tracking low-elevation targets over an uneven surface is challenging because of the complicated and volatile multipath signals. Multipath signals cause the amplitude and phase distortion of direct signal, which degrades the performance and generates mismatch between existing classical multipath signal and actual model. Machine learning-based methods are data-driven, they do not rely on prior assumptions about array geometries, and are expected to adapt better to array imperfections. The amplitude comparison Direction-of-Arrival (DOA) algorithm performs a few calculations, has a simple system structure, and is widely used. In this paper, an efficient DOA estimation approach based on Sum/Difference pattern is merged with deep neural network. Fully learn the potential features of the direct signal from the echo signal. In order to integrate more phase features, the covariance matrix is applied to the amplitude comparison algorithm, it can accommodate multiple snapshot signals instead of a single pulse automatically. The outputs of the deep neural network are concatenated to reconstruct a covariance matrix for DOA estimation. Moreover, the superiority in computational complexity and generalization of proposed method are proved by simulation experiments compared with state-of-the-art physics-driven and data-driven methods. Field data sets acquired from a VHF array radar are carried out to verify the proposed method satisfies practicability in the severe multipath effect.

Automated summary

Tracking low-elevation targets over an uneven surface is difficult due to complicated and volatile multipath signals. Machine learning-based methods, which do not rely on prior assumptions, can adapt better to array imperfections. This paper proposes a DOA estimation approach that combines the Sum/Difference pattern with a deep neural network to fully learn the features of the direct signal. Simulation experiments and field data sets verify the computational superiority and practicality of the proposed method in severe multipath effects.