Tracking a Low-Angle Isolated Target via an Elevation-Angle Estimation Algorithm Based on Extended Kalman Filter with an Array Antenna

In a low-angle tracking situation, estimating the elevation angle is challenging due to the entrance of the multipath signals in the antenna’s main lobe. In this article, we propose two methods based on the extended Kalman filter (EKF) and frequency diversity (FD) process to estimate the elevation angle of a low-angle isolated target. In the first case, a simple weighting of the per-frequency estimates is obtained (termed WFD). Differently, in the second case, a matrix-based elaboration of the per-frequency estimates is proposed (termed MFD). The proposed methods are completely independent of prior knowledge of geometrical information and the physical parameters. The simulation results show that both methods have excellent performance and guarantee accurate elevation angle estimation in different multipath environments and even in very-low SNR conditions. Hence, they are both suitable for low-peak-power radars.

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 (DNN) 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.