When the elevation of target is smaller than a beamwidth, the complex multipath signals will distort the feature of direct signal reflected from target. The elevation of target can hardly be estimated accurately. Hence, in this paper, we proposed three kinds of neural networks models including deep neural network (DNN), 1-D convolutional neural network (1-D CNN) and 2-D convolutional neural network (2-D CNN) and their optimization method to mitigate phase distortion caused by multipath signals and enhance the phase feature of direct signal. The direction of arrival (DOA) estimation accuracy of physics-driven methods including digital beamforming (DBF) and multiple signal classification (MUSIC) is effectively improved. Concretely, we analyze the origins of error of DOA estimation in multipath environment and discuss the importance of phase feature to DOA estimation. A complete framework of feature-to-feature phase enhancement is built for DOA estimation in radar systems. The results of experiments with real data collected from a very high frequency (VHF) radar demonstrate the superior DOA estimation performance of proposed feature-to-feature learning methods with respect to other state-of-the-art methods including physics-driven methods and existing data-driven methods.

We propose anew antenna array design approach for a multiple-input and multiple-output (MIMO) radar, which has closed-form expressions for the sensor locations and the number of achievable degrees of freedom (DOFs). This new approach utilizes the nested array as transmitting and receiving arrays. We employ the difference coarray of the sum coarray (DCSC) of the MIMO radar to obtain more DOFs for direction-of-arrival (DOA) estimation. Via properly designing the interelement spacings of the transmitting and receiving arrays, we can obtain a hole-free DCSC. The characteristics of array geometries are analyzed and the optimal numbers of sensors in transmitting/receiving antenna array are derived when given the total number of physical sensors. Simulations are conducted to demonstrate the advantages of the proposed array in terms of the number of DOFs, the number of resolvable sources, and the DOA estimation performance over the coprime MIMO array.

To improve precision in source localization from a time difference of arrival (TDOA) that has large measurement errors, this paper proposes a TDOA positioning algorithm based on an improved two-step constrained total least-squares algorithm; the algorithm comprise an iterative technique based on the alternating direction method of multipliers (ADMM). The algorithm linearizes the obtained time difference observation equation, and by considering the influence of the TDOA measurement errors on its accuracy, it provides the optimal TDOA positioning model. It first transforms the initial unary optimization problem into a binary optimization problem by introducing intermediate variables. It then obtains a coarse estimate of the source position with ADMM. It subsequently calculates a measurement matrix through the coarse estimate and the finally uses ADMM again to find the final source position. Simulation results show that the proposed algorithm can approach Cramer-Rao lower bound (CRLB) better and outperforms several existing methods when there are large TDOA measurement errors.

In a low-angle target parameter estimation scenario, the backscattered signals from targets are often distorted due to clutter and multipath, which significantly degrades the performance of direction-of-arrival (DOA) estimation. In general, the backscattered multipaths are modeled as coherent signals with respect to the direct path. Decorrelation algorithms such as spatial smoothing and matrix reconstruction can mitigate the effect of multipath. However, most of these methods will perform poorly or even fail for solving parameter estimation problem of low-angle targets in complex terrain where there are rich multipath scatterings. This paper presents a novel method that combines time reversal (TR) technique, coherent signal-subspace method (CSM), and multiple signal classification algorithm to perform DOA estimation in a low-angle target scenario. The TR technique takes advantage of multipath echoes recorded by a sensor array and adaptively adjusts TR probing waveforms to increase the signal-to-noise ratio in a rich scattering environment. Moreover, the CSM method compresses the energy of a signal into a predefined subspace to exploit the full time-bandwidth product of signal sources and cope with coherent wavefronts. As a result, the proposed new DOA estimation algorithm outperforms significantly the conventional methods. The Cramer-Rao bounds analysis and numerical simulations commendably validate the accuracy and robustness of the proposed method.