Direction Of Arrival Angle Estimation Research Articles

A radar extended target angle estimation method based on effective phase clustering

AbstractFor high‐resolution radar, traditional direction of arrival (DOA) angle measuring algorithms for point‐like targets cannot be directly applied to extended targets. Instead, pixel‐based detection and clustering must be performed first, followed by angle estimation. However, the presence of factors such as clutter and system noise makes it impossible to guarantee a one‐to‐one correspondence between the multiple scattering points of an extended target and the real target, which may result in significant discrepancies between the angle estimation and localization results and the real target. To address this issue, this paper proposes and discusses a radar extended target arrival angle estimation method based on effective phase clustering. The proposed method selects effective target scatterers through the interferometric phase information between channels, clusters these points, and then estimates the DOA angle of the extended target, effectively reducing the impact of interference scatterers on DOA angle estimation and improving the angular measurement accuracy. The effectiveness of the proposed method was validated by the measured data obtained from an airborne digital beamforming radar system.

DOA Estimation of Time-Modulated Linear Array Based on Sparse Signal Recovery

Under the circumstances of small number of snapshots, low signal-to-noise ratio, and closely spaced sources, especially with correlated signals, the existing direction of arrival angle (DOA) estimation methods for time-modulated linear arrays (TMLAs) generally does not yield satisfactory results. To deal with those problems, a new weighted l1-norm DOA estimation algorithm is proposed for the TMLA in this letter. The proposed algorithm constructs the weighted matrix by making full use of the orthogonality of the signal subspace and the noise subspace to penalize the l1-norm constrained model. Accordingly, the reconstructed coefficient vector with better sparsity could be achieved, and the false peaks could be effectively suppressed. Simulation results have been provided to validate the effectiveness of the proposed method.

Azimuth and elevation angle estimation with no failure and no eigen decomposition

Recently, Wu et al. proposed a scheme for two-dimensional direction of arrival angle estimation for azimuth and elevation angles, using the propagator method. An advantage of this method over the classical subspace based algorithms, such as ESPRIT and MUSIC, is that it does not apply any eigenvalue decomposition (EVD) to the cross spectral matrix or singular value decomposition (SVD) to the received data. This significantly reduces the computational complexity, compared to the EVD and SVD. However, Wu's method has some drawbacks, such as pair matching between the azimuth and elevation angle estimations for multiple different sources. Furthermore, Wu's method has an estimation failure problem in the range of practical mobile elevation angles. The objectives of this paper are two-fold: (1) to overcome these two problems with less arithmetic operation counts than Wu used; and (2) to improve the performance significantly. To achieve these objectives, we propose an antenna array configuration which avoids these problems. Simulation results verify that the proposed scheme can remove these problems and give much better performance.