Atomic Norm-Based DOA Estimation with Dual-Polarized Radar

Abstract

In the dual-polarized radar system, the horizontally and vertically polarized signals can be exploited to improve the direction of arrival (DOA) estimation performance. In this paper, the DOA estimation problem is considered in the dual-polarized radar. By exploiting the target sparsity in the spatial domain, the sparse-based method is proposed after formulating the DOA estimation problem as a sparse reconstruction problem. In the traditionally sparse methods using the compressed sensing (CS) theory, the spatial domain is discretized into grids to establish a dictionary matrix and solve the sparse reconstruction problem, but the off-grid error is introduced in the discretized grids. Therefore, we formulate a novel definition of atomic norm for the dual-polarized signals and give an atomic norm-based method to denoise the received signals. Then, an efficient semidefinite program (SDP) is derived, and the DOA is estimated by searching the peak values of the denoised signals. Simulation results show that the proposed method can significantly improve the DOA estimation performance in the dual-polarized radar. Additionally, compared with the state-of-art methods, the proposed method has better estimation performance with relatively low computational complexity.

Special Issue on “Advanced Technology Related to Radar Signal, Imaging, and Radar Cross-Section Measurement”

“…After a very careful peer-review process, a total of 32 papers were accepted. These works include SAR/ISAR [2][3][4][5][6][7][8][9], polarimetry [10][11][12], MIMO [13,14], direction of arrival (DOA)/direction of departure (DOD) [13][14][15], sparse sensing [5,14,16], ground-penetrating radar (GPR) [17][18][19], through-wall radar [20,21], coherent integration [22,23], clutter suppression [24,25], and meta-materials, among others [26][27][28][29][30][31]. All of these accepted papers are the latest research results and are expected to be further advanced, applied, and diverted.…”

Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

“…The other two categories, namely, gridless methods and off-grid methods, can eliminate or narrow this gap. Gridless methods [37][38][39][40][41][42] operate in the continuous domain directly so that they can avoid the grid mismatch problem. Gridless methods have strong theoretical guarantees and can only be applied to the uniform or sparse linear arrays.…”

Design of Novel Nested Arrays Based on the Concept of Sum-Difference Coarray

“…Direction of arrival (DOA) estimation of multiple signals is a hot topic in the area of array signal processing since it can be widely used in radar, sonar, and remote diagnosis, etc. [1][2][3][4][5]. In the past few decades, many subspace-based methods, such as multiple signal classification (MUSIC) [6], estimation of signal parameters via rotational invariance technique (ESPRIT) [7], and their modifications, have been proposed to address the DOA estimation problem.…”