scholarly journals Hybrid Reweighted Optimization Method for Gridless Direction of Arrival Estimation in Heteroscedastic Noise Environment

2021 ◽  
Vol 8 (1) ◽  
pp. 125-133
Author(s):  
Lama Zien Alabideen ◽  
Oumayma Al-Dakkak ◽  
Khaldoun Khorzom
Keyword(s):  
Direction Of Arrival ◽  
Doa Estimation ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Local Minima ◽  
Correlated Sources ◽  
Optimization Framework ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Global And Local

In this paper, we present a hybrid optimization framework for gridless sparse Direction of Arrival (DoA) estimation under the consideration of heteroscedastic noise scenarios. The key idea of the proposed framework is to combine global and local minima search techniques that offer a sparser optimizer with boosted immunity to noise variation. In particular, we enforce sparsity by means of reformulating the Atomic Norm Minimization (ANM) problem through applying the nonconvex Schatten-p quasi-norm (0<p<1) relaxation. In addition, to enhance the adaptability of the relaxed ANM in more practical noise scenarios, it is combined with a covariance fitting (CF) criterion resulting in a locally convergent reweighted iterative approach. This combination forms a hybrid optimization framework and offers the advantages of both optimization approaches while balancing their drawbacks. Numerical simulations are performed taking into account the configuration of co-prime array (CPA). The simulations have demonstrated that the proposed method can maintain a high estimation resolution even in heteroscedastic noise environments, a low number of snapshots, and correlated sources.

scholarly journals Superresolution 2D DOA Estimation for a Rectangular Array via Reweighted Decoupled Atomic Norm Minimization

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Ming-Ming Liu ◽  
Chun-Xi Dong ◽  
Yang-Yang Dong ◽  
Guo-Qing Zhao
Keyword(s):  
Computational Complexity ◽  
Optimization Problem ◽  
Toeplitz Matrices ◽  
Doa Estimation ◽  
Rank Minimization ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Surrogate Function

This paper proposes a superresolution two-dimensional (2D) direction of arrival (DOA) estimation algorithm for a rectangular array based on the optimization of the atomic l0 norm and a series of relaxation formulations. The atomic l0 norm of the array response describes the minimum number of sources, which is derived from the atomic norm minimization (ANM) problem. However, the resolution is restricted and high computational complexity is incurred by using ANM for 2D angle estimation. Although an improved algorithm named decoupled atomic norm minimization (DAM) has a reduced computational burden, the resolution is still relatively low in terms of angle estimation. To overcome these limitations, we propose the direct minimization of the atomic l0 norm, which is demonstrated to be equivalent to a decoupled rank optimization problem in the positive semidefinite (PSD) form. Our goal is to solve this rank minimization problem and recover two decoupled Toeplitz matrices in which the azimuth-elevation angles of interest are encoded. Since rank minimization is an NP-hard problem, a novel sparse surrogate function is further proposed to effectively approximate the two decoupled rank functions. Then, the new optimization problem obtained through the above relaxation can be implemented via the majorization-minimization (MM) method. The proposed algorithm offers greatly improved resolution while maintaining the same computational complexity as the DAM algorithm. Moreover, it is possible to use a single snapshot for angle estimation without prior information on the number of sources, and the algorithm is robust to noise due to its iterative nature. In addition, the proposed surrogate function can achieve local convergence faster than existing functions.

scholarly journals Grid-Free DOD and DOA Estimation for MIMO Radar via Duality-Based 2D Atomic Norm Minimization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 60827-60836 ◽  
Author(s):  
Wen-Gen Tang ◽  
Hong Jiang ◽  
Shuai-Xuan Pang
Keyword(s):  
Doa Estimation ◽  
Mimo Radar ◽  
Norm Minimization ◽  
Atomic Norm

scholarly journals DOA estimation based on sum–difference coarray with virtual array interpolation concept

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yarong Ding ◽  
Shiwei Ren ◽  
Weijiang Wang ◽  
Chengbo Xue
Keyword(s):  
Numerical Simulation ◽  
Degrees Of Freedom ◽  
Doa Estimation ◽  
Superior Performance ◽  
Norm Minimization ◽  
Atomic Norm ◽  
Virtual Array ◽  
Simulation Results ◽  
The Difference ◽  
Coprime Array

AbstractThe sum–difference coarray is the union of difference coarray and the sum coarray, which is capable to obtain a higher number of degrees of freedom (DOF) than the difference coarray. However, this method fails to use all information provided by the coprime array because of the existence of holes. In this paper, we introduce the virtual array interpolation into the sum–difference coarray domain. After interpolating the virtual array, we estimate the DOA by reconstructing the covariance matrix to resolve an atomic norm minimization problem in a gridless way. The proposed method is gridless and can effectively utilize the DOF of a larger virtual array. Numerical simulation results verify the effectiveness and the superior performance of the proposed algorithm.

scholarly journals Low Complexity Beamspace Super Resolution for DOA Estimation of Linear Array

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2222
Author(s):  
Jie Pan ◽  
Fu Jiang
Keyword(s):  
Array Signal Processing ◽  
Linear Array ◽  
Mimo Systems ◽  
Super Resolution ◽  
Doa Estimation ◽  
Superior Performance ◽  
Estimation Methods ◽  
Iteration Algorithm ◽  
Norm Minimization ◽  
Atomic Norm

Beamspace processing has become much attractive in recent radar and wireless communication applications, since the advantages of complexity reduction and of performance improvements in array signal processing. In this paper, we concentrate on the beamspace DOA estimation of linear array via atomic norm minimization (ANM). The existed generalized linear spectrum estimation based ANM approaches suffer from the high computational complexity for large scale array, since their complexity depends upon the number of sensors. To deal with this problem, we develop a low dimensional semidefinite programming (SDP) implementation of beamspace atomic norm minimization (BS-ANM) approach for DFT beamspace based on the super resolution theory on the semi-algebraic set. Then, a computational efficient iteration algorithm is proposed based on alternating direction method of multipliers (ADMM) approach. We develop the covariance based DOA estimation methods via BS-ANM and apply the BS-ANM based DOA estimation method to the channel estimation problem for massive MIMO systems. Simulation results demonstrate that the proposed methods exhibit the superior performance compared to the state-of-the-art counterparts.

Sign in / Sign up

Export Citation Format

Share Document