Reweighted nuclear norm minimisation for DOA estimation with unknown mutual coupling

Abstract

A reweighted nuclear norm minimisation algorithm by considering the inherent rank sparsity of the submatrix block for direction‐of‐arrival (DOA) estimation with unknown mutual coupling in a uniform linear array (ULA) is proposed. A novel block overcomplete dictionary is first derived by parameterising the steering vector to avoid the unknown mutual coupling effect. Then, in order to take advantage of the inherent rank sparsity of the submatrix block, a reweighted nuclear norm minimisation algorithm is introduced, and the weighted matrix can be designed by the Capon spectrum for enhancing the sparsity of scheme. Finally, the DOA estimates can be obtained by the spatial spectrum of the reconstructed matrix. Simulation results verify that the proposed method has distinct advantages over state‐of‐the‐art methods.

Green Deployment Method of Micro Base Station for Ultra-Dense Heterogeneous Cellular Networks Based on Constrained Dolphin Swarm Algorithm

“…It is necessary to compare the fitness value, but the constrained multi-objective problem has multiple targets, and it is impossible to judge the individual superiority directly through the fitness value. Therefore, we consider the particularity of constraining the multi-objective problem and propose a new search strategy as shown in (21).…”

“…Analysis ( 21) can be seen: First, in view of the fact that the dolphin swarm algorithm sets three search strategies to match different optimization problems, (21) retains this idea, provides two search strategies, and automatically selects one to explore the new location according to the change of p; Second, using the characteristics of the constrained multi-objective problem, that is, there are multiple non-dominated sorting solutions in the early stage of evolution, that is, most solutions are not close to the real Pareto frontier, and most solutions are in the optimal sorting level in the late stage of evolution. In (21), in the early stage of evolution, most individuals with poor grades learn from the better grades, and can quickly approach the real Pareto front, while at the same time let the excellent infeasible solutions participate in evolution to increase population diversity; By applying variability perturbations to individuals with superior levels, more excellent solutions can be explored, making them evenly distributed at the front of Pareto.…”

Coverage Control Algorithm-Based Adaptive Particle Swarm Optimization and Node Sleeping in Wireless Multimedia Sensor Networks

“…Particle swarm optimization (PSO) is a swarm intelligent optimization algorithm proposed by Kennedy and Eberhart in 1995 through simulating the social behavior of birds foraging [35], [36]. At present, PSO has been widely used in many fields such as function optimization, engineering design, neural network training and fuzzy system control, etc [37], [38]. These successes can be attributed to the fact that PSO has the advantages of simple operation, insensitivity to initial setting and good global search capability.…”

Robust STAP With Reduced Mutual Coupling and Enhanced DOF Based on Super Nested Sampling Structure

“…c and n denote the virtual clutter and noise vector respectively. Comparing (8) with (25), y can be regarded as a virtual clutter plus noise signal which derives from the 2OSNAP difference coarray withN sensors andnd spacing and CPI withP pluses andpT r spacing. Because y is a single snapshot, it is impossible to estimate CNCM using (16).…”

“…In [7], the theory of characteristic modes is used to characterize the mutual coupling effect, and a compensation matrix is constructed to compensate the mutual coupling effect. The reweighted unclear norm minimization algorithm based on the inherent rank sparsity of sub-matrix block can avoid the unknown mutual coupling [8]. In [9], the CNCM of middle subarray is reconstructed to suppress the mutual coupling and address the target space-time steering vector mismatch by using the clutter spectrum of middle subarray to integrate over the clutter spatial temporal domain.…”