Gridless Evolutionary Approach for Line Spectral Estimation With Unknown Model Order

Bai Yan1; Qi Zhao2; Jin Zhang2; J. Andrew Zhang3; Xin Yao2

2022

10.1109/TCYB.2022.3179378

IEEE Transactions on Cybernetics   影响因子和分区

2168-2275

2168-2275

Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic l norm (i.e., the continuous analog of l norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of the atomic l norm, such as the nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order using the atomic l norm. To accomplish this idea, we build a multiobjective optimization model. The measurement error and the atomic l norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic l norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that helpfully exploring the variable and open-ended frequency search space and provide extensive potentials toward the optima. Another innovation is a model-order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model-order selection.

Gridless Line Spectral Estimation (LSE) Multiobjective Evolutionary Algorithm Sparse Representation

SCI ; EI

英语

第一 ; 通讯

20222812349749

Atoms ; Calculations ; Errors ; Evolutionary Algorithms ; Latexes ; Multiobjective Optimization ; Spectrum Analysis

Colloid Chemistry:801.3 ; Mathematics:921 ; Optimization Techniques:921.5 ; Atomic And Molecular Physics:931.3

2-s2.0-85133797135

IEEE

1.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
2.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
3.School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia

Bai Yan,Qi Zhao,Jin Zhang,et al. Gridless Evolutionary Approach for Line Spectral Estimation With Unknown Model Order[J]. IEEE Transactions on Cybernetics,2022,PP(99):1-13.

Bai Yan,Qi Zhao,Jin Zhang,J. Andrew Zhang,&Xin Yao.(2022).Gridless Evolutionary Approach for Line Spectral Estimation With Unknown Model Order.IEEE Transactions on Cybernetics,PP(99),1-13.

Bai Yan,et al."Gridless Evolutionary Approach for Line Spectral Estimation With Unknown Model Order".IEEE Transactions on Cybernetics PP.99(2022):1-13.