Robust AOA-Based Target Localization for Uniformly Distributed Noise via ℓp-ℓ1 Optimization

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 9; p. 1259
• Main Authors: Chen, Yanping, Wang, Chunmei, Yan, Qingli
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 07.09.2022; MDPI
• Subjects: Accuracy; Algorithms; alternating direction method of multipliers; Angle of arrival; Computational geometry; Convexity; Error analysis; Localization; Methods; Noise measurement; Optimization algorithms; Optimization models; outliers; Outliers (statistics); Parameter sensitivity; Propagation; Regularization; Robustness (mathematics); Root-mean-square errors; Sensors; sparse regularization; ℓp-norm
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24091259

This paper addresses the problem of robust angle of arrival (AOA) target localization in the presence of uniformly distributed noise which is modeled as the mixture of Laplacian distribution and uniform distribution. Motivated by the distribution of noise, we develop a localization model by using the ℓp-norm with 0≤p<2 as the measurement error and the ℓ1-norm as the regularization term. Then, an estimator for introducing the proximal operator into the framework of the alternating direction method of multipliers (POADMM) is derived to solve the convex optimization problem when 1≤p<2. However, when 0≤p<1, the corresponding optimization problem is nonconvex and nonsmoothed. To derive a convergent method for this nonconvex and nonsmooth target localization problem, we propose a smoothed POADMM estimator (SPOADMM) by introducing the smoothing strategy into the optimization model. Eventually, the proposed algorithms are compared with some state-of-the-art robust algorithms via numerical simulations, and their effectiveness in uniformly distributed noise is discussed from the perspective of root-mean-squared error (RMSE). The experimental results verify that the proposed method has more robustness against outliers and is less sensitive to the selected parameters, especially the variance of the measurement noise.