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Robust 3-D AOA Localization Based on Maximum Correntropy Criterion with Variable Center

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

This contribution investigates the problem of three-dimensional (3-D) angle-of-arrival (AOA) source localization (SL) in the presence of symmetric α-stable (SαS) impulsive noise for α ϵ (0, 2]. The azimuth and elevation angle measurements are initially rewritten into a pseudolinear form using spherical coordinate conversion, thereby making them more manageable. Subsequently, we adopt the maximum correntropy criterion with variable center (MCC-VC) to devise a robust 3-D AOA location estimator that functions effectively without the prior knowledge of parameters governing the impulsiveness and dispersion of SαS noise distributions. While it gives rise to a straightforward alternating minimization algorithmic framework, our analysis reveals that solely embracing MCC-VC leads to bias issues stemming from the correlation between the measurement matrix and noise. Aiming at addressing such a challenge, we introduce instrumental variables (IVs) to develop a bias-reduced maximum correntropy criterion (MCC) estimator, termed MCC with IV (MCC-IV). Simulation results illustrate a considerable performance enhancement of MCC-IV compared to existing schemes for 3-D AOA SL, particularly in achieving mean square error much closer to the Cramér-Rao lower bound and mitigating bias substantially. © 2024 IEEE.
Original languageEnglish
Pages (from-to)5021-5035
JournalIEEE Transactions on Signal Processing
Volume72
Online published28 Oct 2024
DOIs
Publication statusPublished - 2024

Funding

The work was supported by the Research Grant of Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China under Project R-IND25501.

Research Keywords

  • angle-of-arrival
  • bias
  • Cramér-Rao lower bound
  • impulsive noise
  • instrumental variable
  • Localization
  • maximum correntropy criterion with variable center
  • pseudolinear

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