Virtual-manifold ambiguity in HOS-based direction-finding with electromagnetic vector-sensors

Yougen Xu, Zhiwen Liu, Kainam Thomas Wong, Jinliang Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

37 Citations (Scopus)


Herein derived are the lower and upper bounds for the number of linearly independent (2Q)th-order virtual steering vectors of an array of electromagnetic vector-sensors, with Q being any positive integer over one. These bounds help determine the number of non-Gaussian signals whose directions-of-arrival (DOAs) can be uniquely identified from (2Q)th-order statistics data. The derived lower bounds increase with Q, whereas the derived upper bounds often fall below the maximum number of virtual sensors achievable from (2Q)th-order statistics manipulation. These bounds are independent of the permutation of the (2Q)th-order statistics entries in the higher order cumulant matrix that has a similar algebraic structure of the classical covariance matrix used in the second-order subspace-based direction-finding algorithms.
Original languageEnglish
Pages (from-to)1291-1308
Number of pages18
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number4
Publication statusPublished - 1 Dec 2008


  • Arrays
  • Electromagnetics
  • Estimation
  • Manifolds
  • Navigation
  • Upper bound
  • Vectors

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Virtual-manifold ambiguity in HOS-based direction-finding with electromagnetic vector-sensors'. Together they form a unique fingerprint.

Cite this