Adaptive antenna array for direction-of-arrival estimation utilizing the conjugate gradient method

Seungwon Choi, Tapan K. Sarkar, Jaehoon Choi

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This paper proposes two alternative techniques for the Direction-of-Arrival (DOA) estimation. Both techniques utilize the respective modifications of the conjugate gradient method (CGM) for iteratively finding the weight vector which is orthogonal to the signal subspace. In the first method, an eigenvector corresponding to the smallest eigenvalue is computed by minimizing the Rayleigh quotient of the full complex-valued autocovariance matrix. In the second method, a vector which is orthogonal to the signal subspace is computed directly from the signal matrix by finding a set of weights that minimizes the signal power at the array output. The performances of the proposed techniques are compared to that of the conventional eigen-decomposition (ED) method in terms of angle resolution for a given signal-to-noise ratio (SNR) with a preset number of snapshots in an observation interval. The standard deviation of angle dispersions is also encountered in measuring the array performance when a large number of inseparable signal components are involved in each cluster of the array inputs. From our computer simulations, we found that the proposed techniques in general result in comparable performances to the conventional ED method. The superiority of the suggested procedures becomes evident in adverse signal environments where the detection of the number of array inputs cannot be obtained successfully, for the proposed techniques are performed independently of the detection procedure.

Original languageEnglish (US)
Pages (from-to)313-327
Number of pages15
JournalSignal Processing
Volume45
Issue number3
DOIs
StatePublished - Sep 1995

Keywords

  • Adaptive array
  • Conjugate gradient method
  • Direction-of-arrival estimation
  • Dispersed signals
  • Orthogonality

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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