Simplified adaptive noise subspace algorithms for robust direction tracking

J. F. Yang, H. T. Wu, F. K. Chen

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The authors have developed two simplified adaptive eigen-subspace methods which robustly converge to the noise subspace only if the number of sources is less than the number of sensors. The first simplification, achieved by introducing an orthogonal factor, reduces the computational complexity and preserves the parallel structure of the inflation method (Yang and Kaveh, 1988). The convergence performances and initialization behaviours perform better than other adaptive eigen-subspace algorithms when the number of sources is unknown. Further simplification is achieved using a unitary transformation approach (Huarng and Yeh, 1991). This leads to an adaptive real eigen-subspace algorithm which further reduces the computational complexity and also resolves the paired multipath problem. Simulations for evaluations of the proposed and the existing algorithms are also included in this paper.

Original languageEnglish
Pages (from-to)329-334
Number of pages6
JournalIEE Proceedings, Part F: Radar and Signal Processing
Volume140
Issue number5
DOIs
Publication statusPublished - 1993 Jan 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Simplified adaptive noise subspace algorithms for robust direction tracking'. Together they form a unique fingerprint.

  • Cite this