• DocumentCode
    1846741
  • Title

    New generalized ESPRIT for direction estimation and its mathematical link to RARE method

  • Author

    Guangmin Wang ; Jingmin Xin ; Jiayi Wu ; Jiasong Wang ; Nangning Zheng ; Sano, Akihide

  • Author_Institution
    Inst. of Artificial Intell. & Robot., Xi´an Jiaotong Univ., Xi´an, China
  • Volume
    1
  • fYear
    2012
  • fDate
    21-25 Oct. 2012
  • Firstpage
    360
  • Lastpage
    363
  • Abstract
    The generalized ESPRIT (GESPRIT) method extends the conventional ESPRIT estimator to estimate the directions-of-arrival (DOAs) of multiple incident signals by using the array with more general geometrical configurations, where the translational invariance structure is not required. Unfortunately, the GESPRIT has serious ambiguous DOA estimates in some scenarios, and its performance degrades severely at low signal-to-noise ration (SNR) and with a small number of snapshots. Although a polynomial version of a new GESPRIT (NGESPRIT) method was given, but its derivation and estimation performance are unavailable in published literature. In this paper, in order to overcome the ambiguity of the GESPRIT and improve the estimation performance, the NGESPRIT method is derived explicitly. Moreover, the equivalence between the proposed NGESPRIT method and the rank reduction (RARE) method is clarified, while the former is more computationally efficient than the latter. Finally the effectiveness of the NGESPRIT method is substantiated through numerical examples.
  • Keywords
    array signal processing; direction-of-arrival estimation; polynomials; GESPRIT method; NGESPRIT method; RARE method; SNR; direction estimation; directions-of-arrival estimation; general geometrical configurations; generalized ESPRIT method; mathematical link; polynomial version; rank reduction method; sensor array processing; signal-to-noise ration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing (ICSP), 2012 IEEE 11th International Conference on
  • Conference_Location
    Beijing
  • ISSN
    2164-5221
  • Print_ISBN
    978-1-4673-2196-9
  • Type

    conf

  • DOI
    10.1109/ICoSP.2012.6491675
  • Filename
    6491675