DOA Estimation Algorithm Based on Compressed-Sensing

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 202)

Abstract

A novel kind of method using in the DOA estimate of array signal processing is proposed. This method is based on constructing matrix with random selection of the rows of DFT transformation matrix. Such matrix satisfies the RIP condition (restricted isometry property). Due to the sparsity of space signal, the amount of array sensor is reduced significantly, which results in a lower complexity of the array system. SVD decomposition is used in processing the sampling signal to minimize its dimension and the final performance is much better than traditional algorithms.

Keywords

Compressed sensing DOA estimation SVD decomposition 

References

  1. 1.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306MathSciNetCrossRefGoogle Scholar
  2. 2.
    Candes EJ, Romberg J, Terrence T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509MATHCrossRefGoogle Scholar
  3. 3.
    Cotter SF, Rao BD, Engan K, Delgado K (2005) Sparse solutions for linear inverse problems with multiple measurement vectors. IEEE Trans Signal Process 53(7):2477–2488MathSciNetCrossRefGoogle Scholar
  4. 4.
    Rao BD, Engan K, Cotter SF, Palmer J, Kreutz-Delgado K (2003) Subset selection in noise based on diversity measure minimization. IEEE Trans Signal Process 51(3):760–770CrossRefGoogle Scholar
  5. 5.
    Gorodnitsky IF, Rao BD (1997) Sparse signal reconstruction from limited data using FOCUSS: a re-weighted norm minimization algorithm. IEEE Trans Signal Process 45(3):600–616CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.School of Electronic EngineeringUESTCCheng DuChina

Personalised recommendations