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Title: | DOA Estimation of Time-Modulated Linear Array Based on Sparse Signal Recovery |

Authors: | Wen Tao Li;Ya Jie Lei;Xiao Wei Shi |

Year: | 2017 |

Publisher: | IEEE |

Abstract: | Under the circumstances of small number of snapshots, low signal-to-noise ratio, and closely spaced sources, especially with correlated signals, the existing direction of arrival angle (DOA) estimation methods for time-modulated linear arrays (TMLAs) generally does not yield satisfactory results. To deal with those problems, a new weighted <inline-formula><tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm DOA estimation algorithm is proposed for the TMLA in this letter. The proposed algorithm constructs the weighted matrix by making full use of the orthogonality of the signal subspace and the noise subspace to penalize the <inline-formula> <tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm constrained model. Accordingly, the reconstructed coefficient vector with better sparsity could be achieved, and the false peaks could be effectively suppressed. Simulation results have been provided to validate the effectiveness of the proposed method. |

Description: | |

URI: | http://localhost/handle/Hannan/175581 |

volume: | 16 |

More Information: | 2336, 2340 |

Appears in Collections: | 2017 |

Files in This Item:

File | Size | Format | |
---|---|---|---|

7954768.pdf | 527.29 kB | Adobe PDF |

Title: | DOA Estimation of Time-Modulated Linear Array Based on Sparse Signal Recovery |

Authors: | Wen Tao Li;Ya Jie Lei;Xiao Wei Shi |

Year: | 2017 |

Publisher: | IEEE |

Abstract: | Under the circumstances of small number of snapshots, low signal-to-noise ratio, and closely spaced sources, especially with correlated signals, the existing direction of arrival angle (DOA) estimation methods for time-modulated linear arrays (TMLAs) generally does not yield satisfactory results. To deal with those problems, a new weighted <inline-formula><tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm DOA estimation algorithm is proposed for the TMLA in this letter. The proposed algorithm constructs the weighted matrix by making full use of the orthogonality of the signal subspace and the noise subspace to penalize the <inline-formula> <tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm constrained model. Accordingly, the reconstructed coefficient vector with better sparsity could be achieved, and the false peaks could be effectively suppressed. Simulation results have been provided to validate the effectiveness of the proposed method. |

Description: | |

URI: | http://localhost/handle/Hannan/175581 |

volume: | 16 |

More Information: | 2336, 2340 |

Appears in Collections: | 2017 |

Files in This Item:

File | Size | Format | |
---|---|---|---|

7954768.pdf | 527.29 kB | Adobe PDF |

Title: | DOA Estimation of Time-Modulated Linear Array Based on Sparse Signal Recovery |

Authors: | Wen Tao Li;Ya Jie Lei;Xiao Wei Shi |

Year: | 2017 |

Publisher: | IEEE |

Abstract: | Under the circumstances of small number of snapshots, low signal-to-noise ratio, and closely spaced sources, especially with correlated signals, the existing direction of arrival angle (DOA) estimation methods for time-modulated linear arrays (TMLAs) generally does not yield satisfactory results. To deal with those problems, a new weighted <inline-formula><tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm DOA estimation algorithm is proposed for the TMLA in this letter. The proposed algorithm constructs the weighted matrix by making full use of the orthogonality of the signal subspace and the noise subspace to penalize the <inline-formula> <tex-math notation="LaTeX">\ell 1</tex-math></inline-formula>-norm constrained model. Accordingly, the reconstructed coefficient vector with better sparsity could be achieved, and the false peaks could be effectively suppressed. Simulation results have been provided to validate the effectiveness of the proposed method. |

Description: | |

URI: | http://localhost/handle/Hannan/175581 |

volume: | 16 |

More Information: | 2336, 2340 |

Appears in Collections: | 2017 |

Files in This Item:

File | Size | Format | |
---|---|---|---|

7954768.pdf | 527.29 kB | Adobe PDF |