An improved G-music algorithm for non-Gaussian noise condition direction-of-arrival estimation

2015 ◽  
Author(s):  
Mahmoud Ahmadi ◽  
Ehsan Yazdian ◽  
Ali A. Tadaion
Keyword(s):  
Gaussian Noise ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Music Algorithm ◽  
Noise Condition ◽  
Non Gaussian

scholarly journals Robust direction-of-arrival estimation in non-Gaussian noise

1998 ◽  
Vol 46 (5) ◽  
pp. 1443-1451 ◽  
Author(s):  
Y. Yardimci ◽  
A.E. Cetin ◽  
J.A. Cadzow
Keyword(s):  
Gaussian Noise ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Non Gaussian

scholarly journals An improved algorithm for multiple source direction of arrival estimation

2018 ◽  
Vol 173 ◽  
pp. 03028
Author(s):  
Xuecheng Wang ◽  
Jinquan Ma
Keyword(s):  
Gaussian White Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Spatial Spectrum ◽  
Multiple Source ◽  
Measuring Error ◽  
Direction Of Arrival Estimation ◽  
Music Algorithm ◽  
The Matrix ◽  
Improved Algorithm

Based on MUSIC-LIKE algorithm, an improved algorithm for multiple source direction of arrival estimation was proposed to resolve the problem, the enabled estimation number of source for traditional direction of arrival estimation algorithm restricted by the size of the antenna array. The computations and the matrix dimension were reduced by constructed new fourth cumulate matrix, the number of direction estimation was increased by smoothing computation snapshots based on MFOCMUSIC algorithm. The spatial spectrum measuring error was decreased by smoothing the virtual array extended by fourth order cumulate. Simulation results proved that the proposed algorithm can find more direction of arrival than MFOC-MUSIC algorithm, improve the direction of arrival resolution ratio, and inhibit the influence of gaussian white noise to direction of arrival estimation

scholarly journals Application of Sparse Representation to Bartlett Spectra for Improved Direction of Arrival Estimation

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 77
Author(s):  
Jacob Compaleo ◽  
Inder J. Gupta
Keyword(s):  
Sparse Representation ◽  
Regularization Parameter ◽  
Angular Separation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Small Signal ◽  
Music Algorithm ◽  
A New Technique ◽  
Better Than

A new technique for high-resolution direction of arrival estimation is presented. The method utilizes the traditional Bartlett spectra and sparse representation to locate emitters in single and multiple emitter scenarios. A method for selecting the sparse representation regularization parameter is also presented. Using Monte Carlo simulations, we show that the proposed approach achieves accurate direction of arrival (DOA) estimations that are unbiased and a variance that approaches the Cramer–Rao lower bound. We show that our method outperforms the popular MUSIC algorithm, and is slightly better than the sparse representation based L1-SVD algorithm when angular separation between emitters is small, signal SNR is low, and a small number of snapshots are used in DOA estimation.

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