Blind DOA estimation using higher order cyclostationarity and linear prediction in multipath environment

2005 ◽  
Author(s):  
Haijun Lu ◽  
Shuxun Wang ◽  
Hong Jiang
Keyword(s):  
Linear Prediction ◽  
Doa Estimation ◽  
Higher Order ◽  
Multipath Environment

2D DOA Estimation Using Sparse Representation of Higher-Order Power of Covariance Matrix

2014 ◽  
Vol 998-999 ◽  
pp. 779-783
Author(s):  
Zheng Luo ◽  
Fei Yu ◽  
Lin Wu ◽  
Yuan Liu
Keyword(s):  
Covariance Matrix ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Higher Order ◽  
Eigenvalue Decomposition ◽  
Coherent Signals ◽  
Sparse Decomposition ◽  
Sparse Signal Representation ◽  
2D Doa Estimation ◽  
Source Signals

A novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm utilizing a sparse signal representation of higher-order power of covariance matrix is proposed. Through applying the higher-order power of covariance matrix to construct a new sparse decomposition vector, this algorithm avoids the estimation of incident signal number and eigenvalue decomposition. And the hierarchical granularity-dictionary is studied, which forms the over-complete dictionary adaptively in the light of source signals’ distribution. Compared with MUSIC and L1-SVD, this algorithm not only provides a better 2D DOA performance but also possesses the capability of coherent signals estimation. Theoretical analysis and simulation results demonstrate the validity and robust of the proposed algorithm.

Linear Prediction-Based DOA Estimation for Directional Borehole Radar 3-D Imaging

2019 ◽  
Vol 57 (8) ◽  
pp. 5493-5501 ◽  
Author(s):  
Sixin Liu ◽  
Wentian Wang ◽  
Lei Fu ◽  
Qi Lu
Keyword(s):  
Linear Prediction ◽  
Doa Estimation ◽  
Borehole Radar

scholarly journals DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Panhe Hu ◽  
Qinglong Bao ◽  
Zengping Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Bistatic Radar ◽  
Spatial Difference ◽  
Coherent Signals ◽  
Vhf Radar ◽  
Multipath Environment ◽  
Passive Bistatic Radar

Direction-of-arrival (DOA) estimation in multipath environment is an important issue for passive bistatic radar (PBR) using frequency agile phased array VHF radar as illuminator of opportunity. Under such scenario, the main focus of this paper is to cope with the closely spaced uncorrelated and coherent signals in low signal-to-noise ratio and limited snapshots. Making full use of the characteristics of moduli of eigenvalues, the DOAs of the uncorrelated signals are firstly estimated. Afterwards, their contributions are eliminated by means of spatial difference technique. Finally, in order to improve resolution and accuracy DOA estimation of remaining coherent signals while avoiding the cross-terms effect, a new beamforming solution based iterative adaptive approach (IAA) is proposed to deal with a reconstructed covariance matrix. The proposed method combines the advantages of both spatial difference method and the IAA algorithm while avoiding their shortcomings. Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability, and estimation accuracy are demonstrated by comparison with the existing methods.

scholarly journals DOA Estimation for Mixed Uncorrelated and Coherent Sources in Multipath Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Heping Shi ◽  
Wen Leng ◽  
Anguo Wang ◽  
Tongfeng Guo
Keyword(s):  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Uniform Linear Array ◽  
Performance Simulation ◽  
Coherent Sources ◽  
Multipath Environment ◽  
Simulation Results ◽  
Effectiveness And Efficiency

A novel direction-of-arrival (DOA) estimation method is proposed to cope with the scenario where a number of uncorrelated and coherent narrowband sources simultaneously impinge on the far-field of a uniform linear array (ULA). In the proposed method, the DOAs of uncorrelated sources are firstly estimated by utilizing the property of the moduli of eigenvalues of the DOA matrix. Afterwards, the contributions of uncorrelated sources and the interference of noise are eliminated completely by exploiting the improved spatial differencing technique and only the coherent components remain in the spatial differencing matrix. Finally, the remaining coherent sources can be resolved by performing the improved spatial smoothing scheme on the spatial differencing matrix. The presented method can resolve more number of sources than that of the array elements and distinguish the uncorrelated and coherent sources that come from the same direction as well as improving the estimation performance. Simulation results demonstrate the effectiveness and efficiency of the proposed method.

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