Dissertation > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Communication theory > Information Theory > Signal detection and estimation

Coherent Source DOA Estimation Based on a Single Snapshot of the Uniform Circular Array

Author DingJunJun
Tutor ZhangXuXiang
School Nanjing University of Posts and Telecommunications
Course Electromagnetic Field and Microwave Technology
Keywords Uniform circular array Uniform circular array rotation invariant subspace algorithms Uniform circular array to finding roots Multiple signal classification algorithm Coherent Source Single Snapshot
CLC TN911.23
Type Master's thesis
Year 2012
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Spatial spectrum estimation algorithm is a method used to estimate the spatial signal reaches the receiver array to wave direction (DOA) DOA estimation of the array signal processing field an extremely important application . Need to estimate the direction of arrival of the signal are in the process of radar , sonar , wireless communications and signal processing . On the basis of the model of uniform circular array (UCA) signal reception , combined with uniform circular array rotation invariant subspace (UCA-ESPRIT) algorithm and uniform circular array root finding multiple signal classification (UCA-ROOT-MUSIC) algorithm , on the single uniform circular array snapshots coherent source DOA estimation algorithm exploratory research . First , the use of phase mode excitation array element space uniform circular array (UCA) conversion the pattern space uniform linear array , that is a virtual uniform linear array ( VULA ) . Next, the data vector obtained by the virtual linear array (VULA) Hermitian Toeplitz matrix reconstruction , after reconstitution of the matrix is used to restore the dimension of the signal space and noise space addition to the source correlation . Finally, the use of TLS-ESPRIT algorithm or ROOT-MUSIC algorithm to separate the signal space and noise space , thus correctly estimate the direction of arrival of the signal . The the article proposed algorithm does not require against $ subarray division does not need subarray covariance matrix before / after to smooth space , thus reducing the computational complexity . Simulation results prove that the article mentioned algorithms and use the 100 snapshots traditional spatial smoothing class algorithm similar performance , but the article mentioned algorithm uses only a snapshot algorithm to calculate the amount is very small .

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