Nested linear array enables to enhance localization resolution and achieve under-determined direction of arrival(DOA)estimation.In this paper,the traditional two-level nested linear array is improved to achieve more d...Nested linear array enables to enhance localization resolution and achieve under-determined direction of arrival(DOA)estimation.In this paper,the traditional two-level nested linear array is improved to achieve more degrees of freedom(DOFs)and better angle estimation performance.Furthermore,a computationally efficient DOA estimation algorithm is proposed.The discrete Fourier transform(DFT)method is utilized to obtain coarse DOA estimates,and subsequently,fine DOA estimates are achieved by spatial smoothing multiple signals classification(SS-MUSIC)algorithm.Compared to SS-MUSIC algorithm,the proposed algorithm has the same estimation accuracy with lower computational complexity because the coarse DOA estimates enable to shrink the range of angle spectral search.In addition,the estimation of the number of signals is not required in advance by DFT method.Extensive simulation results testify the effectiveness of the proposed algorithm.展开更多
In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subar...In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix(CCM) of the subarrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays,and the unitary estimation of the signal parameters via rotational invariance techniques(ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio(SNR) environment.展开更多
基金supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.SJCX18_0103)Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology (No.KF20181915)
文摘Nested linear array enables to enhance localization resolution and achieve under-determined direction of arrival(DOA)estimation.In this paper,the traditional two-level nested linear array is improved to achieve more degrees of freedom(DOFs)and better angle estimation performance.Furthermore,a computationally efficient DOA estimation algorithm is proposed.The discrete Fourier transform(DFT)method is utilized to obtain coarse DOA estimates,and subsequently,fine DOA estimates are achieved by spatial smoothing multiple signals classification(SS-MUSIC)algorithm.Compared to SS-MUSIC algorithm,the proposed algorithm has the same estimation accuracy with lower computational complexity because the coarse DOA estimates enable to shrink the range of angle spectral search.In addition,the estimation of the number of signals is not required in advance by DFT method.Extensive simulation results testify the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China (NSFC) [grant number. 61871414]。
文摘In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix(CCM) of the subarrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays,and the unitary estimation of the signal parameters via rotational invariance techniques(ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio(SNR) environment.
