Abstract
In order to solve the problem that the subspace-like direction of arrival (DOA) estimation performs poor due to the error of sources number, this paper proposes a new super-resolution DOA estimation algorithm based on the diagonal-symmetric loading (DSL). Specifically, orthogonality principle of the minimum eigenvector of the specific covariance matrix and the source number estimation based on the improved K-means method were adopted to construct the spatial spectrum. Then, by considering the signal-to-interference-to-noise ratio (SINR), the theoretical basis for selecting parameters was given and verified by numerical experiment. To evaluate the effectiveness of the proposed algorithm, this paper compared it with the methods of minimum variance distortionless response (MVDR) and new signal subspace processing (NSSP). Experimental results prove that the proposed DSL has higher resolution and better estimation accuracy than the MVDR and NSSP.
Highlights
(1) A new super-resolution direction of arrival estimation algorithm based on the diagonal-symmetric loading is proposed (2) e source number estimation is based on the improved K-means method (3) By considering the signal-to-interference-to-noise ratio (SINR), the theoretical basis for selecting parameters in the proposed algorithm is described e paper is organized as follows
Data Model. e article assumes that the independent narrowband signals (S1, S2, . . ., Sk) are incident on the M-element uniform linear array (ULA) from θ1, θ2, · · ·, θK, which has been shown in Figure 1. e numbers of signal K are less than the numbers of array elements M, and the element spacing d of the uniform linear array is half a wavelength
When the signal-to-noise ratio (SNR) is greater than 7 dB, the root mean square error (RMSE) of the algorithm decreases sharply, which means that when the minimum variance distortionless response (MVDR) algorithm is less than 7 dB, the two targets of 0° and 10° cannot be distinguished. e reason is that the decrease of SNR can make direction of arrival (DOA) estimation more difficult
Summary
Notations: this paper uses E[·] to represent the expected operation, [·]T and [·]H represent the transpose and conjugate transpose, respectively, and rank (·) represents the rank operation. It can be seen that suppressing the disturbance of the small eigenvalues corresponding to the noise subspace can reduce the sidelobes and improve the DOA resolution performance and reduce the expected signal component. E signal-to-interference-to-noise ratio (SINR) in the covariance matrix R after diagonal-symmetric loading (DSL). When θ θ0, the expected signal component and interference component in the covariance matrix are increased by β times simultaneously, the SINR ratio in equation (15) will become larger, and the peak can be obtained well. (i) When the value of β is very small, only the diagonal loading part is considered temporarily; when α ⟶ − 1, theoretically, the small eigenvalue disturbance corresponding to the noise subspace can be suppressed, and an effective peak can be formed. It can be seen from the above analysis that a suitable α can increase the resolving power of the main peak, and increasing the value of β reasonably can improve the robustness of the algorithm. e relationship between α and β is further discussed in Section 3 of this article
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