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BD-CVSA: A Broadband Direction Finding Method Based on Constructing Virtual Sparse Arrays.
- Source :
-
Signal Processing . Nov2023, Vol. 212, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- • Phase ambiguity problem limits the development of broadband direction finding. • Constructing virtual arrays with different aperture sizes can solve the problem. • Multi-layer convolutional operation can construct virtual arrays with different sizes. • Error backpropagation algorithm can optimize the combination of virtual arrays. As the electromagnetic environment becomes more complex, radio direction finding systems need to cover a wide frequency range. However, the phase ambiguity of high frequency limits the development of broadband direction finding technology. The existing methods in solving the phase ambiguity problem heavily rely on designing the precise actual sparse array structure by theoretical derivation, which is expensive and time-consuming. To solve this problem, we propose a broadband direction finding method based on constructing virtual sparse arrays. First, we perform the convolutional operation on the direction feature matrix to achieve the weighted summation of the original baselines, which generates numerous virtual array elements and virtual baselines with different directions. The virtual array elements are combined to form virtual sparse arrays with different aperture sizes. Second, we optimize the parameters through the error backpropagation algorithm and obtain the optimal combination of virtual sparse arrays, which eliminates the phase ambiguity. Meanwhile, the virtual sparse arrays can maintain the advantages of sparse arrays, which can accomplish high-precision broadband direction finding. The experimental results demonstrate that the proposed method can solve the phase ambiguity problem effectively. It has high accuracy and strong robustness in simulation environment. [ABSTRACT FROM AUTHOR]
- Subjects :
- *AMBIGUITY
*PROBLEM solving
Subjects
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 212
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 169752334
- Full Text :
- https://doi.org/10.1016/j.sigpro.2023.109160