Dimension decomposition algorithm for multiple source localization using uniform circular array

doi:10.23919/JSEE.2023.000016

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (3): 650-660.doi: 10.23919/JSEE.2023.000016

• ELECTRONICS TECHNOLOGY • Previous Articles

Dimension decomposition algorithm for multiple source localization using uniform circular array

Xiaolong SU¹(), Panhe HU¹^,*(), Zhenhua WEI²(), Zhen LIU¹(), Junpeng SHI¹(), Xiang LI¹()

¹ College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
² Combat Support College, Rocket Force University of Engineering, Xi’an 710025, China

Received:2021-03-19 Online:2023-06-15 Published:2023-06-30
Contact: Panhe HU E-mail:suxiaolong_nudt@163.com;hupanhe13@nudt.edu.cn;wzh016001@aliyun.com;zhen_liu@nudt.edu.cn;shijunpeng20@nudt.edu.cn;lixiang01@vip.sina.com
About author:
SU Xiaolong was born in 1994. He received his M.S. degree from National University of Defense Technology, Changsha, China, in 2018. He is currently pursuing his Ph.D. degree in National University of Defense Technology. His research interests include array signal processing and deep learning. E-mail: suxiaolong_nudt@163.com

HU Panhe was born in 1991. He received his Ph.D. degree from National University of Defense Technology, Changsha, China, in 2019. He is an associate professor in National University of Defense Technology. His research interests include radar system design, array signal processing, and deep learning. E-mail: hupanhe13@nudt.edu.cn

WEI Zhenhua was born in 1983. He received his M.S. degree from National University of Defense Technology, Changsha, China, in 2006. He is an associate professor in Rocket Force University of Engineering. His research interests include anti-jamming and wireless communication.E-mail: wzh016001@aliyun.com

LIU Zhen was born in 1983. He received his Ph.D. degree from National University of Defense Technology, Changsha, China, in 2013. He is a professor in National University of Defense Technology. His research interests include radar target recognition and countermeasures, array signal processing and machine learning.E-mail: zhen_liu@nudt.edu.cn

SHI Junpeng was born in 1988. He received his Ph.D. degree from Air Force Engineering University, Xi’an, China, in 2018. He is an associate professor in National University of Defense Technology. His research interest is tensor signal processing with sparse MIMO radar.E-mail: shijunpeng20@nudt.edu.cn

LI Xiang was born in 1967. He received his Ph.D. degree from National University of Defense Technology, Changsha, China, in 1998. He is a professor in National University of Defense Technology. His research interests include target recognition, signal detection, and radar imaging.E-mail: lixiang01@vip.sina.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62022091;61921001).

Abstract

Abstract:

A dimension decomposition (DIDE) method for multiple incoherent source localization using uniform circular array (UCA) is proposed. Due to the fact that the far-field signal can be considered as the state where the range parameter of the near-field signal is infinite, the algorithm for the near-field source localization is also suitable for estimating the direction of arrival (DOA) of far-field signals. By decomposing the first and second exponent term of the steering vector, the three-dimensional (3-D) parameter is transformed into two-dimensional (2-D) and one-dimensional (1-D) parameter estimation. First, by partitioning the received data, we exploit propagator to acquire the noise subspace. Next, the objective function is established and partial derivative is applied to acquire the spatial spectrum of 2-D DOA. At last, the estimated 2-D DOA is utilized to calculate the phase of the decomposed vector, and the least squares (LS) is performed to acquire the range parameters. In comparison to the existing algorithms, the proposed DIDE algorithm requires neither the eigendecomposition of covariance matrix nor the search process of range spatial spectrum, which can achieve satisfactory localization and reduce computational complexity. Simulations are implemented to illustrate the advantages of the proposed DIDE method. Moreover, simulations demonstrate that the proposed DIDE method can also classify the mixed far-field and near-field signals.

Key words: source localization, parameter estimation, uniform circular array (UCA), propagator, partial derivative, least squares (LS)

Xiaolong SU, Panhe HU, Zhenhua WEI, Zhen LIU, Junpeng SHI, Xiang LI. Dimension decomposition algorithm for multiple source localization using uniform circular array[J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 650-660.

Figures/Tables 8

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Algorithm	Computational complexity
DIDE	$\begin{gathered} O({K^2}M + KMN + ({M^2} + 2M)(M - K) + (360 \times 90/{{\Delta } }_{ {\text{DOA} } }^2) \cdot \\[-5pt] ({M^2} + 10M) + 13M) \end{gathered}$
TSMUSIC	$\begin{gathered} O(M\left( {N - 1} \right)/2 + {M^2}N + 2{M^3} + (360 \times 90/{{\Delta } }_{ {\text{DOA} } }^2)(M + 2{M^2})+ \\[-5pt] K((8{R^2}/\lambda - 0.62{(8{R^3}/\lambda )^{1/2} })/{ {{\Delta } }_{ {\text{range} } } })(M + 2{M^2})) \end{gathered}$
SOS-based	$\begin{gathered} O({M^2}N + {M^3} + 2{K^2}M(360 \times 90/{{\Delta } }_{ {\text{DOA} } }^2) + \\[-5pt] K((8{R^2}/\lambda - 0.62{(8{R^3}/\lambda )^{1/2} })/{ {{\Delta } }_{ {\text{range} } } })(M + 2{M^2})) \end{gathered}$
Phase-based	$ O\left( {KNM + 20KM} \right) $

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Dimension decomposition algorithm for multiple source localization using uniform circular array

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