Target Bearing Estimation Using Vortex Electromagnetic Waves with Mutual Coupling

doi:10.12382/bgxb.2022.0480

Abstract

Abstract:

The use of a uniform circular array to generate vortex electromagnetic waves can lead to changes in the radiation pattern of antenna elements due to mutual coupling between adjacent array elements, causing variations in amplitude and phase that affect target azimuth detection performance. To address this problem, the mutual coupling matrix is expanded into the mode domain by Fourier transform using the symmetric circulant of the uniform circular array mutual coupling matrix. This allows for describing the relationship between array element excitation and the number of orbital angular momentum modes. Based on the above analysis, an improved propagation operator decoupling azimuth estimation algorithm based on orbital angular momentum is proposed, which avoids spectral peak search and large eigenvalue decomposition. Simulation results show that the mutual coupling matrix in the mode domain effectively compensates for the influence of the mutual coupling effect. Compared with traditional algorithms, the proposed algorithm boasts lower computational complexity and avoids errors stemming from Bessel function approximations.

Key words: vortex electromagnetic wave, mutual coupling, uniform circular array, propagator method, bearing estimation

CLC Number:

TJ43⁺4.1

ZHANG Hongyun, LI Ping, LI Guolin, JIA Ruili. Target Bearing Estimation Using Vortex Electromagnetic Waves with Mutual Coupling[J]. Acta Armamentarii, 2023, 44(10): 3218-3226.

Figures/Tables 12

Fig.1 UCA array geometry

Fig.2 Phase distribution of different OAM modes of 12 elements in ideal conditions

Fig.3 Schematic of the array mutual coupling feeding network

Fig.4 OAM mode circuit model of the UCA including mutual coupling

Fig.5 Schematic diagram of vortex electromagnetic wave detection

Fig.6 Calibrated and uncalibrated MUSIC spatial spectrums

Table 1 FEKO simulation parameters

参数	数值
载波频率/GHz	24
波长/mm	12.5
阵元数	12
圆阵半径/mm	12.5
偶极子长度/mm	6.25
采样点	2701

Table 2 Computational complexity of different algorithms

算法	计算复杂度
MUSIC算法	O=(2L+1)²K+(2L+1)³+ 360(2L+2)(2L+1-P)
ESPRIT算法	O=(2L+1)²K+(2L+1)³+ 6P²(2L-1)+12P³
PM算法	O=(2L+1)²K+(2L+1)²P+ P²(2L+1)+360(2L+2)(2L+1-P)
OAM-PM算法	O=(2L+1)²K+(2L+1)²P+ P²(2L+1)+6P²(2L-1)+12P³

Fig.7 Computational complexity of different mode numbers

Fig.8 Computational complexity of different snapshots

Fig.9 Dependence of root mean square error on signal-to-noise ratio

Fig.10 Dependence of root mean square error on snapshots

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