Hourglass arrays: Planar sparse arrays with hole-free coarrays and reduced mutual coupling

2017 ◽  
Vol 141 (5) ◽  
pp. 3842-3842
Author(s):  
Chun-Lin Liu ◽  
Palghat Vaidyanathan
Keyword(s):  
Mutual Coupling ◽  
Sparse Arrays

scholarly journals Thinning and Weighting of Planar/Conformal Arrays Considering Mutual Coupling Effects

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
You-Feng Cheng ◽  
Wei Shao ◽  
Ran Zhang ◽  
Xiao Ding ◽  
Meng-Xia Yu
Keyword(s):  
Mutual Coupling ◽  
Differential Evolution Algorithm ◽  
Far Field ◽  
Field Calculation ◽  
Sidelobe Suppression ◽  
Sparse Arrays ◽  
Low Sidelobe ◽  
Key Points ◽  
Element Pattern ◽  
Synthesis Approach

Based on an improved active element pattern (AEP) technique, a novel effective method for sidelobe suppression considering mutual coupling (MC) in planar and conformal sparse arrays is proposed in this paper. A thinning and weighting process that includes the thinning module, optimization module, and far-field calculation module is presented, and three key points, namely, the modified AEP modeling, far-field calculation of planar and conformal thinned arrays, and modified thinning strategy, are highlighted. As an effective optimization algorithm, the differential evolution algorithm (DEA) is adopted in order to achieve low sidelobe. Several numerical examples are shown to validate the consistency and effectiveness of the proposed synthesis approach. With the first use of the AEP technique for the synthesis of sparse arrays, the planar and conformal microstrip arrays with the desired array filling factor are studied to obtain the expected sidelobe level (SLL).

scholarly journals COPRIME ARRAY PARAMETERS OPTIMIZATION FOR DOA ESTIMATION

2021 ◽  
Vol 4 (2) ◽  
pp. 23-32
Author(s):  
Fatimah A. Salman ◽  
Bayan M. Sabbar
Keyword(s):  
Mutual Coupling ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Parameters Optimization ◽  
Degree Of Freedom ◽  
Direction Of Arrival Estimation ◽  
Sparse Arrays ◽  
Sparse Array ◽  
Simulation Results ◽  
Coprime Array

Sparse array such as the coprime array is one of the most preferable sparse arrays for direction of arrival estimation due to its properties, like simplicity, capability of resolving more sources than the number of elements and resistance to mutual coupling issue.  In this paper, a new coprime array model is proposed to increase the number of degree of freedom (DOF) and improve the performance of coprime array.   The new designed array can avoid the mutual coupling by minimizing the lag redundancy and expand the central lags in the virtual difference co-array. Thus, the propose structure can resolve more sources than the prototype coprime array using the same number of elements with improved direction of arrival estimation. Simulation results demonstrate that the proposed array model is more efficient than the others coprime array model.

scholarly journals DOA Estimation of an Enhanced Generalized Nested Array with Increased Degrees of Freedom and Reduced Mutual Coupling

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yule Zhang ◽  
Guoping Hu ◽  
Junpeng Shi ◽  
Hao Zhou ◽  
Chenghong Zhan ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Superior Performance ◽  
Sparse Arrays ◽  
Single Antenna ◽  
Simulation Results ◽  
The Difference ◽  
Low Degrees ◽  
Special Case

Aiming at low degrees of freedom (DOF) and high mutual coupling (MC) of the existing sparse arrays, an enhanced generalized nested array (EGNA) is proposed in this paper. Specifically, the proposed array adds a single antenna on the basis of generalized nested array (GNA), and the difference of coprime factors is employed as the spacing between the second subarray and the additional antenna. Then, the values of the coprime factors are analyzed in detail, which indicates that Yang-NA can be explained as a special case. Compared with the majority of the existing sparse arrays, EGNA not only has the closed-form expressions of the physical antenna locations, consecutive lags, and unique lags, but also significantly increases DOF and reduces MC. In view of the above advantages, EGNA can obtain superior performance in direction of arrival (DOA) estimation. Numerical simulation results verify the rationality and superiority of the proposed nested array.

scholarly journals Sparse Linear Antenna Arrays: A Review

2021 ◽  
Author(s):  
Ashish Patwari
Keyword(s):  
Antenna Arrays ◽  
Communication Systems ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Future Research ◽  
Linear Arrays ◽  
Sparse Arrays ◽  
The Past ◽  
Hole Arrays ◽  
Nested Arrays

Linear sparse antenna arrays have been widely studied in array processing literature. They belong to the general class of non-uniform linear arrays (NULAs). Sparse arrays need fewer sensor elements than uniform linear arrays (ULAs) to realize a given aperture. Alternately, for a given number of sensors, sparse arrays provide larger apertures and higher degrees of freedom than full arrays (ability to detect more source signals through direction-of-arrival (DOA) estimation). Another advantage of sparse arrays is that they are less affected by mutual coupling compared to ULAs. Different types of linear sparse arrays have been studied in the past. While minimum redundancy arrays (MRAs) and minimum hole arrays (MHAs) existed for more than five decades, other sparse arrays such as nested arrays, co-prime arrays and super-nested arrays have been introduced in the past decade. Subsequent to the introduction of co-prime and nested arrays in the past decade, many modifications, improvements and alternate sensor array configurations have been presented in the literature in the past five years (2015–2020). The use of sparse arrays in future communication systems is promising as they operate with little or no degradation in performance compared to ULAs. In this chapter, various linear sparse arrays have been compared with respect to parameters such as the aperture provided for a given number of sensors, ability to provide large hole-free co-arrays, higher degrees of freedom (DOFs), sharp angular resolutions and susceptibility to mutual coupling. The chapter concludes with a few recommendations and possible future research directions.

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