Robust adaptive beamforming via coprime coarray interpolation

Zheng, Zhi; Yang, Tong; Wang, Wen-Qin; Zhang, Shunsheng

doi:10.1016/j.sigpro.2019.107382

Cited by 64 publications

(17 citation statements)

References 51 publications

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“…In sparse arrays, beamforming is usually performed in the co-array domain [102][103][104]. The weights of the virtual sensors in the coarray are adjusted to obtain the desired beam pattern.…”

Section: Future Scopementioning

confidence: 99%

Sparse Linear Antenna Arrays: A Review

Patwari¹

2022

Antenna Systems

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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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Section: Future Scopementioning

confidence: 99%

Sparse Linear Antenna Arrays: A Review

Patwari¹

2022

Antenna Systems

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“…One means of controlling the beampatterns is to instead adaptive beamforming by robust adaptive beamforming. Various robust beamforming algorithms [22–30] have been proposed. The DL method [22, 23, 27] is one of the most popular methods for improving the robustness of the adaptive beamformer.…”

Section: Spectral Characteristics and Robust Low‐slidelobe Target Smentioning

confidence: 99%

Robust low‐range‐sidelobe target synthesis for airborne FDMA–MIMO STAP radar

Zhao

Suo

et al. 2020

IET Radar, Sonar & Navigation

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“…Insufficient number of sampling snapshots, element position errors, and different array models will cause model mismatch. As long as there is an error between the true value and the prior value, especially when the desired signal component exists in the sampling covariance matrix (SCM), the performance of the beamformer will be seriously degraded [5,6]. erefore, improving the robustness and efficiency of adaptive beamforming under different SA conditions and making it resistant to prior information is an important research direction of current beamforming.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming Algorithm for Sparse Array Based on Covariance Matrix Reconstruction Technology

Cui

Wang

et al. 2022

International Journal of Antennas and Propagation

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When the array structure of the sparse arrays (SA) cannot be determined, the existing beamforming algorithms designed according to specific formations such as coprime arrays (CA), nested arrays (NA), etc. will fail. To solve this problem, we propose two algorithms that are suitable for a variety of SA. In the first method, assuming that the desired signal is a non-Gaussian signal, the desired signal direction vector (DSDV) is estimated using the fourth-order cumulant, and then the interference plus noise covariance matrix (INCM) is reconstructed by integrating the area outside the desired signal. When the desired signal is a Gaussian signal, we propose the second method. The second method estimates the power and direction of arrival (DOA) of the signals by performing eigenvalue decomposition on the sampled covariance matrix (SCM) and finally calculates the weight vector. However, this method needs to estimate the DOA of the signals, so it has certain requirements for the SA structure design. The simulation results show that the proposed method has good performance and strong robustness under different SA.

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Robust adaptive beamforming via coprime coarray interpolation

Cited by 64 publications

References 51 publications

Sparse Linear Antenna Arrays: A Review

Sparse Linear Antenna Arrays: A Review

Robust low‐range‐sidelobe target synthesis for airborne FDMA–MIMO STAP radar

Robust Adaptive Beamforming Algorithm for Sparse Array Based on Covariance Matrix Reconstruction Technology

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