Sparse Arrays Method with Minimum Variance for High Quality Image in Ultrasound Ultrafast Imaging

Gomes, Danilo Fernandes; Zimbico, Acácio José; Maia, Joaquim Miguel; Neves, Larissa Comar; Assef, Amauri Amorin; Schneider, Fábio Kurt; Costa, Eduardo Tavares

doi:10.1109/sensors43011.2019.8956867

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Subsequently, there are many high-resolution estimation methods that extend nonlinear signal processing technology to spatial domain. There are Burg's maximum entropy method (MEM), Pisarenko's method, and Capon's minimum variance method (MVM) [8][9][10]. All these methods have a common premise.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Parametric Spatial Spectrum Estimation in Internet of Things

Liu

Zhao

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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When massive numbers of wireless IoT devices are being deployed, cognitive spectrum management is critical to satisfy the explosive broadband requirements of IoT applications. Heterogeneous the target of the spatial spectrum estimation which is part of array signal processing is to achieve the evaluation of space signal parameters and source location, which result in that the spatial spectrum estimation becomes the most basic content of the array signal processing. It needs a method by which the large dimensional array still has its consistency. Therefore, this paper studies an improved large dimensional array parameterized spatial spectrum estimation method based on Pisarenko method, named G-Pisarenko method. Firstly, an improved estimator about the logarithm of the covariance matrix of a certain bilinear form is analyzed which is based on the theory of large dimension random matrix. We can find out a relatively better method, i.e., MW method. The method will become the primitive method for us to improve. Then, aimed at the relating covariance matrix in MW, we use an improved large dimensional array estimation method which can improve the logarithm of the covariance matrix estimation. Finally, we compare the improved method and the original method by simulation, and it can be seen the clear advantage of G-Pisarenko method when the sample number and observed dimensions are in the same order of magnitude.

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Section: Introductionmentioning

confidence: 99%