Mixing Matrix Estimation Algorithm for Time-Varying Radar Signals in a Dynamic System Under UBSS Model

Bai, Xiaowei; Fu, Wei; Zhou, Chunhua; Liu, Yongyuan

doi:10.1007/s00034-020-01614-4

Cited by 3 publications

(10 citation statements)

References 28 publications

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“…Fig. 10 shows the simulation results of the proposed algorithm and the algorithm in [7] under different SNR when the number of source signals is 5.…”

Section: B Simulation Of the Proposed Estimation Algorithm Of The Number Of Source Signals Based On Element Sortingmentioning

confidence: 99%

“…10, it can be seen that the performance of the proposed algorithm is better than that of the reference algorithm in the whole SNR range. Compared with the algorithm in [7], the proposed algorithm This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”

Section: B Simulation Of the Proposed Estimation Algorithm Of The Number Of Source Signals Based On Element Sortingmentioning

confidence: 99%

“…Content may change prior to final publication. The algorithm in [7] The proposed algorithm has higher estimation accuracy of the number of source signals.…”

Section: B Simulation Of the Proposed Estimation Algorithm Of The Number Of Source Signals Based On Element Sortingmentioning

confidence: 99%

“…When using clustering algorithm to do clustering analysis, AHC algorithm can effectively avoid the problem of clustering error caused by the selection error of initial clustering center. Compared with the traditional Fuzzy C-means (FCM) algorithm [7] and Kmeans algorithm [14], the accuracy and robustness of AHC algorithm are higher. The experiment is performed 100 times under different SNRs from 5 dB to 30 dB.…”

Section: Simulation Of Different Mixing Matrix Estimation Algorithmsmentioning

confidence: 99%

“…At present, two-step method is the main method to solve UBSS problem. The first step is to estimate the mixing matrix, and the second step is to recover the source signals according to the obtained mixing matrix [7]. There is no doubt that the estimation accuracy of mixing matrix directly affects the recovery results of source signals.…”

Section: Introductionmentioning

confidence: 99%

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Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Bai

Shi³

et al. 2021

IEEE Access

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An original algorithm for underdetermined mixing matrix estimation is proposed in this paper, which can estimate the mixing matrix effectively when the number of source signals is unknown. Firstly, a new single source point (SSP) detection algorithm based on transformation matrix is proposed, which can effectively detect single source time-frequency (TF) points by using the characteristics of complex ratio and improve the sparsity of source signals. In view of the fact that the number of source signals is unknown, a novel estimation algorithm based on element sorting is proposed, which can significantly improve the estimation accuracy of the number of source signals. Finally, the mixing matrix is estimated by using the cluster center obtained by agglomerative hierarchical clustering (AHC) algorithm. The simulation results show that the proposed algorithm can improve the estimation accuracy of the number of source signals and underdetermined mixing matrix obviously, and the algorithm has higher robustness compared with other algorithms.

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“…Fig. 10 shows the simulation results of the proposed algorithm and the algorithm in [7] under different SNR when the number of source signals is 5.…”

Section: B Simulation Of the Proposed Estimation Algorithm Of The Number Of Source Signals Based On Element Sortingmentioning

confidence: 99%

Section: B Simulation Of the Proposed Estimation Algorithm Of The Number Of Source Signals Based On Element Sortingmentioning

confidence: 99%

“…Content may change prior to final publication. The algorithm in [7] The proposed algorithm has higher estimation accuracy of the number of source signals.…”

Section: B Simulation Of the Proposed Estimation Algorithm Of The Number Of Source Signals Based On Element Sortingmentioning

confidence: 99%

Section: Simulation Of Different Mixing Matrix Estimation Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Bai

Shi³

et al. 2021

IEEE Access

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A Single-Channel Blind Separation Convolutional Network Combined with Attention Mechanism for Communication Signals

Fu,

Zhao,

Zhang

2023

Circuits Syst Signal Process

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Separation of Spacecraft Noise From Geomagnetic Field Observations Through Density‐Based Cluster Analysis and Compressive Sensing

Hoffmann

Moldwin

2022

JGR Space Physics

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Spacecraft equipped with magnetometers can be used to capture in situ measurements of magnetic phenomena in the geospace environment. These measurements are necessary to answer key questions about the nature of the Earth's magnetosphere and its interaction with interplanetary magnetic fields. Understanding how the heliosphere directs the flow of energy, mass, and momentum between the Sun and Earth is critical for applications such as space weather modeling, space exploration, and climate science. A number of missions use spacecraft equipped with magnetometers to measure magnetic fields. For example, The European Space Agency's SWARM mission uses a constellation of three satellites to provide high fidelity magnetic field measurements used to model the Earth's magnetic field and study the Earth's dynamo (Fratter et al., 2016). Magnetometers provide invaluable data for space science research, however, the quality of the data are often limited by magnetic noise generated by the spacecraft. Electrical systems onboard a spacecraft generate stray magnetic fields that interfere with magnetic field measurements. The strength of magnetic fields in the geospace environment ranges several orders of magnitude with natural phenomena such as the interplanetary magnetic field occurring on the order of 6 nT to the Earth's magnetosphere in low-Earth orbit measuring on the order of 60,000 nT. Spacecraft subsystem magnetic fields may completely eclipse the perturbations in natural magnetic fields which are of interest to understanding waves and currents in the solar wind and magnetosphere. The presence of these stray magnetic fields is a significant obstacle for missions that utilize magnetic field data (Ludlam et al., 2009;Russell, 2004).

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Mixing Matrix Estimation Algorithm for Time-Varying Radar Signals in a Dynamic System Under UBSS Model

Cited by 3 publications

References 28 publications

Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

Mixing Matrix Estimation Algorithm for Underdetermined Blind Source Separation

A Single-Channel Blind Separation Convolutional Network Combined with Attention Mechanism for Communication Signals

Separation of Spacecraft Noise From Geomagnetic Field Observations Through Density‐Based Cluster Analysis and Compressive Sensing

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