Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Ciuonzo, Domenico; Javadi, S. Hamed; Mohammadi, Abdolreza; Rossi, Pierluigi Salvo

doi:10.1109/tsipn.2020.3037832

Cited by 38 publications

(18 citation statements)

References 42 publications

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“…Decision-level fusion has good real-time performance and fault tolerance, but its preprocessing cost is high. At present, networkbased signal or information processing often adopts this level of data fusion [36,37].…”

Section: Function Descriptionmentioning

confidence: 99%

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Chen

Liu

et al. 2021

Mathematical Problems in Engineering

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Multisensor data generalized fusion algorithm is a kind of symbolic computing model with multiple application objects based on sensor generalized integration. It is the theoretical basis of numerical fusion. This paper aims to comprehensively review the generalized fusion algorithms of multisensor data. Firstly, the development and definition of multisensor data fusion are analyzed and the definition of multisensor data generalized fusion is given. Secondly, the classification of multisensor data fusion is discussed, and the generalized integration structure of multisensor and its data acquisition and representation are given, abandoning the research characteristics of object oriented. Then, the principle and architecture of multisensor data fusion are analyzed, and a generalized multisensor data fusion model is presented based on the JDL model. Finally, according to the multisensor data generalized fusion architecture, some related theories and methods are reviewed, and the tensor-based multisensor heterogeneous data generalized fusion algorithm is proposed, and the future work is prospected.

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Section: Function Descriptionmentioning

confidence: 99%

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Chen

Liu

et al. 2021

Mathematical Problems in Engineering

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“…In order to further improve the detection performance of the fusion system, a series of new optimal fusion algorithms based on covariance, large deviation analysis, least square fusion rules, and Rao test [8][9][10][11][12] of the distributed detection fusion system are proposed. In recent years, many scholars have introduced a neural network [13], Kalman filter [14][15][16], and (generalized) likelihood ratio (GLRT) [17][18][19][20] into sensor systems to realize signal detection in various fields. All the above researches assume that the noise obeys a certain distribution and lack the research on chaotic noise background combined with phase space reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background

Zhao

et al. 2021

Journal of Sensors

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This paper combines the distributed sensor fusion system with the signal detection under chaotic noise to realize the distributed sensor fusion detection from chaotic background. First, based on the short-term predictability of the chaotic signal and its sensitivity to small interference, the phase space reconstruction of the observation signal of each sensor is carried out. Second, the distributed sensor local linear autoregressive (DS-LLAR) model is constructed to obtain the one-step prediction error of each sensor. Then, we construct a Bayesian risk model and also obtain the corresponding conditional probability density function under each sensor’s hypothesis test which firstly needs to fit the distribution of prediction errors according to the parameter estimation. Finally, the fusion optimization algorithm is designed based on the Bayesian fusion criterion, and the optimal decision rule of each sensor and the optimal fusion rule of the fusion center are jointly solved to effectively detect the weak pulse signal in the observation signal. Simulation experiments show that the proposed method which used a distributed sensor combined with a local linear model can effectively detect weak pulse signals from chaotic background.

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“…Many data processing techniques used in traditional seismological research originated from small datasets and limited computing power. Low-cost MEMS acceleration sensors have been extensively used in the monitoring system of Internet of Things (IoT) over the last few years, because of their low installation and operation costs, the examples include a wireless sensor network (WSN) [6], [7], community seismic network (CSN) [8]. Although they have a great potential to replace the traditional expensive seismic networks whose coverage is hardly dense due to the high installation and operation costs, however, the large noises inherent in a low-cost MEMS acceleration sensor reduce the quality of data recorded [9] , thus a novel approach is needed to adapt to the data with different signal-to-noise ratios (SNR).…”

Section: Introductionmentioning

confidence: 99%

Augmenting Seismic Data Using Generative Adversarial Network for Low-Cost MEMS Sensors

Wu¹,

Shin

Ahn

et al. 2021

IEEE Access

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The performance of a deep learning (DL) model depends on sufficient training datasets and is related to the algorithm structure. Seismological research based on DL suffers from the lack of seismic data recorded by low-cost micro-electro-mechanical systems (MEMS) sensors whose data is usually polluted by different types of noise. Therefore, increasing seismic datasets is necessary for intelligently generating seismic data through data-augmentation techniques. However, it is difficult to characterize and measure the evolution process of seismic sequences, making the feature extraction and data generation of seismic sequences still a significant challenge. By combining the framework of Generative Adversarial Network (GAN) with long short-term memory (LSTM), attention mechanism and neural network (NN), a novel deep generation model (DGM) named EQGAN is developed to overcome the challenges, which can automatically capture the different time histories and dimension characteristics of seismic sequences, meanwhile stably generating high-quality seismic data. The reality of generated data is qualitatively clarified through the analysis of frequency domain and data autocorrelation distribution. Based on the High-throughput Screening (HTS) Theory, the quantitative evaluation index of statistical metrics is designed, and the generation performance of different machine learning models (standard GAN, LSTM, NN) is compared to prove the stability and effectiveness of EQGAN. The experimental results denote that the EQGAN has excellent stability and performance (up to 81%, much higher than that of other generation models), which provides a suitable data expansion approach for the field of seismological research.

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Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Cited by 38 publications

References 42 publications

A New View of Multisensor Data Fusion: Research on Generalized Fusion

A New View of Multisensor Data Fusion: Research on Generalized Fusion

Distributed Sensor Local Linear Fusion Detection of Weak Pulse Signal in Chaotic Background

Augmenting Seismic Data Using Generative Adversarial Network for Low-Cost MEMS Sensors

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