Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

Jung, Kyu Sik; Min, Sawon; Kim, Jeongwoo; Kim, Nammoon; Kim, Euntai

doi:10.1109/access.2021.3071515

Cited by 2 publications

(1 citation statement)

References 55 publications

(102 reference statements)

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“…The proposed controller formed the conventional multiloop structure including disturbance observers for each loop. Jung et al [19] proposed a compound method for target classification based on evidence theory and the fuzzy logic method to achieve target localization by fusing data obtained from cameras and radar sensors. Ye et al [20] proposed a two-stage Kalman filter algorithm, which employed two intertwined filters for channel tracking, position tracking, and abrupt channel change detection.…”

Section: Introductionmentioning

confidence: 99%

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Wang

Fan

2022

Sustainability

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With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the mapping relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shijingshan District, Beijing is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model.

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Section: Introductionmentioning

confidence: 99%

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Wang

Fan

2022

Sustainability

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A multi-sensor fault diagnosis method for rotating machinery based on improved fuzzy support fusion and self-normalized spatio-temporal network

Yang,

Gao,

Zhang

et al. 2023

Meas. Sci. Technol.

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Recently, the fault diagnosis of rotating machinery based on deep learning has achieved increasingly widespread applications. However, it is often difficult to achieve the expected results by relying on a single sensor due to the limited information obtained by the single sensor and the susceptibility to the influence of the additive noise. To address the above problems, this paper proposes a multi-sensor fusion fault diagnosis method for rotating machinery based on improved fuzzy support fusion and self-normalized spatio-temporal network to enhance feature learning while achieving multi-sensor data fusion. This method includes a data pre-processing module, a fusion module and a fault recognition module. In the first module, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is introduced to decompose and reconstruct the multi-source sensor signals, thereby reducing the impact of environmental noise on data quality. In the fusion module, a data fusion algorithm based on improved fuzzy support is designed to achieve the data-level fusion of multi-source sensors. By introducing the self-normalized properties into the convolutional structure with bi-directional gated recurrent unit (BiGRU), a self-normalized spatio-temporal network is designed in the fault recognition module to perform the fault diagnosis of rotating machinery. The experimental results show that the proposed method can achieve high quality data-level fusion and outperforms the state-of-the-art fault diagnosis methods in terms of fault classification.

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Evidence-Theoretic Reentry Target Classification Using Radar: A Fuzzy Logic Approach

Cited by 2 publications

References 55 publications

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion

A multi-sensor fault diagnosis method for rotating machinery based on improved fuzzy support fusion and self-normalized spatio-temporal network

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