Gaussian Process Flow and physical model fusion driven fatigue evaluation model using Kalman Filter

Liang, Tianyou; Yin, Aijun; Pan, Mengchun; Chen, Dixiang

doi:10.1016/j.ijfatigue.2022.107182

Cited by 4 publications

(1 citation statement)

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“…This improvement is not a little one but a huge step forward, making possible the detailed exploration of physical phenomena with the help of polarization properties. KF's importance lies in various sectors like environmental monitoring [12][13][14][15][16][17][18][19][20][21][22][23][24][25], weather forecast [26][27][28][29][30][31][32][33][34][35][36][37], defense surveillance [38][39][40][41][42][43][44][45][46][47], and autonomous navigation systems . Its capacity to draw clear information even from messy real-world data improving the precision and reliability of measurements has made it a key tool for boosting real-time decision-making abilities [71][72][73][74][75][76][77][78][79][80][81]…”

Section: Introductionmentioning

confidence: 99%

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

Obaideen,

AlShabi,

Gadsden

et al. 2024

Polarization: Measurement, Analysis, and Remote Sensing XVI

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This paper presents a comprehensive study on the evolution, applications, and impact of the Polarimetric Kalman Filter (PKF) in the fields of signal processing and remote sensing. By employing a methodological framework that integrates literature collection, screening and selection, bibliometric analysis, and synthesis, we aim to elucidate the PKF's contributions and potential research trajectories within its applications. Our findings highlight the PKF's significant role in enhancing data assimilation, improving predictive accuracy, and refining measurement techniques across various domains, particularly in meteorological research and environmental monitoring. The analysis reveals the interdisciplinary nature of PKF applications and its capability to merge theoretical advancements with practical implementations, underlining the importance of sophisticated signal processing algorithms in interpreting complex environmental data. The study also identifies emerging trends, such as the integration of PKF with machine learning and artificial intelligence, indicating future directions for research that promise to push the boundaries of current methodologies and applications. This paper underscores the PKF's pivotal role in advancing signal processing and remote sensing technologies, offering insights into its continuing development and the expanding scope of its applications.

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