Sliding innovation filter for micorgrid application

Al-Shabi, Mohammad; Obaideen, Khaled; El-Nadi, A.; Gadsden, S. Andrew; Bonny, Talal

doi:10.1117/12.2664078

Cited by 2 publications

(2 citation statements)

References 61 publications

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“…The Kalman Filter, a seminal algorithm devised by Rudolf Kalman in the 1960s, stands as a cornerstone in the estimation of dynamic systems' states [1][2][3][4][5][6][7][8][9]. Its widespread adoption spans a multitude of disciplines, from control systems to signal processing.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the extended Kalman filter's role in oceanic science

Obaideen,

AlShabi,

Bonny

2024

Ocean Sensing and Monitoring XVI

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This study delves into the Extended Kalman Filter's (EKF) use in ocean science through a detailed bibliometric and text mining examination. Tracing its roots back to the original Kalman Filter from the 1960s, the EKF has become crucial for managing nonlinear dynamics, especially in oceanography. Our analysis, drawing from Scopus data covering 1980-2023, delivers an extensive overview of the EKF's growth, applications, and cross-disciplinary influence in this area. We employed sophisticated bibliometric methods, including Biblioshiny, and text mining approaches via VOSviewer to dissect trends, and thematic groupings in EKF-related ocean science research. The results demonstrate a steady increase in EKF applications, particularly in autonomous underwater vehicle navigation, forecasting ocean currents, and modeling marine ecosystems. The bibliometric findings show its broad interdisciplinary appeal, while the text analysis underscores the EKF's integration with cutting-edge computational techniques and its significance in burgeoning oceanographic technologies. The paper highlights the EKF's indispensable role in ocean science, reflecting its historical importance and versatility in addressing contemporary challenges in marine technology. The study not only sheds light on the EKF's historical and current uses but also suggests potential future directions for research and innovation. It aims to offer crucial insights to researchers, academicians, and policy makers, underlining the EKF's significance in the dynamic, ever-changing realm of ocean science.

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Section: Introductionmentioning

confidence: 99%

Analysis of the extended Kalman filter's role in oceanic science

Obaideen,

AlShabi,

Bonny

2024

Ocean Sensing and Monitoring XVI

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“…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][82][83]…”

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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Sliding innovation filter for micorgrid application

Cited by 2 publications

References 61 publications

Analysis of the extended Kalman filter's role in oceanic science

Analysis of the extended Kalman filter's role in oceanic science

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

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