Fundamentals of Kalman Filtering: A Practical Approach, Fourth Edition

Musoff, Howard; Zarchan, Paul

doi:10.2514/4.102776

Cited by 44 publications

(2 citation statements)

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“…Machine learning, predictive analytics, or statistical learning represents a dynamic and ever-evolving research field situated at the fascinating crossroads of statistics, artificial intelligence, and computer science [43]. This interdisciplinary domain serves as a veritable powerhouse, equipping us with the essential tools required to extract knowledge and make predictions when faced with a deluge of structured or unstructured data [44]. To truly appreciate the profound impact of machine learning, it's insightful to take a step back to the early days of artificial intelligence.…”

Section: Literature Reviewmentioning

confidence: 99%

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Nasrallah,

Stepanyan,

Nassrullah

et al. 2024

RIA

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The present study delves into the utilization of subsumption architecture for the modeling of mobile robot behaviors, particularly those that respond adaptively to environmental dynamics and inaccuracies in sensor measurements. Central to this investigation is the deployment of reactive controller networks, wherein each node-representing a distinct state-is governed by sensor-triggered conditions that dictate state transitions. The methodology adopted comprises a thorough literature review, encompassing sources from IEEE Xplore, ScienceDirect, and the ACM Digital Library, which discuss the integration of subsumption architecture in the realm of mobile robot control. Through this review, the effectiveness of subsumption architecture in crafting reactive robotic behaviors is underscored. It has been established that augmented finite state machines (AFSMs), which are integral to the subsumption architecture and possess internal timing mechanisms, are pivotal in managing the temporal aspects of state transitions. Additionally, the technique of layering-merging multiple simple networks to form intricate behavior patterns-emerges as a significant finding, accentuating the architecture's capability to facilitate complex behavioral constructs. The prime contribution of this body of work lies in identifying and elucidating the strategic role of subsumption architecture in enhancing the adaptability and robustness of mobile robots. The insights gleaned from this study not only advance our understanding of robotic control systems but also hold implications for the amplification of industrial efficiency and effectiveness through the application of sophisticated AI and machine learning techniques in mobile robotics.

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Section: Literature Reviewmentioning

confidence: 99%

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Nasrallah,

Stepanyan,

Nassrullah

et al. 2024

RIA

View full text Add to dashboard Cite

show abstract

“…In order to provide a solution to inverse problems posed in the state-space framework, a time-dependent probabilistic approach based on the Kalman filter (KF) algorithm is adopted, whereby the array of unknown hydrogen density at time k is estimated from a prior reconstruction at time k − 1, along with measurements from time k (Kalman, 1960;Zarchan & Mussof, 2000). The recursive KF process starts with an initial state vector of H densityx k|k−1 (also known as one-step prediction), which may be specified as the static tomographic reconstruction under quiet conditions (x s ), and the initial state [N × N] error covariance matrix P k|k − 1 .…”

Section: Kalman Filtermentioning

confidence: 99%

New Sensing Strategies for Estimation of Global, Exospheric Density

Cucho‐Padin

Waldrop

2020

JGR Space Physics

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Reliable quantification of the global and time-dependent structure of the Earth's outermost atmosphere, a vast region known as the exosphere, has long been elusive, owing mainly to the sparse spatial and temporal sampling afforded by exospheric sensing platforms deployed to date. In this paper, we introduce new observing schemes that overcome these historical limitations and enable high-fidelity, high-cadence reconstruction of the exospheric density distribution on a global scale. Our approach leverages several state-of-the-art remote sensing capabilities, including (1) wide-field photometric imaging of exospheric emission, which is created through resonant photon scattering by the exosphere's constituent hydrogen atoms; (2) constellation sensor deployment near lunar orbit, which provides common-volume sensing; and (3) tomographic inversion of a discretized exospheric emission model that explicitly accounts for the Poisson-distributed shot noise inherent in the photometric data constraints. Using ensembles of synthetic emission data acquired at various realistic temporal cadences, we evaluate the fidelity of both static density reconstructions, in which the solution is formulated in the context of maximum a posteriori (MAP) estimation, as well as dynamic density reconstructions, which incorporate temporal evolution via Kalman filtering. The numerical results demonstrate that the new sensing strategies are capable of retrieving reliable estimates of exospheric density distribution at unprecedented spatial scales and temporal cadence, in support of numerous high-priority investigations of Earth's near-space environment.

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Aircraft and Flight Test Instrumentation

2021

Introduction to Flight Testing

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Fundamentals of Kalman Filtering: A Practical Approach, Fourth Edition

Cited by 44 publications

References 0 publications

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

Elevating Mobile Robotics: Pioneering Applications of Artificial Intelligence and Machine Learning

New Sensing Strategies for Estimation of Global, Exospheric Density

Aircraft and Flight Test Instrumentation

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