Optimization of an unscented Kalman filter for an embedded platform

Graybill, Philip; Gluckman, Bruce J.; Kiani, Mehdi

doi:10.1016/j.compbiomed.2022.105557

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…Future research could focus on the reliability and generality of the developed MPOD-NIROM framework. The reliability could require employing a data assimilation algorithm [65]. At the same time, generality of the developed framework may need to be evaluated based on other cases of aquatic-inspired robots [22,72].…”

Section: Discussionmentioning

confidence: 99%

“…The current LSTM NN is trained using the adaptive moment estimation [64], a stochastic gradient descent algorithm [65]. (equations ( 8)-( 10)); (iv) compute Ψ s i , b s i based on the local feature identification (equation ( 11)); (v) train the LSTM NN to learn the mapping from input to output based on the adaptive moment estimation algorithm.…”

Section: Temporal Evolution Prediction Of Solution Statesmentioning

confidence: 99%

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A two-dimensional hydrodynamics prediction framework for mantle-undulated propulsion robot using multiple proper orthogonal decomposition and long short term memory neural network

Ying,

Zhang,

Wang

et al. 2023

Bioinspir. Biomim.

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In this paper, a deep learning based framework has been developed to predict hydrodynamic forces on a mantle-undulated propulsion robot. A multiple proper orthogonal decomposition (MPOD) algorithm has been proposed to efficiently identify fluid features near the undulating mantle of the mantle-undulated propulsion robot globally and locally. The results indicate that the L^2 error of the solution states near the undulating boundary of the proposed MPOD algorithm converges almost linearly to 0.2%. Furthermore, a hydrodynamics prediction framework has been developed based on the proposed MPOD algorithm, where a long short-term memory neural network predicts the temporal coefficients of the MPOD spatial modes. The developed framework achieves economical and reliable predictions of hydrodynamic forces acting on the undulating boundary compared to simulations and experiments. Moreover, the L^2 error of the developed framework is one to two orders of magnitude lower than that of the frameworks based on the classical POD algorithm when the degrees of freedom are consistent. Finally, the reliability of the proposed MPOD-NIROM is discussed through an offline parameter planning case of an aquatic-inspired robot. The model presented in this paper can provide support for the offline parameter planning of aquatic-inspired robots.

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Section: Discussionmentioning

confidence: 99%

Section: Temporal Evolution Prediction Of Solution Statesmentioning

confidence: 99%

A two-dimensional hydrodynamics prediction framework for mantle-undulated propulsion robot using multiple proper orthogonal decomposition and long short term memory neural network

Ying,

Zhang,

Wang

et al. 2023

Bioinspir. Biomim.

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“…As a more systematic approach, Scardua and Cruz [33,34] developed a surrogate model of the desired hyperparameters and tried to optimize the parameters by maximizing the probability of the measured data. In another approach, Graybill et al [35] defined a multi-objective loss function to find optimal hyperparameters that satisfy the desired objectives, such as optimizing computational time or specified error functions.…”

Section: Introductionmentioning

confidence: 99%

Physics-Aware Tuning of Unscented Kalman Filter:Statistical Framework for Solving Inverse Problems Involving Nonlinear Dynamical Systems and Missing Data

Ghorbani,

Dollon,

Gosselin

2024

Preprint

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The performance of the Unscented Kalman Filter (UKF) strongly depends on the proper choice of measurement and process uncertainty matrices, as well as on the scaling parameter of the unscented transform. To avoid cumbersome trial-and-error manual settings in finding the optimal hyperparameters, we introduce a hands-off meta-optimization framework, which incorporates a nonlinear mesh adaptive direct search optimization algorithm in an offline outer loop, paired with a physics-aware loss function. The novelty of this approach is twofold. First, a physics-aware loss function is used to optimize the UKF hyperparameters. It minimizes the physical discrepancy induced by the data-driven correction of the prior states during filtering. Notably, sensor data are not directly incorporated into the calculation of the loss function, which expedites the tuning of the filter in weakly informative data scenarios, especially when the underlying physics is well understood. Second, UKF relaxation is embedded in the optimization to make the measurement and process noise covariance matrices adaptive, which greatly reduces the dimension of the optimization space while remaining very general with respect to the structure of the matrices. We demonstrate the effectiveness of the proposed framework, as the cornerstone of a future digital twin technology combining data- and physics-based models, through various classical problems in solid mechanics, rotating machinery, civil engineering, and fluid-solid interaction.

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“…This property of adaptability and predictive precision of the Kalman filter [36][37][38][39][40][41][42] in dealing with noise and uncertainties of the fluorescence signals convert it into an invaluable resource, that in its turn raises the signal-to-noise ratio (SNR) and allows us to extract meaningful data from serious biological problems. This not only helps in gaining a better insight into biological processes at cellular and molecular levels but also results in a more accurate generation of diagnostic tools and therapies [43][44][45][46][47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

The Kalman filter's role in optimizing fluorescence analysis

Obaideen,

Faroukh,

AlShabi

2024

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX

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This paper investigates the integration of the Kalman filter with fluorescence analysis in biomedical imaging, a synergy that holds the promise of advancing diagnostic accuracy and enhancing research methodologies in the study of biological systems. Employing a rigorous bibliometric analysis through VOSviewer, we explore the key trends, influential research clusters, and seminal publications that have marked the evolution of this interdisciplinary field. The Kalman filter, renowned for its predictive capabilities in real-time signal processing, emerges as a crucial tool for improving the signalto-noise ratio in fluorescence imaging, thereby facilitating the extraction of more accurate and meaningful data from complex biological phenomena. Our analysis reveals a dynamic and growing research landscape, where methodological advancements and computational challenges intersect with practical applications in biomedical imaging. By highlighting the significant contributions and identifying areas ripe for future investigation, this study underscores the potential of Kalman filter-enhanced fluorescence analysis to revolutionize biomedical diagnostics and imaging, offering new insights into cellular and molecular processes. Through this synthesis, we aim to provide a comprehensive overview of the current state of the art and to chart a course for the next wave of innovations in the field.

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Optimization of an unscented Kalman filter for an embedded platform

Cited by 4 publications

References 38 publications

A two-dimensional hydrodynamics prediction framework for mantle-undulated propulsion robot using multiple proper orthogonal decomposition and long short term memory neural network

A two-dimensional hydrodynamics prediction framework for mantle-undulated propulsion robot using multiple proper orthogonal decomposition and long short term memory neural network

Physics-Aware Tuning of Unscented Kalman Filter:Statistical Framework for Solving Inverse Problems Involving Nonlinear Dynamical Systems and Missing Data

The Kalman filter's role in optimizing fluorescence analysis

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