Robust Kalman Filtering With Probabilistic Uncertainty in System Parameters

Kim, Sunsoo; Deshpande, Vedang M.; Bhattacharya, Raktim

doi:10.1109/lcsys.2020.3001490

Cited by 12 publications

(5 citation statements)

References 34 publications

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“…The random filtering method is based on a two-step Bayesian process, that includes time or measurement updates [67]. The KF assumes uncertainty in the dynamics of the Gaussian distribution system and uses the mean and covariance of the state vector for update adjustments [68]. Observer KF was proposed to handle measurement noise and mild nonlinearity.…”

Section: Offline Identificationmentioning

confidence: 99%

AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

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Section: Offline Identificationmentioning

confidence: 99%

AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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“…The main problem of human OD flow prediction is to predict the transfer flow between origin and destination areas [9]. In the early days, mathematical models such as the moving autoregressive model ARIMA [10], Kalman filter algorithm [11], and its extended algorithm were mainly used for OD traffic prediction. In recent years, various machine learning and deep learning models have been widely used in traffic OD flow prediction.…”

Section: Related Workmentioning

confidence: 99%

Human Origin‐Destination Flow Prediction Based on Large Scale Mobile Signal Data

Huang

Yang

et al. 2021

Wireless Communications and Mobile Computing

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The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

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“…In the case of uncertainties, e.g., in system parameters, robust Kalman filters have been developed in the past. The uncertainties can be quantified in different ways, e.g., with interval [17,18] or stochastic variables [19].…”

Section: Introductionmentioning

confidence: 99%

The Data Assimilation Approach in a Multilayered Uncertainty Space

Drieschner,

Herrmann,

Petryna

2023

Modelling

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The simultaneous consideration of a numerical model and of different observations can be achieved using data-assimilation methods. In this contribution, the ensemble Kalman filter (EnKF) is applied to obtain the system-state development and also an estimation of unknown model parameters. An extension of the Kalman filter used is presented for the case of uncertain model parameters, which should not or cannot be estimated due to a lack of necessary measurements. It is shown that incorrectly assumed probability density functions for present uncertainties adversely affect the model parameter to be estimated. Therefore, the problem is embedded in a multilayered uncertainty space consisting of the stochastic space, the interval space, and the fuzzy space. Then, we propose classifying all present uncertainties into aleatory and epistemic ones. Aleatorically uncertain parameters can be used directly within the EnKF without an increase in computational costs and without the necessity of additional methods for the output evaluation. Epistemically uncertain parameters cannot be integrated into the classical EnKF procedure, so a multilayered uncertainty space is defined, leading to inevitable higher computational costs. Various possibilities for uncertainty quantification based on probability and possibility theory are shown, and the influence on the results is analyzed in an academic example. Here, uncertainties in the initial conditions are of less importance compared to uncertainties in system parameters that continuously influence the system state and the model parameter estimation. Finally, the proposed extension using a multilayered uncertainty space is applied on a multi-degree-of-freedom (MDOF) laboratory structure: a beam made of stainless steel with synthetic data or real measured data of vertical accelerations. Young’s modulus as a model parameter can be estimated in a reasonable range, independently of the measurement data generation.

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Robust Kalman Filtering With Probabilistic Uncertainty in System Parameters

Cited by 12 publications

References 34 publications

AUV Trajectory Tracking Models and Control Strategies: A Review

AUV Trajectory Tracking Models and Control Strategies: A Review

Human Origin‐Destination Flow Prediction Based on Large Scale Mobile Signal Data

The Data Assimilation Approach in a Multilayered Uncertainty Space

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