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29 pages, 7388 KiB

Open AccessArticle

UAV-UGV Collaborative Localisation with Minimum Sensing

by A. H. T. Eranga De Silva and Jayantha Katupitiya

Sensors 2024, 24(14), 4629; https://doi.org/10.3390/s24144629 - 17 Jul 2024

Viewed by 737

This paper presents a novel methodology to localise Unmanned Ground Vehicles (UGVs) using Unmanned Aerial Vehicles (UAVs). The UGVs are assumed to be operating in a Global Navigation Satellite System (GNSS)-denied environment. The localisation of the ground vehicles is achieved using UAVs that have full access to the GNSS. The UAVs use range sensors to localise the UGV. One of the major requirements is to use the minimum number of UAVs, which is two UAVs in this paper. Using only two UAVs leads to a significant complication that results an estimation unobservability under certain circumstances. As a solution to the unobservability problem, the main contribution of this paper is to present a methodology to treat the unobservability problem. A Constrained Extended Kalman Filter (CEKF)-based solution, which uses novel kinematics and heuristics-based constraints, is presented. The proposed methodology has been assessed based on the stochastic observability using the Posterior Cramér–Rao Bound (PCRB), and the results demonstrate the successful operation of the proposed localisation method. Full article

(This article belongs to the Special Issue New Methods and Applications for UAVs)

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20 pages, 3218 KiB

Open AccessArticle

Compressed Gaussian Estimation under Low Precision Numerical Representation

by Jose Guivant, Karan Narula, Jonghyuk Kim, Xuesong Li and Subhan Khan

Sensors 2023, 23(14), 6406; https://doi.org/10.3390/s23146406 - 14 Jul 2023

Cited by 2 | Viewed by 1071

Abstract

This paper introduces a novel method for computationally efficient Gaussian estimation of high-dimensional problems such as Simultaneous Localization and Mapping (SLAM) processes and for treating certain Stochastic Partial Differential Equations (SPDEs). The authors have presented the Generalized Compressed Kalman Filter (GCKF) framework to reduce the computational complexity of the filters by partitioning the state vector into local and global and compressing the global state updates. The compressed state update, however, still suffers from high computational costs, making it challenging to implement on embedded processors. We propose a low-precision numerical representation for the global filter, such as 16-bit integer or 32-bit single-precision formats for the global covariance matrix, instead of the expensive double-precision, floating-point representation (64 bits). This truncation can inevitably cause filter instability since the truncated covariance matrix becomes overoptimistic or even turns to be an invalid covariance matrix. We introduce a Minimal Covariance Inflation (MCI) method to make the filter consistent while minimizing the truncation errors. Simulation-based experiments results show significant improvement of the proposed method with a reduction in the processing time with minimal loss of accuracy. Full article

(This article belongs to the Section Intelligent Sensors)

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22 pages, 1512 KiB

Open AccessFeature PaperArticle

Influence of Estimators and Numerical Approaches on the Implementation of NMPCs

by Fernando Arrais Romero Dias Lima, Ruan de Rezende Faria, Rodrigo Curvelo, Matheus Calheiros Fernandes Cadorini, César Augusto García Echeverry, Maurício Bezerra de Souza and Argimiro Resende Secchi

Processes 2023, 11(4), 1102; https://doi.org/10.3390/pr11041102 - 4 Apr 2023

Cited by 1 | Viewed by 1481

Abstract

Advanced control strategies, together with state-estimation methods, are frequently applied to nonlinear and complex systems. It is crucial to understand which of these are the most efficient methods for the best use of these approaches in a chemical process. In the current work, nonlinear model predictive control (NMPC) approaches were developed that considered three numerical methods: single shooting (SS), multiple shooting (MS), and orthogonal collocation (OC). Their performance was compared against the Van de Vusse reactor benchmark while considering set-point changes, unreachable set-point, disturbances, and mismatches. The results showed that the NMPC based on OC presented less computational cost than the other approaches. The extended Kalman filter (EKF), constrained extended Kalman filter (CEKF), and the moving horizon estimator (MHE) were also developed. The estimators’ performance was compared for the same benchmark by considering the computational cost and the mean squared error (MSE) for the estimated variables, thereby verifying the CEKF as the best option. Finally, the performance of the nine combinations of estimators and control approaches was compared to consider the Van de Vusse reactor and the same scenarios, thereby verifying the best performance of the CEKF with the OC. The present work can help with choosing the numerical method and the estimator for controlling chemical processes. Full article

(This article belongs to the Section Chemical Processes and Systems)

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26 pages, 1768 KiB

Open AccessArticle

Tightly Coupled INS/APS Passive Single Beacon Navigation

by Zhuoyang Zou, Wenrui Wang, Bin Wu, Lingyun Ye and Washington Yotto Ochieng

Remote Sens. 2023, 15(7), 1854; https://doi.org/10.3390/rs15071854 - 30 Mar 2023

Cited by 2 | Viewed by 1644

Abstract

Unlike aerial or terrestrial navigation, the global navigation satellite system (GNSS) is not available underwater. This is a big challenge for underwater navigation. The inertial navigation system (INS) aided by the single-beacon acoustic positioning system (APS) provides one solution, but the long-range case is limited by low-SNR conditions. Inspired by passive synthetic aperture detection, we proposed a new tightly coupled navigation algorithm based on spatial synthesis and one-way-travel-time (OWTT) range measurement. We design two estimators: the DOA/range estimator using the model-based method and the tightly coupled INS/APS navigation estimator. Based on the improved UKF, all information is combined. Simulation is carried out in MATLAB. Compared with range-only tightly coupled INS/APS navigation, synthetic long baseline (SLBL) algorithm and Doppler velocity logger (DVL) aided centralized extended Kalman filter (CEKF) based single beacon INS/OWTT navigation, the proposed method’s performance is proven. The main contributions of this work are: (1). Propose a new architecture of underwater integrated navigation; (2). Apply the passive acoustic detecting method in the navigation to improve accuracy. (3). Apply the tightly coupled method to improve availability. Full article

(This article belongs to the Special Issue Advances in Positioning, Navigation and 3D Mapping of Underwater Environments)

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18 pages, 6439 KiB

Open AccessArticle

State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

by Jiandong Duan, Peng Wang, Wentao Ma, Xinyu Qiu, Xuan Tian and Shuai Fang

Energies 2020, 13(16), 4197; https://doi.org/10.3390/en13164197 - 14 Aug 2020

Cited by 33 | Viewed by 2537

Abstract

State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions. Full article

(This article belongs to the Special Issue Advanced Battery Technologies for Energy Storage Devices)

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