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A Novel smooth variable structure filter for Target Tracking Under Model Uncertainty

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第6期23卷 5823-5839页

作者： Li, Yaowen Li, Gang Liu, Yu Zhang, Xiao-Ping He, You Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Naval Aviat Univ Res Inst Informat Fus Yantai 264001 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

Model uncertainty is a serious challenge for robustness of tracking algorithms in radar systems. The smooth variable structure filter (SVSF) achieves error-bounded estimations for target state by scaling the magnitude of kinematic modeling error and accordingly performing a flexible switching strategy for the correction gain. However, the SVSF, without any smoothing functions, suffers from undesired chattering phenomenon since the measurement noise causes random disturbance to the identification of actual level of uncertainties, leading to obvious deterioration of tracking accuracy. In this paper, we present a new switching function for SVSF, i.e. the hyperbolic tangent function, for effective chattering suppression. Then we propose a new algorithm named as the Tanh-SVSF, which reformulates the correction gain with the new switching function, to improve the estimation accuracy for target state. A mathematical definition of SVSF chattering is proposed to quantify the chattering amplitude. It is demonstrated that the new switching function exerts a nonlinear compressing effect on the likelihood of measurement innovation and substantially reduces the disturbance of measurement noise, leading to elimination of the chattering problem. The stability of the Tanh-SVSF is analyzed, based on a proposed stability theorem and the numerical exhaustion strategy. Finally, the proposed method is tested on a simulated vehicle tracking scenario and real-world radar data from the Oxford Radar RobotCar Dataset, and shows superior performance over existing SVSF formulations and the Kalman filter, in view of tracking accuracy, track continuity and the proposed chattering indicator.

关键词： Chattering suppression model uncertainty radar target tracking smooth variable structure filter

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A new particle filter based on smooth variable structure filter

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2020年第1期34卷 32-41页

作者： Zhou, Haomiao Xia, Yuanqing Deng, Yulin Beijing Inst Technol Sch Life Sci Beijing Peoples R China Tangshan Univ Intelligence & Informat Engn Coll Tangshan Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this paper, a new particle filter (PF) based on the smooth variable structure filter (SVSF) is presented. Here, PF is a popular estimation method that makes use of distributed particles to form an approximation of the probability distribution function. The SVSF is a sliding mode estimator, which is based on predictor-corrector methods and sliding mode concepts. Moreover, the gain of the SVSF is calculated based on a switching surface. The new filter called SVSF-PF utilizes a smoothing boundary layer of SVSF to control the particles in a range, which can improve the overall estimation accuracy and stability. Finally, a simulation example of nonlinear target tracking is used to demonstrate effectiveness.

关键词： chattering particle filter smooth variable structure filter

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Simultaneous localization and mapping navigation of unmanned ground vehicle based on second-order smooth variable structure filter with improved technique to combat fading for advanced wireless communications

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2021年第7期235卷 1258-1271页

作者： Mosbah, Abdelkader Demim, Fethi Mansoul, Ali Benssalah, Mustapha Nemra, Abdelkrim Ecole Mil Polytech EMP Lab Traitement Signal Bordj El Bahri 16000 Algeria Ecole Mil Polytech EMP Lab Vehicules Autonomes Intelligents Bordj El Bahri Algeria Ctr Dev Technol Avancees CDTA Telecom Div Baba Hassen Algeria

Simultaneous localization and mapping is very essential for autonomous navigation when the mobile robot is navigating in unknown environment without a global positioning system. Various techniques to solve the simultaneous localization and mapping problem have been extensively studied using the combination of low-cost sensors. Most of the work in mobile robotics still consists of finding solutions to problems in data exchange between mobile robot and communication control station, which is a challenging task. In fact, communication systems impose severe constraints in terms of channel capacity and transmission quality, because the transmission channel in communication systems is undergoing at the different physical phenomena like scattering, diffusion and diffraction, which occur interference and multiple path effects in wireless communications, while keeping these effects levels low. This article describes a simultaneous localization and mapping problem based on second-order smooth variable structure filter embedded in mobile robot equipped with a sensor for data wireless collection. The inclusion of the control in environments outside the mobile robot field of view can make the wireless communication simultaneous localization and mapping process much more difficult to find a solution under realistic conditions. In order to solve the simultaneous localization and mapping issue and to mitigate the fading phenomena, which affect the quality of service in advanced wireless communication systems, we use a new approach to combat the fading effect without requiring any statistical knowledge of the propagation channel parameters. Several experiments have been done in real-world applications, and good performances were obtained using a second-order smooth variable structure filter-simultaneous localization and mapping algorithm-based wireless communication.

关键词： Localization map building smooth variable structure filter wireless communication fading effect

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A new filtering strategy for target tracking application using the second form of smooth variable structure filter

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2022年第6期236卷 1224-1249页

作者： Demim, Fethi Rouigueb, Abdenebi Nemra, Abdelkrim Bouguessa, Rekia Chahmi, Abdelghani Ecole Mil Polytech Lab Vehicules Autonomes Intelligents Algiers 16046 Algeria Ecole Mil Polytech Modeling & Optimizat Tech Lab Algiers Algeria Ecole Mil Polytech Energy Syst Lab Algiers Algeria USTO MB Elect Drives Lab Oran Algeria

Target tracking process consists of tracking and measuring a quantity over time, which is a difficult estimation problem. The latter gives erroneous measurements that are affected by the unknown number of targets to be tracked, as well as, measurements that are corrupted by noise. Thus, overcoming the target tracking problem includes basically solving the filtering problem which leads to a data association problem. Tracking problems that force lightly nonlinear models are solved appropriately with the Extended Kalman filter. Although, problems that use nonlinear models, applying analytical expressions of the Jacobian of dynamic systems, or measurement vector functions are potentially solved by the Unscented Kalman filter. The first technique is well built, but not precise, while the second technique yields very accurate results, but in terms of complexity, it is not suitable for real-time applications. Another filter called Particle filter used a Monte Carlo method based on probability distribution over the state is proposed. Subsequently, we used a Gaussian Mixture Model which is based on the Expectation Maximization algorithm for fitting mixture-of-Gaussian models is considered to guarantee the estimation of optimal parameters set for each target. In order to achieve better trade between optimality and robustness of the tracking effect, the smooth variable structure filter is proposed as an alternative. This latter filter does not make any assumptions about noise characteristics and provides accurate estimation results. Furthermore, in this article, we propose a new target tracking algorithm based on the second form of smooth variable structure filter, which is implemented in a simulation using different scenarios. Experimental scenarios, under realistic conditions, are presented and the obtained results confirm the effectiveness of the smooth variable structure filter approach compared to other filters.

关键词： Target tracking state estimation sensor models smooth variable structure filter measurement systems

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Advances of the smooth variable structure filter: square-root and two-pass formulations

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷

作者： Gadsden, S. Andrew Lee, Andrew S. Univ Guelph Sch Engn Guelph ON Canada Univ Maryland Dept Mech Engn Baltimore MD USA

The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilize a switching gain that brings an inherent amount of stability to the estimation process. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potter's algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation, and the results are compared with the popular Kalman filter. In addition, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the Kalman-based two-pass smoother. (C) 2017 Society of PhotoOptical Instrumentation Engineers (SPIE)

关键词： Kalman filter smooth variable structure filter square root minimum variance smoother target tracking

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Artificial neural network training utilizing the smooth variable structure filter estimation strategy

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NEURAL COMPUTING & APPLICATIONS 2016年第3期27卷 537-548页

作者： Ahmed, Ryan El Sayed, Mohammed Gadsden, S. Andrew Tjong, Jimi Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada Univ Maryland Baltimore Cty Dept Mech Engn Baltimore MD 21201 USA Ford Canada Windsor ON Canada

A multilayered neural network is a multi-input, multi-output nonlinear system in which network weights can be trained by using parameter estimation algorithms. In this paper, a novel training method is proposed. This method is based on the relatively new smooth variable structure filter (SVSF) and is formulated for feed-forward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the sliding mode concept and works in a predictor-corrector fashion. The SVSF training performance is tested on three benchmark pattern classification problems. Furthermore, a study is presented comparing the popular back-propagation method, the extended Kalman filter, and the SVSF.

关键词： Neural networks Extended Kalman filter smooth variable structure filter Estimation

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Kalman filtering strategies utilizing the chattering effects of the smooth variable structure filter

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SIGNAL PROCESSING 2013年第2期93卷 420-431页

作者： Al-Shabi, M. Gadsden, S. A. Habibi, S. R. McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada Philadelphia Univ Dept Mechatron Engn Amman Jordan

The Kalman filter (KF) remains the most popular method for linear state and parameter estimation. Various forms of the KF have been created to handle nonlinear estimation problems, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The robustness and stability of the EKF and UKF can be improved by combining it with the recently proposed smooth variable structure filter (SVSF) concept. The SVSF is a predictor-corrector method based on sliding mode concepts, where the gain is calculated based on a switching surface. A phenomenon known as chattering is present in the SVSF, which may be used to determine changes in the system. In this paper, the concept of SVSF chattering is introduced and explained, and is used to determine the presence of modeling uncertainties. This knowledge is used to create combined filtering strategies in an effort to improve the overall accuracy and stability of the estimates. Simulations are performed to compare and demonstrate the accuracy, robustness, and stability of the Kalman-based filters and their combinations with the SVSF. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Kalman filter smooth variable structure filter State and parameter estimation Chattering

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The uncertainty learning filter: A revised smooth variable structure filter

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SIGNAL PROCESSING 2018年 152卷 217-226页

作者： Spiller, Mark Bakhshande, Fateme Soeffker, Dirk Univ Duisburg Essen Chair Dynam & Control Lotharstr 1 D-47057 Duisburg Germany

In this contribution the problem of state estimation of systems with imprecise description is addressed. A revised version of smooth variable structure filter (SVSF) called uncertainty learning filter (ULF) is proposed. By introducing an uncertainty learning parameter a novel strategy to minimize a pseudo mean squared estimation error is developed. The introduced uncertainty learning parameter acts as a tuning factor controlling the influence of the imprecise process model on the estimations. Parameter dependencies can be avoided by adapting the uncertainty learning parameter from the innovation process. Consequently the proposed adaptive uncertainty learning filter (A-ULF) has the ability to tune itself according to the uncertainties of the process model. The boundedness of estimation error of A-ULF approach is proven. Simulation results are given to compare the estimation performance of the proposed method with SVSF, and the extended Kalman filter (EKF). (C) 2018 Elsevier B.V. All rights reserved.

关键词： smooth variable structure filter State estimation of nonlinear systems Robust estimation Parameter uncertainties Extended Kalman filter Adaptive filtering

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An efficient field programmable gate arrays based real-time implementation of smooth variable structure filter to estimate the state of charge of Li-ion battery in electric vehicle application

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IET ELECTRICAL SYSTEMS IN TRANSPORTATION 2022年第4期12卷 298-314页

作者： Jemmali, Sabeur Hamouda, Mahmoud Manai, Bilal Univ Sousse Ecole Natl Ingenieurs Sousse LATIS Lab Adv Technol & Intelligent Syst BP 264 Sousse Ernadh Sousse 4023 Tunisia

The computational efficiency is a challenging task in the real-time implementation of battery State of Charge (SoC) estimation algorithms in Electric Vehicle (EV) application. This study proposes for the first time a pure hardware architecture of a smooth variable structure filter (SVSF) to estimate the SoC of a lithium-ion battery in an EV. The proposed embedded architecture is implemented on a Xilinx Zynq-7000 field programmable gate arrays board without using any soft-core, and validated with real-time tests carried out on a 20 Ah NMC battery with nominal voltage about 3.65 V. The whole embedded architecture of the SVSF needs a reduced execution time in the range of 921 ns, and it consumes only 466 mW. The average estimation errors of the SoC and battery voltage at different temperatures are kept within 4.95% and 2.35% respectively. The successful convergence of the algorithm and the accurate results obtained in different circumstances, prove the practicability and computational efficiency of the proposed hardware architecture.

关键词： lithium-ion battery SVSF manganese compounds battery state of charge estimation algorithm NMC battery voltage power filters lithium compounds embedded architecture nickel compounds hardware architecture cobalt compounds smooth variable structure filter field programmable gate arrays electric vehicle application battery powered vehicles

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Simultaneous localisation and mapping for autonomous underwater vehicle using a combined smooth variable structure filter and extended kalman filter

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2022年第4期34卷 621-650页

作者： Demim, Fethi Benmansour, Souhila Abdelkrim, Nemra Rouigueb, Abdenebi Hamerlain, Mustapha Bazoula, Abdelouahab Ecole Mil Polytech EMP Lab Vehicules Autonomes Intelligents BP 17 Algiers 16111 Algeria Univ Oran AVCIS Lab Oran Algeria Ecole Mil Polytech EMP Modeling & Optimizat Tech Lab Algiers Algeria Ctr Dev Technol Avancees CDTA Robot Dept Algiers Baba Hassen Algeria

Localisation technology is one of the most important challenges of underwater vehicle applications that accomplish any scheduled mission in the complex underwater environment. Currently, the Simultaneous Localisation and Mapping (SLAM) of the Autonomous Underwater Vehicle (AUV) is becoming a hotspot research. AUVs have, only recently, received more attention and underwater platforms continue to dominate the research. To ensure the success of an accurate AUV localisation mission, the problem of drift on the estimated trajectory must be overcome. In order to improve the positioning accuracy of the AUV localisation, a new filter referred to as the Adaptive smooth variable structure filter (ASVSF) based SLAM positioning algorithm is proposed. To verify the improvement of this filter, the combined SVSF and the Extended Kalman filter (EKF) are presented. Experimental results based on dataset for underwater SLAM algorithm show the accuracy and stability of the ASVSF AUV localisation position. Several experiments were tested under real-life conditions with an autonomous underwater vehicle based on different filters. The results of these filters have been compared based on Root Mean Squared Error (RMSE) and in terms of localisation and map building errors. It is shown that the adaptive SVSF-SLAM strategy obtains the best performance compared to other algorithms.

关键词： Localisation map building sonar data fusion autonomous underwater vehicle smooth variable structure filter

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