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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

作者： Lee, Andrew Gadsden, S. Andrew Wilkerson, Stephen A. Univ Guelph 50 Stone Rd E Guelph ON N1G 2W1 Canada York Coll Penn York PA 17403 USA

ISBN: (纸本)9781510627024

Estimation theory is an important field in mechanical and electrical engineering, and is comprised of strategies that are used to predict, estimate, or smooth out important system state and parameters. The most popular and well-studied estimation strategy was developed over 60 years ago, and is referred to as the Kalman filter (KF). The KF yields the optimal solution in terms of estimation error for linear, well-known systems. Other variants of the KF have been developed to handle modeling uncertainties, non-Gaussian noise, and nonlinear systems and measurements. Although KF-based methods typically work well, they lack robustness to uncertainties and external disturbances - which are prevalent in signal processing and target tracking problems. The smooth variable structure filter (SVSF) was introduced in an effort to provide a more robust estimation strategy. In an effort to improve the robustness and filtering strategy further, this paper introduces an adaptive form of the SVSF based on the static multiple model strategy.

关键词： smooth variable structure filter Static Multiple Models Estimator Extended Kalman filter

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The Cubature smooth variable structure filter Estimation Strategy Applied to a Quadrotor Controller 24

The Cubature Smooth Variable Structure Filter Estimation Str...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV

作者： Al-Shabi, M. Gadsden, S. A. Wilkerson, S. A. Philadelphia Univ Amman Jordan Univ Maryland Baltimore Cty UMBC Baltimore MD 21250 USA US Army Res Lab Aberdeen MD 21001 USA

ISBN: (纸本)9781628415902

Unmanned aerial systems (UAS) are becoming increasingly popular in industry, military, and social environments. An UAS that provides good operating performance and robustness to disturbances is often quite expensive and prohibitive to the general public. To improve UAS performance without affecting the overall cost, an estimation strategy can be implemented on the internal controller. The use of an estimation strategy or filter reduces the number of required sensors and power requirement, and improves the controller performance. UAS devices are highly nonlinear, and implementation of filters can be quite challenging. This paper presents the implementation of the relatively new cubature smooth variable structure filter (CSVSF) on a quadrotor controller. The results are compared with other state and parameter estimation strategies.

关键词： Kalman filter smooth variable structure filter estimation strategies quadrotor dynamics

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Identification of Non-linear Dynamic Systems Using smooth variable structure filter

Identification of Non-linear Dynamic Systems Using Smooth Va...

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IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Parija, Sebamai Dash, P. K. Hasan, Shazia Siksha O Anusandhan Univ ITER Dept Elect & Instrumentat Bhubaneswar Orissa India Siksha O Anusandhan Univ Multidisciplinary Res Cell Bhubaneswar Orissa India BITS Dept Elect & Elect Engn Dubai U Arab Emirates

ISBN: (纸本)9781479974559

This paper proposes a model based approach for nonlinear dynamic system identification, which utilizes smooth variable structure filter (SVSF) having multi fold advantages over other adaptive filters. The SVSF is based on sliding mode control concept and can be applied to both linear and nonlinear system. Further to increase the accuracy of nonlinear chaotic system identification the 'memory element'. is tuned iteratively in the measurement matrix of SVSF. So, a self tuning approach for updating memory element is proposed, which is inversely proportional to the sample number. Further SVSF is modified by introducing a nonlinear term with square innovation error in measurement matrix to achieve higher order sliding mode control. The effect and the improvement of this modification has been verified under different test conditions. Various simulations have been carried out to show the stability and improved robustness of the proposed algorithm over other conventional methods. The mean square error obtained using this approach is found to be significantly less than other conventional methods.

关键词： Kalman filter System model smooth variable structure filter state model

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A New Nonlinear smooth variable structure filter Algorithm Applied to Air Traffic Control Tracking 35

A New Nonlinear Smooth Variable Structure Filter Algorithm A...

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第35届中国控制会议

作者： SHI Zhen CHEN Shuai DING Jicheng College of Automation Harbin Engineering University

ISBN: (纸本)9781509009107

A target-tracking problem based on Air Traffic Control(ATC) Scenario is a typical estimation problem. The targettracking process that provides good estimation accuracy and robustness to disturbances is often quite important. In order to refine the target-tracking performance, a new nonlinear smooth variable structure filter algorithm is proposed to handle the ATC tracking problem with erroneous measurements. The new estimation algorithm is developed from a fifth-degree cubature Kalman filter, smooth variable structure filter and a time-varying boundary layer. The whole estimation process mainly relies on acquisition of the cubature point, the gain matrices and the time-varying boundary layer rather than updating the error covariance matrix. Simulation results show that the correctness and efficacy of the new algorithm used in the ATC tracking process is superior to other estimation methods. Moreover, the new algorithm provides an alternative fault detection function for the estimation process.

关键词： Target tracking smooth variable structure filter 5-th CKF time varying boundary layer

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The Toeplitz-Observability smooth variable structure filter

The Toeplitz-Observability Smooth Variable Structure Filter

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IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)

作者： Al-Shabi, Mohammad A. Gadsden, S. Andrew Habibi, Saeid R. Philadelphia Univ Dept Mechatron Engn Jerash Jordan

ISBN: (纸本)9781479923052;9781479923038

The smooth variable structure filter (SVSF) is a recently proposed method that is used for estimation purposes, such as fault detection [1-2]. The SVSF demonstrates good results and robustness when it is applied to linear and non-linear systems that are fully measured. However, the results differ when some of the states are not measured. In this case, the SVSF is combined with the Luenberger method, which has some limitations. In this paper, a novel form of the SVSF is derived using the Observability and Toeplitz matrices. The benefits of the proposed method are demonstrated by using a computer simulation that involves an electro-hydrostatic actuator proposed in [3-5].

关键词： estimation smooth variable structure filter observability Toeplitz electro-hydrostatic actuator

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Statically Fused smooth variable structure filter for Robust Tracking with Doppler Radar

Statically Fused Smooth Variable Structure Filter for Robust...

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IEEE Radar Conference (RadarConf)

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

ISBN: (纸本)9781728153681

The smooth variable structure filter (SVSF) provides robust state estimation in case of model uncertainty but requires a linear measurement model and a full-rank measurement matrix. Doppler radar reports target position and range rate in the polar or spherical coordinates without the tangential velocity, leading to a nonlinear and underdetermined measurement model, which does not satisfy the requirements of the standard SVSF. In this paper, we propose a robust nonlinear estimator, named as the statically fused smooth variable structure filter (SFSVSF), for robust target state estimation with Doppler radar. First, the converted position measurement (CPM) is calculated in Cartesian coordinates and a converted Doppler measurement (CDM) is constructed from the product of the range and range rate measurements. Accordingly, the nonlinear Doppler radar measurement model is decomposed into a Cartesian-measurement model and a pseudo-measurement model. Both are linear time-invariant and underdetermined, and can be handled by the generalized SVSF. Thus, two generalized SVSFs, referred to as the CPM-SVSF and the CDM-SVSF, are implemented based on these two submodels for robust estimations of the Cartesian state and the pseudo state, respectively. The Cartesian-state estimation and the pseudo-state estimation are statically fused by an approximate linear minimum mean square error (LMMSE) estimator for the final estimation of target state. Simulation results show the effectiveness of the SFSVSF algorithm in nonlinear estimation and the advantage in robustness over the EKF in case of target motion uncertainty.

关键词： Doppler radar tracking smooth variable structure filter robust estimation nonlinear estimation

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Distributed Lattice smooth variable structure filter for Target Tracking Under Model Uncertainty 38

Distributed Lattice Smooth Variable Structure Filter for Tar...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Jiao, Yuzhao Niu, Jianxiong Zhao, Hongmei Lou, Taishan Zhao, Liangyu Zhengzhou University of Light Industry School of Electrical and Information Engineering Zhengzhou China Beijing Institute of Technology School of Aerospace Engineering Beijing100081 China

ISBN: (纸本)9798350303636

The conventional filters algorithm cannot provide a stable and precise state estimation for target tracking in Air Traffic Control (ATC) with motion model uncertainty. In this paper, the new filter and distributed fusion strategies are proposed. First, a Lattice smooth variable structure filter (LSVSF) is proposed for nonlinear system with model uncertainty, which avoids the calculation of Jacobi matrices. Then, the distributed lattice smooth variable structure filter Distributed Lattice smooth variable structure filter (MW-LSVSF) is proposed, and the cross-covariance of local estimators is calculated by lattice sampling. Finally, the state estimation accuracy and robustness are verified by ATC scenarios. © 2023 IEEE.

关键词： distributed fusion smooth variable structure filter state estimation target tracking

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Training of Neural Networks Using the smooth variable structure filter with Application to Fault Detection

Training of Neural Networks Using the Smooth Variable Struct...

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作者： Ahmed, Ryan McMaster University

学位级别：master

Artificial neural network (ANNs) is an information processing paradigm inspired by the human brain. ANNs have been used in numerous applications to provide complex nonlinear input-output mappings. They have the ability to adapt and learn from observed *** training of neural networks is an important area of research and consideration. Training techniques have to provide high accuracy, fast speed of convergence, and avoid premature convergence to local *** this thesis, a novel training method is proposed. This method is based on the relatively new smooth variable structure filter (SVSF) and is formulated for feedforward 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 applies a discontinuous corrective term to estimate state and parameters. Its advantages include guaranteed stability, robustness, and fast speed of *** proposed training technique is applied to three real-world benchmark problems and to a fault detection application in a Ford diesel ***-based training technique shows an excellent generalization capability and a fast speed of convergence.%%%%Artificial neural network (ANNs) is an information processing paradigm inspired by the human brain. ANNs have been used in numerous applications to provide complex nonlinear input-output mappings. They have the ability to adapt and learn from observed *** training of neural networks is an important area of research and consideration. Training techniques have to provide high accuracy, fast speed of convergence, and avoid premature convergence to local *** this thesis, a novel training method is proposed. This method is based on the relatively new smooth variable structure filter (SVSF) and is formulated for feedforward multilayer perceptron training. The SVSF is a state and parameter estimation that is based on the Sliding Mode Concept and works in a predi

关键词： Neural Networks smooth variable structure filter

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An Improved smooth variable structure filter for Robust Target Tracking

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REMOTE SENSING 2021年第22期13卷 4612-4612页

作者： Chen, Yu Xu, Luping Wang, Guangmin Yan, Bo Sun, Jingrong Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Peoples R China Univ Bologna Dept Elect Elect & Informat Engn I-47521 Cesena FC Italy

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

关键词： state estimation target tracking smooth variable structure filter Kalman filter

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The smooth variable structure filter: A comprehensive review

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DIGITAL SIGNAL PROCESSING 2021年 110卷 102912-102912页

作者： Avzayesh, Mohammad Abdel-Hafez, Mamoun AlShabi, M. Gadsden, S. A. Amer Univ Sharjah Dept Mech Engn POB 26666 Sharjah U Arab Emirates Univ Sharjah Dept Mech & Nucl Engn POB 27272 Sharjah U Arab Emirates Univ Guelph Coll Engn & Phys Sci Guelph ON Canada

The smooth variable structure filter (SVSF) is a type of sliding mode filter formulated in a predictor-corrector format and has seen significant development over the last 15 years. In this paper, we provide a comprehensive review of the SVSF and its variants. The developments, applications and improvements of the SVSF in terms of robustness and optimality are investigated. In addition, the combination of the SVSF with different filtering strategies is considered in an effort to improve estimation accuracy while maintaining robustness to model uncertainty. State estimation techniques such as the unscented and cubature Kalman filters (UKF & CKF), SVSF, the combination of SVSF with UKF (UK-SVSF), and the combination of CKF with SVSF (CK-SVSF) are applied on a 4-DOF industrial robotic arm. The SVSF state estimation performance is examined under different operating conditions. The results of these filters have been compared based a number of statistics such as the root mean squared error (RMSE) and mean absolute error (MAE), among others. It is shown that the UK-SVSF and CK-SVSF strategies acquire the best performance in the presence of uncertainties. (C) 2020 The Authors. Published by Elsevier Inc.

关键词： Estimation theory filtering Kalman filters smooth variable structure filter Robustness

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