Target Tracking Using the Smooth Variable Structure Filter

2009 ◽  
Author(s):  
Andrew Gadsden ◽  
Saeid Habibi
Keyword(s):  
Target Tracking ◽  
Sliding Mode ◽  
Initial Conditions ◽  
A Priori ◽  
Variable Structure ◽  
Tracking Problem ◽  
Tracking Accuracy ◽  
State Estimate ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

This article discusses the application of the smooth variable structure filter (SVSF) on a target tracking problem. The SVSF is a relatively new predictor-corrector method used for state and parameter estimation. It is a sliding mode estimator, where gain switching is used to ensure that the estimates converge to true state values. An internal model of the system, either linear or nonlinear, is used to predict an a priori state estimate. A corrective term is then applied to calculate the a posteriori state estimate, and the estimation process is repeated iteratively. The results of applying this filter on a target tracking problem demonstrate its stability and robustness. Both of these attributes make using the SVSF advantageous over the well-known Kalman and extended Kalman filters. The performances of these algorithms are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, tracking accuracy and computational complexity.

scholarly journals An Improved Smooth Variable Structure Filter for Robust Target Tracking

2021 ◽  
Vol 13 (22) ◽  
pp. 4612
Author(s):  
Yu Chen ◽  
Luping Xu ◽  
Guangmin Wang ◽  
Bo Yan ◽  
Jingrong Sun
Keyword(s):  
Target Tracking ◽  
Sliding Mode ◽  
Sudden Change ◽  
Variable Structure ◽  
The State ◽  
Modeling Uncertainties ◽  
Modeling Errors ◽  
The Face ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

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.

scholarly journals A Novel Smooth Variable Structure Smoother for Robust Estimation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1781
Author(s):  
Yu Chen ◽  
Luping Xu ◽  
Bo Yan ◽  
Cong Li
Keyword(s):  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Accuracy ◽  
Maneuvering Target Tracking ◽  
State Estimate ◽  
Maneuvering Target ◽  
Modeling Uncertainties ◽  
New Type ◽  
Gaussian System ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.

A Multi-Target Tracking Formulation of SVSF With the Joint Probabilistic Data Association Technique

2014 ◽  
Author(s):  
Mina Attari ◽  
S. Andrew Gadsden ◽  
Saeid R. Habibi
Keyword(s):  
Parameter Estimation ◽  
Target Tracking ◽  
Data Association ◽  
Joint Probability ◽  
Variable Structure ◽  
Tracking Problem ◽  
Proof Of Concept ◽  
Multi Target Tracking ◽  
Smooth Variable Structure Filter ◽  
Parameter Estimation Techniques

Target tracking scenarios offer an interesting challenge for state and parameter estimation techniques. This paper studies a situation with multiple targets in the presence of clutter. In this paper, the relatively new smooth variable structure filter (SVSF) is combined with the joint probability data association (JPDA) technique. This new method, referred to as the JPDA-SVSF, is applied on a simple multi-target tracking problem for a proof of concept. The results are compared with the popular Kalman filter (KF).

A Review of Smooth Variable Structure Filters: Recent Advances in Theory and Applications

2015 ◽  
Author(s):  
S. Andrew Gadsden ◽  
Hamed H. Afshari
Keyword(s):  
Review Paper ◽  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Methods ◽  
Original Form ◽  
Estimation Technique ◽  
Modeling Errors ◽  
Recent Developments ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a relatively new state and parameter estimation technique. Introduced in 2007, it is based on the sliding mode concept, and is formulated in a predictor-corrector fashion. The main advantages of the SVSF, over other estimation methods, are robustness to modeling errors and uncertainties, and its ability to detect system changes. Recent developments have looked at improving the SVSF from its original form. This review paper provides an overview of the SVSF, and summarizes the main advances in its theory.

Nonlinear Estimation Techniques Applied on Target Tracking Problems

2012 ◽  
Vol 134 (5) ◽  
Author(s):  
Andrew Gadsden ◽  
Saeid Habibi ◽  
Darcy Dunne ◽  
T. Kirubarajan
Keyword(s):  
Target Tracking ◽  
Traffic Control ◽  
Initial Conditions ◽  
Nonlinear Estimation ◽  
Variable Structure ◽  
Two Dimensions ◽  
Tracking Accuracy ◽  
Linear Measurements ◽  
Estimation Techniques ◽  
System Equations

This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observations. These two target tracking problems provide a good benchmark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its stability and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters performances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accuracy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlinear target tracking problems, which in the past have typically been solved by Kalman or particle filters.

Sliding Mode Control of a Three Degrees of Freedom Anthropoid Robot by Driving the Controller Parameters to an Equivalent Regime

2000 ◽  
Vol 122 (4) ◽  
pp. 632-640 ◽  
Author(s):  
M. Onder Efe ◽  
Okyay Kaynak ◽  
Xinghuo Yu
Keyword(s):  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Initial Conditions ◽  
Tracking Error ◽  
Variable Structure ◽  
Mathematical Representation ◽  
Dynamic Complexity ◽  
Variable Structure Systems ◽  
Sliding Motion ◽  
Three Degrees Of Freedom

Noise rejection, handling the difficulties coming from the mathematical representation of the system under investigation and alleviation of structural or unstructural uncertainties constitute prime challenges that are frequently encountered in the practice of systems and control engineering. Designing a controller has primarily the aim of achieving the tracking precision as well as a degree of robustness against the difficulties stated. From this point of view, variable structure systems theory offer well formulated solutions to such ill-posed problems containing uncertainty and imprecision. In this paper, a simple controller structure is discussed. The architecture is known as Adaptive Linear Element (ADALINE) in the framework of neural computing. The parameters of the controller evolve dynamically in time such that a sliding motion is obtained. The inner sliding motion concerns the establishment of a sliding mode in controller parameters, which aims to minimize the error on the controller outputs. The outer sliding motion is designed for the plant. The algorithm discussed drives the error on the output of the controller toward zero learning error level, and the state tracking error vector of the plant is driven toward the origin of the phase space simultaneously. The paper gives the analysis of the equivalence between the two sliding motions and demonstrates the performance of the algorithm on a three degrees of freedom, anthropoid robotic manipulator. In order to clarify the performance of the scheme, together with the dynamic complexity of the plant, the adverse effects of observation noise and nonzero initial conditions are studied. [S0022-0434(00)01704-4]

Fault Detection of an Electrohydrostatic Actuator With the SVSF Time-Varying Boundary Layer Concept

2013 ◽  
Author(s):  
S. Andrew Gadsden ◽  
Saeid R. Habibi
Keyword(s):  
Boundary Layer ◽  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Methods ◽  
Gear Pump ◽  
Time Varying ◽  
Aerospace Applications ◽  
Performance Metric ◽  
Efficient Type ◽  
Predictor Corrector

The electrohydrostatic actuator (EHA) is an efficient type of actuator commonly used in aerospace applications. It makes use of a closed hydraulic circuit, a number of control valves, an electric motor, and a fluid pump (usually a type of gear pump). The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode concepts formulated in a predictor-corrector fashion. The SVSF offers a number of advantages over other traditional estimation methods, including robust and stable estimates, and an additional performance metric. A fixed smoothing boundary layer was implemented in an effort to ensure stable estimates, and is defined based on the amount of uncertainties and noise present in the estimation process. Recent advances in SVSF theory include a time-varying smoothing boundary layer. This method, known as the SVSF-VBL, offers an optimal formulation of the SVSF as well as a method for detecting changes or faults in a system. This paper implements the SVSF-VBL in an effort to detect faults in an EHA. The results are compared with traditional Kalman filter-based methods.

A Novel Fuzzy Sliding Mode Control Scheme for Nonlinear Systems with Bounded Inputs

2002 ◽  
Vol 8 (7) ◽  
pp. 945-965 ◽  
Author(s):  
Juhng-Perng Su ◽  
Chi-Ying Liang
Keyword(s):  
Sliding Mode Control ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Initial Conditions ◽  
Variable Structure ◽  
Single Frequency ◽  
Bench Mark ◽  
Mode Control ◽  
Variable Structure System ◽  
Control Scheme

In this paper, we investigated the design of robust controllers for a class of nonlinear uncertain systems with bounded inputs, which have not yet been thoroughly discussed. Based on the variable structure system theory, we developed a novel stable sliding mode control scheme for this class of systems. A key feature of this control scheme is the introduction of a new generalized error as a complement to the conventional generalized error to form a meaningful error measure so that a new sliding mode controller incorporated with a two-input one-output fuzzy controller can be constructed to improve the reaching behavior of the system during the reaching phase as well as the tracking precision while in the boundary layer. The nonlinear bench mark problem, TORA, was used as an example to demonstrate the effectiveness of the design. Simulation results showed that, as compared with various available controllers in literature, much better responses to any initial conditions and to single-frequency sinusoidal disturbances can be obtained.

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