A Novel Smooth Variable Structure Filter for Target Tracking Under Model Uncertainty

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.

Download Full-text

Partitioned time-varying smooth variable structure filter for airport target tracking

2016 CIE International Conference on Radar (RADAR) ◽

10.1109/radar.2016.8059444 ◽

2016 ◽

Cited By ~ 1

Author(s):

Wenjuan Li ◽

Hong Gu ◽

Weimin Su

Keyword(s):

Target Tracking ◽

Variable Structure ◽

Time Varying ◽

Smooth Variable Structure Filter

Download Full-text

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

Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing ◽

10.1115/dscc2014-5866 ◽

2014 ◽

Cited By ~ 3

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).

Download Full-text

Target Tracking Using the Smooth Variable Structure Filter

ASME 2009 Dynamic Systems and Control Conference, Volume 2 ◽

10.1115/dscc2009-2632 ◽

2009 ◽

Cited By ~ 3

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.

Download Full-text