Comparison of extended and unscented Kalman, particle, and smooth variable structure filters on a bearing-only target tracking problem

2009 ◽  
Author(s):  
S. A. Gadsden ◽  
D. Dunne ◽  
S. R. Habibi ◽  
T. Kirubarajan
Keyword(s):  
Target Tracking ◽  
Variable Structure ◽  
Tracking Problem

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).

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.

Maneuvering multi-target tracking based on variable structure multiple model GMCPHD filter

2017 ◽  
Vol 141 ◽  
pp. 158-167 ◽  
Author(s):  
Peng Dong ◽  
Zhongliang Jing ◽  
Deren Gong ◽  
Baitao Tang
Keyword(s):  
Target Tracking ◽  
Variable Structure ◽  
Multiple Model ◽  
Multi Target Tracking
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