An Improved Smooth Variable Structure Filter for Robust Target Tracking

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.

Automated summary

SVSF is a new-style filter that is more robust compared to the Kalman filter, while ISVSF combines Bayesian theory to improve estimation accuracy and shows high robustness in dealing with modeling uncertainties and noise. The proposed ISVSF can deliver satisfying performance even in the presence of sudden changes in system state, outperforming existing filters in target tracking simulations.