Abstract
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.
Highlights
The state and parameter estimation of dynamic systems plays a key role in various application fields
Five hundred trials of Monte Carlo are performed in each simulation, and the estimation results are expressed in the figures of merit-root mean square error (RMSE), accumulative RMSE and the average value
smooth variable structure smoother (SVSS) improves tracking accuracy by about 22% compared with smooth variable structure filter (SVSF) and RSTKF, while Kalman smoother (KS) only improves about 7%
Summary
The state and parameter estimation of dynamic systems plays a key role in various application fields. The smooth variable structure filter (SVSF) was proposed based on variable structure theory in 2007, and it has advantages in dealing with the problem of uncertain modeling and noise interference [32]. Similar to the KF method, the SVSF is a predictor-corrector strategy, its gain is based on the discontinuous correction gain, and limits the state estimation around the true state trajectory with a small deviation. Thereby it improves the stability and the robustness of the estimation [32]. The SVSF gain will force the estimated state to switch back and forth along the true state trajectory.
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