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

Abstract

In this paper, we study a nonlinear bearing-only target tracking problem using four different estimation strategies and compare their performances. This study is based on 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 angle observations. Four nonlinear estimation strategies are used to track the target: the popular extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The SVSF is a 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 performances of these methods applied on a bearing-only target tracking problem are compared in terms of estimation accuracy and filter robustness.

Low Power Prediction Mechanism for Wsn-based Object Tracking

“…Also the accuracy of predictors and the effect of their accuracy on missing rate and energy consumption is considered. So the linear predictor [1], modified version of Kalman filter named EKF which introduced in [22] and another modified version of Kalman filter named UKF [23] are considered to have a fair comparison. Finally the impact of node selection algorithm on network lifetime is investigated.…”

A signal-based fault detection and classification strategy with application to an internal combustion engine

“…Therefore, a combined variable structure and Kalman filtering approach for parameter estimation has been proposed in [15]. A comparison between the EKF, SVSF, PF, and UKF on a bearing-only target tracking problem was demonstrated in [16]. The results demonstrated that the SVSF yielded accurate state estimates while maintaining robustness to uncertainties.…”

Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

“…31,32 A corrective term, referred to as the SVSF gain, is computed as a function of the error in the predicted output, as well as a gain matrix and the smoothing boundary layer width. 30,32,33 The corrected term calculated in equation (21) is then used in equation (24) to find a posteriori state estimate. The priori and posteriori output error estimates are considered as critical variables which are defined by equations (25) and (26), respectively 32,33 The estimation progression makes a summary of equations (21)- (28) and is repeated iteratively.…”

“…30,32,33 The corrected term calculated in equation (21) is then used in equation (24) to find a posteriori state estimate. The priori and posteriori output error estimates are considered as critical variables which are defined by equations (25) and (26), respectively 32,33 The estimation progression makes a summary of equations (21)- (28) and is repeated iteratively. U k is the input control vector.…”

“…31,33 The calculation of the SVSF gain is needed to use two main parameters. The first parameter is (g) which control the speed of convergence, where the second (u) refers to the boundary layer width which is used to smooth out the switching action.…”