Analysis on Strong Tracking Filtering for Linear Dynamic Systems

Abstract

Strong tracking filtering (STF) is a popular adaptive estimation method to effectively deal with state estimation for linear and nonlinear dynamic systems with inaccurate models or sudden change of state. The key of the STF is to use a time-variant fading factor, which can be evaluated based on the current measurement innovation in real time, to forcefully correct one step state prediction error covariance. The strong tracking filtering technology has been extensively applied in many practical systems, but the theoretical analysis is highly lacking. In an effort to better understand STF, a novel analysis framework is developed for the strong tracking filtering and some new problems are discussed for the first time. For this, we propose a new perspective that correcting the state prediction error covariance by using the fading factor can be thought of directly modifying the state model by correcting the covariance of the process noise. Based on this proposed point of view, the conditions for the STF function to be effective are deeply analyzed in a certain linear dynamic system. Meanwhile, issues of false alarm and alarm failure are also briefly discussed for the strong tracking filtering function. Some numerical simulation examples are demonstrated to validate the results.

Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

“…Similarly, in situations where system states are changing abruptly, robustness of the filter also becomes very critical, so that the filter can keep up with sharp changes in observations/measurements. This is achieved by adjusting Kalman gain K, or process covariance matrix P [27]. In order to achieve strong convergence, filter accuracy may have to be suppressed in such cases.…”

“…When dealing with inaccurate linear or non-linear dynamic system models, or in situations where state changes are more sudden, the strong tracking filter may provide another effective method for adaptive state estimation [27]. Although lacking in extensive theoretical analysis, the filter has widely been used in practical applications.…”

Group Targets Tracking Using Multiple Models GGIW-CPHD Based on Best-Fitting Gaussian Approximation and Strong Tracking Filter

“…It should be indicated that it is available to adjust the filter gain online by STF on the basis of measurement set partition in order to prevent the problem of tracking model mismatched which eventually makes the discrepancy sequence orthogonal. The adaptation of STF is mainly reflected in the identification of prediction variance, namely, fixing prediction covariance with a fading factor [16]. In this paper, ( , ) | −1 is fixed by a fading factor ( , ) of STF where…”

“…Therefore, the multiple models based on best-fitting Gaussian approximation and strong tracking filter will be introduced to GGIW-CPHD algorithm to track maneuvering group targets with unknown number in this paper. In order to improve the tracking accuracy when group targets maneuver, the best-fitting Gaussian approximation method is used to implement the fusion of multiple models in the CPHD prediction stage and a fading factor of strong tracking filter [16] is used to correct the prediction covariance matrix of the GGIW component.…”

Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

“…To tackle the problem mentioned above, a new algorithm called strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF) is proposed in this paper. The STSSRCKF is developed based on the combination of strong tracking filter (STF) [ 7 , 21 , 22 ] and SSRCKF. The new algorithm using the strong tracking idea and the fading factor based on the residual to modify the prior covariance matrix quickly.…”