Robust Interacting Multiple Model With Modeling Uncertainties for Maneuvering Target Tracking

Abstract

This paper proposes an improved robust interacting multiple model (RIMM) algorithms with modeling uncertainties for maneuvering target tracking with changing dynamics. To mitigate the effects of the modeling uncertainty, a compensation step is introduced to adjust the degree of dependence of the filtering on the system or the measurement model based on the orthogonality principle between the state estimation error and innovation sequence of the subfilter model in the RIMM algorithm. By relying on the compensation parameter, the proposed algorithm fully utilizes the useful information in the innovation sequence and reduces the impact of system model error. The numerical simulation and car-mounted experiments using time difference of arrival (TDOA) measurements of the maneuvering target tracking with changing dynamics are conducted to verify the effectiveness of the proposed RIMM algorithm. Compared with the conventional approaches, the proposed RIMM algorithm results in a remarkable improvement in the state estimation accuracy and small bias while improving the consistency of the filter. INDEX TERMS Robust interacting multiple model, maneuvering target tracking, modeling uncertainties, orthogonality principle, unscented Kalman filter, extended Kalman filter.

Combined Quaternion-Based Error State Kalman Filtering and Smooth Variable Structure Filtering for Robust Attitude Estimation

“…In addition, selecting the appropriate subfilter model to detect system faults is unrealistic in practical engineering applications. Alternatively, the interacting multiple model (IMM) filter can be utilized to address the aforementioned issue, but the IMM filter, which consists of a number of subfilters corresponding to different fault models, incurs a substantial computational burden [43]. Therefore, to address the aforementioned problem, we propose an alternative algorithm for the SVSF-VBL based on a comprehensive fault detection strategy.…”

Gauss Hermite H ∞ Filter for UAV Tracking Using LEO Satellites TDOA/FDOA Measurement—Part I

“…In [22], a new adaptive robust Unscented Kalman Filter (UKF) based on Singular Value Decomposition (SVD) with QR decomposition for improving the accuracy of target tracking has been proposed; the authors used a classical measurement for Target Tracking Range and Bearing; as well as they, applied a simple target Tracking state model (steady-state-transition matrix is stationary and fixed). Consequently, Quadrature Information Kaman Filters (QIKFs) and H ∞ Filter based on TDOA/FDOA measurement can overcome the problem of the high-nonlinear [23]- [26]. QIKFs is a package of nonlinear higher-degree class filters that use for accurate target tracking and space navigation [23].…”

“…QIKFs is a package of nonlinear higher-degree class filters that use for accurate target tracking and space navigation [23]. H ∞ filtering is employed to overcome the uncertainty of target tracking and maneuvering measurements [25], [26]. The main contribution of this paper is to achieve high precision of geolocation and tracking for a dynamic emitter using H ∞ /GHKF 3 rd and H ∞ /GHKF 5 th that based on TDOA/FDOA measurements.…”

Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors

“…To resolve this mode-changing problem, a variety of methods have been investigated, such as the interacting multiple model (IMM) algorithm. 10,11 Two main trajectory tracking and prediction algorithms are investigated in this study. The first is an algorithm based on parameter identification of aircraft dynamics and kinematics models.…”