Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.) (Book)

Survival Analysis: Techniques for Censored and Truncated Data

A new direct filtering approach to INS/GNSS integration

Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system

This paper deals with the estimation of the noise covariance matrices of systems described by state-space models. Stress is laid on the systematic survey and classification of both the recursive an...

Noise covariance matrices in state-space models: A survey and comparison of estimation methods—Part I

Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter

The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamic systems due to its simple calculation process and superior performance in highly nonlinear systems. However, its solution will be degraded or even divergent when the system model involves uncertainty. This paper presents an interacting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This method combines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbance of system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process model uncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principle of innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF and robust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilistic weighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparison analysis validate the efficacy of the proposed method.

Interacting multiple model estimation-based adaptive robust unscented Kalman filter

This paper presents an unscented Kalman filter (UKF) based multi-sensor optimal data fusion methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration based on nonlinear system model. This methodology is of two-level structure: at the bottom level, the UKF is served as local filters to integrate GNSS and CNS with INS respectively for generating the local optimal state estimates; and at the top level, a novel optimal data fusion approach is derived based on the principle of linear minimum variance for the fusion of local state estimates to obtain the global optimal state estimation. The proposed methodology refrains from the use of covariance upper bound to eliminate the correlation between local states. Its efficacy is verified through simulations, practical experiments and comparison analysis with the existing methods for INS/GNSS/CNS integration.

Bingbing Gao

Papers

A new direct filtering approach to INS/GNSS integration

Unscented kalman filter with process noise covariance estimation for vehicular ins/gps integration system

Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter

Interacting multiple model estimation-based adaptive robust unscented Kalman filter

Multi-sensor Optimal Data Fusion for INS/GNSS/CNS Integration Based on Unscented Kalman Filter