Shesheng Gao

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

A SINS/SRS/GNS Autonomous Integrated Navigation System Based on Spectral Redshift Velocity Measurements

In order to meet the requirements of autonomy and reliability for the navigation system, combined with the method of measuring speed by using the spectral redshift information of the natural celestial bodies, a new scheme, consisting of Strapdown Inertial Navigation System (SINS)/Spectral Redshift (SRS)/Geomagnetic Navigation System (GNS), is designed for autonomous integrated navigation systems. The principle of this SINS/SRS/GNS autonomous integrated navigation system is explored, and the corresponding mathematical model is established. Furthermore, a robust adaptive central difference particle filtering algorithm is proposed for this autonomous integrated navigation system. The simulation experiments are conducted and the results show that the designed SINS/SRS/GNS autonomous integrated navigation system possesses good autonomy, strong robustness and high reliability, thus providing a new solution for autonomous navigation technology.

Adaptive Square-Root Unscented Particle Filtering Algorithm for Dynamic Navigation

This paper presents a new adaptive square-root unscented particle filtering algorithm by combining the adaptive filtering and square-root filtering into the unscented particle filter to inhibit the disturbance of kinematic model noise and the instability of filtering data in the process of nonlinear filtering. To prevent particles from degeneracy, the proposed algorithm adaptively adjusts the adaptive factor, which is constructed from predicted residuals, to refrain from the disturbance of abnormal observation and the kinematic model noise. Cholesky factorization is also applied to suppress the negative definiteness of the covariance matrices of the predicted state vector and observation vector. Experiments and comparison analysis were conducted to comprehensively evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm exhibits a strong overall performance for integrated navigation systems.

A Fading Factor Unscented Particle Filter and Its Application in INS/GPS Integrated Navigation

The tracking ability and robustness of the Unscented Particle filter based on the minimum variance estimation principle are weakened when the actual data is abruptly changed. A fading factor unscented particle filter (FFUPF) algorithm is proposed in this paper, which can improve the accuracy of the unscented particle filtering and increase the tracking ability and robustness. The proposed fading factor is used to adaptively adapt the time-varying system noise. And the proposed algorithm infuses the latest observation data in the prior distribution update phase, designs the importance density function by fading factor, and makes it more close to the system status posterior probability density. Simulation has been performed by using an illustrative example and results have been provided to demonstrate the validity of the proposed algorithm as well as its improved performance over the conventional one.

Variational Bayesian-based adaptive unscented particle filter for BDS/INS integration

This paper presents a variational Bayesian-based adaptive unscented particle filter (VB-AUPF) to improve the positioning accuracy of BDS/INS integrated system under the condition without accurate measurement noise covariance. This method approximates the joint posterior distribution of states and measurement noise by using variational Bayesian approach on each time step. Based on this, the measurement noise covariance is estimated with a fixed point iteration and subsequently fed back to the UPF procedure to improve the filtering accuracy. Compared with the traditional UPF, the proposed method can simultaneously estimate state and inaccurate measurement noise variance. The efficacy of the proposed method is demonstrated through the simulation of BDS/INS integrated system.

Adaptive robust unscented particle filter and its application in SINS/SAR integration navigation system

This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of the adaptive robust filtering to the unscented particle filter. This algorithm adaptively determines the equivalent weight function and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise, thus preventing particles from degeneracy. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of adaptive robust filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, and the filtering accuracy is much higher than the extended Kalman filter, unscented Kalman filter, standard particle filter and unscented particle filter.