State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative, and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the “single” disturbance can be either rejected or attenuated. Since the pioneering work in 2012 [42], a novel state estimation methodology called composite disturbance filtering (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances.

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods. Note to Practitioners —Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

Nowadays, the ultrawideband (UWB) has become a popular solution to the indoor localization problem of quadrotor unmanned aerial vehicle (UAV) due to its low power consumption and ease of implementation. Nonetheless, the accuracy of UAV localization is affected by the skew- $t$ measurement noises of the UWB sensor induced mainly by the nonline-of-sight (NLOS) errors and the multipath effects, and the uncertainties in the UAV dynamics caused by external wind disturbances. In this article, a composite filtering approach is proposed for time difference of arrival (TDOA)-based UWB localization of the quadrotor UAV. The main advantage of the composite filtering approach lies in its capability of dealing with the skew- $t$ measurement noises and the dynamic uncertainties simultaneously. Specifically, the external winds are estimated and rejected in real time via the disturbance observer (DO). Meanwhile, the posterior state distribution under skew- $t$ measurement noises is updated in an iterative fashion within the variational Bayes (VB) framework. The effectiveness of the proposed approach is validated through both numerical simulation and experimental tests on a quadrotor UAV.

The polarization-based attitude and heading reference system (PAHRS) provides an effective solution for attitude and heading information acquisition. Its practical performance, however, will be degraded due to partial loss and/or occlusion of the optical signal. To improve the adaptivity of PAHRS, in this article, we establish a polarization-based tightly coupled model (PTCM) and propose a reliable fusion strategy for information extraction from the polarization sensor (PS) and inertial navigation system (INS). As compared to the existing PS/INS fusion model, the proposed PTCM directly adopt PS raw observations (polarized skylight intensity) to compensate for the accumulation errors of INS, thereby removing the constraints on the least number of PS observation channels and avoiding nonlinear transformation of PS noises. Moreover, the reliable fusion strategy consists of a reliable observation channel selection step followed by a nonlinear filtering step, which can reduce the effect of unreliable polarized skylight intensity measurement. Finally, the simulation, static and semi-physical vehicle-mounted tests confirm the effectiveness of the proposed PTCM and fusion strategy.

This article proposes a robust particle filtering (PF) approach for a generic class of nonlinear systems with both additive time-varying uncertainty (ATVU) in the state transition equation and inaccurate process noise covariance matrices. To avoid sampling efficiency degradation of the PF approach caused by ATVU, we employ the disturbance observer-based PF (DOBPF) approach where the effect of ATVU is compensated in the particle generation stage. Different from the existing DOBPF method where disturbance estimation is achieved via the Kalman filter, the disturbance observer adopted in this article is in the form of variational Bayesian adaptive Kalman filter (VBAKF) which deals with the inaccurate process noise covariance matrices in both the dynamic models of the state and the ATVU. Compared with conventional PF approaches, the proposed method, named VBAKF-PF, exhibits enhanced robustness against both the ATVU in the state transition equation and the uncertainties of process noise covariance matrices. The simulation results demonstrate the superiority of VBAKF-PF over both the VBAKF and DOBPF methods.

This article investigates the composite disturbance filtering (CDF) problem for a class of nonlinear stochastic systems subject to composite disturbances. The concerned disturbances include both the unknown deterministic type and the non-Gaussian stochastic type. In order to obtain the optimal state estimation under the influence of unknown input and non-Gaussian noise, a nonlinear CDF method is developed by resorting to the maximum correntropy criterion (MCC). Faced with the nonlinearity of system model as well as a conditionally linear substructure with respect to unknown input, the marginalized unscented transformation is exploited for computation-efficient statistics propagation, and then the statistical linearization is performed to provide a regression model for simultaneous state and unknown input estimation (SSUIE) under the MCC. The proposed filtering algorithm is demonstrated via a numerical example, and further applied to an integrated navigation system (INS). Simulation results confirm that our method has enhanced disturbance rejection ability and improved estimation accuracy in complex non-Gaussian environments.