A New Strategy for Combining Nonlinear Kalman Filters With Smooth Variable Structure Filters

Bayesian filters exemplified by the celebrated Kalman Filter (KF), and its non-linear variants rely on a fairly accurate state-space model of the system under study. To address the issue of modelling uncertainty in state estimation, the Smooth Variable Structure Filter (SVSF) was proposed in 2007. Since then, several SVSF variants have been proposed to extend its domain of applicability. In some of these algorithms, SVSF has been viewed as a complementary approach alongside the well-established nonlinear Kalman Filters. This paper seeks a general framework for SVSF formulation to unify some of the recent developments in SVSF literature under one umbrella. In this way, the SVSF variants are revisited as special cases of the proposed framework. This paper proposes a new strategy to combine SVSF filters with other nonlinear filters and puts existing SVSF filters under one umbrella. Six filters are formulated based on the proposed method of combining filters. The proposed filters relax limitations of existing SVSF variants, making the proposed filters more universal. In simulations, the new filters outperform state-of-the-art nonlinear KFs and some existing SVSF filters. To demonstrate the merits of the proposed framework, the new filters are applied to target tracking and are comparatively evaluated.

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State-Based Decoding of Force Signals From Multi-Channel Local Field Potentials

The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat’s primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder.

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