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State-of-the-art indoor localization techniques can reach high localization accuracy, but rely on widely deployed infrastructure which may be absent in less developed regions. To achieve infrastructure-free indoor localization, researchers have proposed to use the inertial sensors on the mobile devices to update the users potential locations by walking steps. Such methods are known to accumulate errors...
A square root unscented kalman filter algorithm based on singular valued decomposition is presented to enhance the robustness in the single observer passive location. The Cholesky decomposition or update is replaced by singular value decomposition, so the new method solves the unstable problem of SRUKF (Square Root unscented kalman filter) which is caused by covariance matrix morbidity in strong nonlinear...
To reduce the learning time of reinforcement learning (RL), hybrid algorithms that combine reinforcement learning with various supervised learning methods have attracted many research interests. However, the global convergence and optimality become one of the main problems for hybrid reinforcement learning algorithms. In this paper, the convergence of a hybrid RL algorithm, which is combined with...
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