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The Kalman filter is considered as an optimal filter with the hypothesis of gaussian noise and linear model. For nonlinear model several approaches have been proposed and Unscented Kalman Filter (UKF) seems to be one of the most accurate. In this study, we wonder if an appropriate constraint can enhance the efficiency of UKF. We propose a new algorithm called Constrained Sigma Points (CGS) that constrained...
The two-stage Kalman filter was proposed with the objective to avoid bias when the system is in presence of input signals. A common technical difficulty in this technique is that the dynamics of input signal is always unknown whereas the optimality of such filter can only be achieved with sufficient priori knowledge (i.e., known dynamics and statistics). Unbiased minimum-variance filter is capable...
Natural disasters, such as earthquake, typhoon and flood, have caused great loss of lives and property each year, which makes emergency monitoring and rescue an imperative problem to be addressed. In this paper, we designed a novel monitoring and rescue system based on wireless sensor network (WSN) for disaster scenarios, which combines environment monitoring, information transmission, and emergency...
This paper studies distributed cooperative localization problem for a multirobot team with one leader and two followers. Each robot in the team is equipped local sensors and can exchange data with its neighbors through wireless communication network. A distributed localization algorithm is developed by using extended Kalman filter (EKF) scheme. In every sampling period, each member in the team estimates...
This paper introduces a new robust 3D ultrasound needle detection approach integrated in a 3D needle steering system associated to a real-time path planning. The robustness of an existing algorithm is improved by limiting the needle detection to a curvilinear region of interest (ROI) using a novel mechanical-based prediction model. This linear model is also used in a Kalman filter to reduce detection...
This paper studies the mean square quadratic (MSQ) detectability for multi-output networked systems over finite-state fading channels. The unreliability from the plant output to the estimator input is described by multiplicative noises. A finite-state random process is introduced to model time-varying fading channels. Necessary and sufficient conditions for MSQ detectability are given in the form...
The distributed state estimation problem for the time-varying stochastic system is considered in this paper. Through minimizing the mean square error for each sensor, the optimal distributed Kalman filter (ODKF) based on matrix weights is derived. To deal with the computation complexity of ODKF, a suboptimal distributed filter is proposed, which keeps the consistent property of estimation. With the...
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