A target can be positioned by wireless communication sensors. When the range based sensors have biased measurements, an expectationmaximization (EM) algorithm is proposed to jointly estimate the target state and sens...
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ISBN:
(纸本)9781467391047
A target can be positioned by wireless communication sensors. When the range based sensors have biased measurements, an expectationmaximization (EM) algorithm is proposed to jointly estimate the target state and sensors' biases, including the batch EM and sliding window EM algorithms. To implement the algorithms, the Iterated Extended Kalman Smoother (IEKS) is also embedded in the EM algorithm. The simulation results show that the batch algorithm has the best estimation performance. The sliding window EM algorithm has better estimation performance than the augmented UKF (AUKF) algorithm. Since batch EM algorithm is not suitable for real time estimation scenario, the sliding window EM algorithm is recommended for real time target positioning.
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Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i.e., the number of changes between two consecutive solutions is bounded). We provide algorithms in this setting with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances. Copyright 2024 by the author(s)
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