Optimal sensor selection strategy for discrete-time state estimators

1994 ◽  
Vol 30 (2) ◽  
pp. 307-314 ◽  
Author(s):  
Y. Oshman
Keyword(s):  
Discrete Time ◽  
Sensor Selection ◽  
Selection Strategy ◽  
State Estimators

scholarly journals State estimation in a decentralized discrete time LQG control for a multisensor system

2017 ◽  
Vol 27 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Zdzislaw Duda
Keyword(s):  
Discrete Time ◽  
Global State ◽  
Linear Quadratic ◽  
Control Laws ◽  
Decentralized System ◽  
Multisensor System ◽  
State Estimators ◽  
Local Measurements ◽  
Information Pattern ◽  
The Cost

Abstract In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes. In the paper, contrary to this, the controls are not transmitted between nodes. It leads to nonconventional filtration because the controls from other nodes are treated as random variables for each node. The cost for the additional reduced transmission is an increased filter computation at each node.

scholarly journals Sensor selection scheme in activity recognition based on hierarchical feature reduction

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879380 ◽  
Author(s):  
Yu Wei ◽  
Libin Jiao ◽  
Jie Sha ◽  
Jixin Ma ◽  
Anton Umek ◽  
...  
Keyword(s):  
Mutual Information ◽  
Activity Recognition ◽  
Complete Solution ◽  
Reduction Process ◽  
Feature Reduction ◽  
The Body ◽  
Sensor Selection ◽  
Selection Strategy ◽  
Selection Scheme ◽  
Low Dimensional

To better understand the activity state of human, we might need multiple sensors on different parts of the body. According to different types of activities, the number and slot of required sensors would also be different. Therefore, how to determine the number and slot of necessary sensors regarding to wearers’ experience and processing efficiency is a meaningful study in actual practice. In this work, we propose a novel sensor selection scheme that is based on the improvement of the feature reduction process of the recognition. This scheme applies a hierarchical feature reduction method based on mutual information with max relevance and low-dimensional embedding strategy. It divides the process of feature reduction into two stages: first, redundant sensors are removed with one-order sequential forward selection based on mutual information; second, feature selection strategy that maximizing class-relevance is integrated with low-dimensional mapping so that the set of features will be further compressed. To verify the feasibility and superiority of the scheme, we design a complete solution for real practice of human activity recognition. According to the results of the experiments, we are able to recognize human activities accurately and efficiently with as few sensors as possible.

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