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Content Provider | IET Digital Library |
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Author | Wang, Huan Ge, Jianjun Zhang, De Liu, Guanghong |
Abstract | In sensor management for target tracking, the existing sensor selection methods based on information entropy generally select the sensor that brings the most information gain for target; however, for multi-dimension state vector of target, information gain for target in the specified dimension cannot be guaranteed. For the ambiguity problem in the measure for multi-dimension state vector using information entropy, this study proposes a sensor selection method based on single dimension information gain of the target, which uses particle filter to compute the marginal probability density of target state in the specified dimension and exploits the Kullback–Leibler divergence to measure the information gain of target in the specified dimension, in such a way, by selecting the sensor that brings the most information gain for target in the specified dimension. The proposed method can guarantee higher tracking accuracy gain of target in the specified dimension and obtain more reliable tracking results. Experiment results illustrate that the proposed method has a higher track accuracy of position and velocity in the specified dimension than sensor selection method based on target state entropy. |
Starting Page | 6562 |
Ending Page | 6565 |
Page Count | 4 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 20, Oct (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/20 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0298 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2019-07-10 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Algebra Entropy Filtering Method in Signal Processing Information Entropy Kullback–Leibler Divergence Marginal Probability Density Multidimension State Vector Particle Filter Probability Sensing Device And Transducers Sensor Sensor Management Sensor Selection Method Signal Processing Theory Single Dimension Information Gain Statistics Target State Entropy Target Tracking Vector |
Content Type | Text |
Resource Type | Article |
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