Name
Title | ||
|---|---|---|
Neural Network Aided Unscented Kalman Filter for Maneuvering Target Tracking in Distributed Acoustic Sensor Networks |
Abstract | ||
|---|---|---|
A new neural network aided unscented Kalman filter is presented for tracking maneuvering target in distributed acoustic sensor networks. In practice, the system dynamics of these problems are usually incompletely observed, there may be large modeling errors when the target is maneuverable and some parameters of the system models may be inaccurate. So we propose using an offline trained neural network to correct these errors, the nonlinear inferring process is done by the normal unscented Kalman filter. This method doesn't need complex modeling for tracking maneuvering target and is very suitable for real-time implementation because the implementation time is only the sum of the unscented Kalman filter and the neural network recall time |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICCTA.2007.88 | ICCTA |
Keywords | Field | DocType |
neural network,distributed acoustic sensor networks,acoustic sensor network,normal unscented kalman filter,acoustic sensor networks,kalman filters,kalman filter,learning (artificial intelligence),target tracking,nonlinear inferring process,neural network recall time,unscented kalman filter,maneuvering target,maneuvering target tracking,telecommunication computing,neural network aided unscented kalman filter,new neural network,offline trained neural network,wireless sensor networks,complex modeling,implementation time,neural nets,learning artificial intelligence,sensor network,model error,system dynamics,neural networks,system modeling,information technology,filtering,error correction | Computer vision,Extended Kalman filter,Nonlinear system,Control theory,Computer science,Kalman filter,Moving horizon estimation,Acoustic sensor,Artificial intelligence,System dynamics,Artificial neural network,Wireless sensor network | Conference |
ISBN | Citations | PageRank |
0-7695-2770-1 | 5 | 0.53 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhijun Yu | 1 | 12 | 2.73 |
| Shao-Long Dong | 2 | 8 | 1.03 |
| Jianming Wei | 3 | 33 | 5.38 |
| Tao Xing | 4 | 6 | 0.95 |
| Haitao Liu | 5 | 151 | 11.39 |