Name
Abstract | ||
|---|---|---|
In this letter, a receding horizon path planning algorithm is introduced for unmanned aerial vehicle swarms to cooperatively localize a moving radio frequency transmitter. In the core of the proposed algorithm is a model to predict the Fisher information matrix. Using this prediction model, we formulate the most favorable course of action to solve the path planning using local optimization, which helps the system as a whole to achieve the goal over a finite receding horizon. The effectiveness of the proposed algorithm is demonstrated by comparing the proposed mechanism with the non-predictive cooperative technique through computer simulation. It is shown that the expected estimation error can be significantly reduced using the proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LCOMM.2016.2603977 | IEEE Communications Letters |
Keywords | Field | DocType |
Unmanned aerial vehicles,Target tracking,Trajectory,Optimization,Computational modeling,Predictive models | Motion planning,Transmitter,Course of action,Control theory,Computer science,Horizon,Real-time computing,Radio frequency,Fisher information,Local search (optimization),Trajectory | Journal |
Volume | Issue | ISSN |
21 | 6 | 1089-7798 |
Citations | PageRank | References |
11 | 0.63 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farshad Koohifar | 1 | 13 | 1.04 |
| Abhaykumar Kumbhar | 2 | 54 | 4.29 |
| Ismail Güvenç | 3 | 2041 | 153.03 |