Name
Abstract | ||
|---|---|---|
In three-dimensional space, current target tracking algorithms based on wireless sensor networks are mainly non-iterative and operated with only current measurement result. A typical example is the least square algorithm. Compared with iterative algorithms which use historical information, such as extended Kalman filter, non-iterative algorithms always achieve lower accuracy but can avoid the dependence upon prior knowledge of system noises. In this letter, we firstly proposed a minimum residual localization algorithm based on particle swarm optimization, which is a non-iterative algorithm. Then, a data-fitting strategy is adopted to convert the non-iterative algorithm into iterative one without knowledge of system noise. Hence, the historical information can be used to improve the accuracy of non-iterative algorithm significantly. Simulation results show that the proposed algorithm acquires better localization result with strong adaptability for different motion. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11277-016-3783-x | Wireless Personal Communications |
Keywords | Field | DocType |
Wireless sensor network, Three-dimensional target tracking, Data fitting, Minimum residual, Particle swarm optimization | Adaptability,Particle swarm optimization,Residual,Computer vision,Three-dimensional space,Extended Kalman filter,Curve fitting,Computer science,Brooks–Iyengar algorithm,Artificial intelligence,Wireless sensor network | Journal |
Volume | Issue | ISSN |
94 | 4 | 1572-834X |
Citations | PageRank | References |
3 | 0.40 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| xu ning | 1 | 25 | 15.72 |
| Yunzhou Zhang | 2 | 219 | 30.98 |
| Du Zhang | 3 | 285 | 42.16 |
| Shuying Zhao | 4 | 3 | 0.40 |
| Wenyan Fu | 5 | 45 | 4.20 |