Abstract
| Language | English |
|---|---|
| Pages | 358-364 |
| Number of pages | 7 |
| Journal | Wireless Sensor Networks |
| Volume | 2 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2010 |
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Keywords
- angle-of-arrival
- AOA
- radar
- sonar
- radio astronomy
- mobile communications
- communication
- colored noise fields
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Accurate angle-of-arrival measurement using particle swarm optimization. / Li, M.; Ho, Kwok Shun; Hayward, G.
In: Wireless Sensor Networks, Vol. 2, No. 5, 05.2010, p. 358-364.Research output: Contribution to journal › Article
TY - JOUR
T1 - Accurate angle-of-arrival measurement using particle swarm optimization
AU - Li, M.
AU - Ho, Kwok Shun
AU - Hayward, G.
PY - 2010/5
Y1 - 2010/5
N2 - As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates.
AB - As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates.
KW - angle-of-arrival
KW - AOA
KW - radar
KW - sonar
KW - radio astronomy
KW - mobile communications
KW - communication
KW - colored noise fields
U2 - 10.4236/wsn.2010.24047
DO - 10.4236/wsn.2010.24047
M3 - Article
VL - 2
SP - 358
EP - 364
JO - Wireless Sensor Networks
T2 - Wireless Sensor Networks
JF - Wireless Sensor Networks
SN - 1945-3078
IS - 5
ER -