As one of the major methods for location positioning, angle of-arrival (AOA) measurement is a significant technology in wireless sensor networks (WSN), which can be explored for node and target localization, improving communication quality, location-based routing, sensor management, and other diverse applications. Due to the size, cost and energy constraints typical of a sensor node and the sophisticated resident environment, popular high resolution AOA estimation methods may malfunction and perform poorly. In this paper, we propose an algorithm for bearing estimation in unknown noise fields and harsh WSN scenarios. By modelling the 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 presented for optimization of the ML cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior bearing estimates, especially in unfavourable scenarios typical of a sensor network.
|Publication status||Unpublished - Aug 2008|
|Event||16th European Signal Processing Conference - Lausanne, Switzerland|
Duration: 25 Aug 2008 → 29 Aug 2008
|Conference||16th European Signal Processing Conference|
|Period||25/08/08 → 29/08/08|
- wireless networks