Abstract
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.
Highlights
Estimation of the incident signals’ directions, or angle-ofarrival (AOA) estimation, is a fundamental problem in numerous applications such as radar, sonar, radio astronomy, and mobile communications
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
We demonstrate that the proposed algorithm yields superior performance over other popular methods, especially in unfavorable scenarios involving low signal-tonoise ratio (SNR), highly correlated signals, short data samples, and small arrays
Summary
Estimation of the incident signals’ directions, or angle-ofarrival (AOA) estimation, is a fundamental problem in numerous applications such as radar, sonar, radio astronomy, and mobile communications. The benefits of using smart antennas are that the sender can focus the transmission energy towards the desired user while minimizing the effect of interference, and the receiver can form a directed beam towards the sender while simultaneously placing nulls in the directions of the other transmitters This spatial filtering capability leads to increased user capacity, reduced power consumption, lower bit error rates (BER), and larger range coverage [9,10]. We demonstrate that the proposed algorithm yields superior performance over other popular methods, especially in unfavorable scenarios involving low SNR, highly correlated signals, short data samples, and small arrays.
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