Adaptive generalized sidelobe cancelers (GSCs) have been widely used to enhance the desired signal and instantaneously suppress interference signal and noise. However, the GSC beamforming method must know the direction of arrival (DOA) of the desired signal in advance. In this paper, we consider the case of a sensor array application in free-field air in which the target signal source is moving, leading to a time-varying DOA problem. Through analysis of the GSC output error signal, we propose an effective method for estimating the time-varying DOA for a GSC. The new method avoids the intensive complexity requirements of conventional DOA estimation algorithms such as the multiple signal classification algorithm and estimation of signal parameters via rotational invariant techniques. In addition, the convergence performance of adaptive GSC algorithms suffers from an error signal in the presence of the desired signal. A simple augmented Kalman filter (AKF) is employed to calculate the beamformer's weighting coefficients, removing the influence of the desired signal from the GSC output to improve the convergence performance. A simulation evaluation of the signal-to-interference-plus-noise ratio (SINR) revealed that the AKF algorithm combined with the new DOA tracking method has a better convergence rate and SINR performance than other adaptive GSC algorithms of similar complexity such as the standard Kalman filter and recursive least squares.
- Kalman filter (KF)
- direction-of-arrival (DOA)
- generalized sidelobe canceler (GSC)
- sensor array