Iterative robust adaptive beamforming

Abstract

The minimum power distortionless response beamformer has a good interference rejection capability, but the desired signal will be suppressed if signal steering vector or data covariance matrix is not precise. The worst-case performance optimization-based robust adaptive beamformer (WCB) has been developed to solve this problem. However, the solution of WCB cannot be expressed in a closed form, and its performance is affected by a prior parameter, which is the steering vector error norm bound of the desired signal. In this paper, we derive an approximate diagonal loading expression of WCB. This expression reveals a feedback loop relationship between steering vector and weight vector. Then, a novel robust adaptive beamformer is developed based on the iterative implementation of this feedback loop. Theoretical analysis indicates that as the iterative step increases, the performance of the proposed beamformer gets better and the iteration converges. Furthermore, the proposed beamformer does not subject to the steering vector error norm bound constraint. Simulation examples show that the proposed beamformer has better performance than some classical and similar beamformers.

Elman Recurrent Neural Network Application in Adaptive Beamforming of Smart Antenna System

“…The signals received by different elements of an antenna array combined to form a single output. Classically, this is achieved by decreasing the mean square error (MSE) between the actual array output and desired output [14]. Fig.3 shows the block diagram of adaptive beamformer.…”

“…In this step the neural network will estimate the weights of the new incoming signals that network never seen before during training step (network did not trained on them) ,the correlation matrix of the received signal are calculated ,and then the only input to the neural network is B vector that given in eq (14), and the network will estimate (or predict) the output (optimum weight (W op ) ) based on empirical knowledge gained through the training process. These weights are used by antenna array system to form multiple narrow beams toward the desired directions and make nulling in the direction of interferences.…”

Mainlobe interference suppression via eigen-projection processing and covariance matrix sparse reconstruction

“…In recent years, adaptive beamforming has been deeply researched and widely applied in radar, wireless communication, microphone and other fields. Sidelobe interferences can be suppressed by adaptive beamforming, effectively [1,2,3]. However, when the interference falls into mainlobe area, the conventional adaptive beamforming may lead to the pattern distortion and the output signal to interference plus noise ratio (SINR) decreased, et al [4].…”

Radar Angle of Arrival System Design Optimization Using a Genetic Algorithm

“…Beamforming can be performed adaptively by constructing new beams relative to the input data, increasing the accuracy of AOA estimation [31]. Digital beamforming can even be applied in both azimuth and elevation to fully construct 3D images [32].…”