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A new method for adaptive beamforming in uniform linear arrays (ULAs) is introduced. The proposed method is based on generalised loading of the covariance matrix of the data vector received by the array. The covariance matrix is loaded with a Hermitian matrix which is chosen to shape the directional response of the array. Simulation results show that the proposed approach offers better handling of mismatches between presumed and actual array steering vectors. It produces output signal‐to‐interference‐plus‐noise ratio values higher than some well‐known approaches in the literature with much less complexity. [ABSTRACT FROM AUTHOR]

The application of the complex quadratic form as the decision boundary for complex‐valued data classification is described. This function is always real when its matrix is Hermitian. Thus, a simple sign function to classify the input data is used. This matrix is obtained through an iterative learning process similar to the Rosenblatt algorithm. The concept of the Frobenius matrix norm is used to prove that the proposed learning algorithm converges if a solution exists. This approach is different from other complex‐valued neural networks that use optimisation techniques or feature mapping. An artificial neuron that uses a complex quadratic form as the decision boundary is called a complex quadratic neural unit. [ABSTRACT FROM AUTHOR]