2D Angularly Dependent Array Error Calibration for 1D Array via Neural Network with Local Manifold Interpolation

Abstract

The calibration of the angularly dependent array error is a challenging task for signal processing. In this paper, we propose a neural network (NN)-based two-dimensional (2D) calibration method for a linear array. Firstly, the array steering vectors are measured on an azimuth grid at different elevations in an anechoic chamber, and the off-grid steering vectors are derived by the proposed local manifold interpolation (LMI) technique to reduce the risk of model overfitting. Then, the phase differences are extracted to form the features of the training data. At last, noise is added to the training data to enable the NN model to generalize well to the noisy data. The proposed method is evaluated by the indoor and outdoor measured data from a 77 GHz automotive radar and is compared with the conventional signal processing-based methods. The evaluation results show that a single NN model trained at the lowest signal-to-noise ratio (SNR) outperforms conventional methods by at least 55% on average over the entire SNR range and gives close performance to the perfect array without array error at low to medium SNR.