An Efficient Spherical Far-Field Extrapolation and Calibration Method for Near-Field Vehicle Antenna Testing

Complete vehicle antenna measurements usually suffer from antenna displacement w.r.t. the measurement center and overly dense sampling. To tackle this challenging problem, this work presents a method combining matrix inversion and high-order expansion theory to effectively overcome the issues of far-field extrapolation for displaced and under-sampled near-field tests. The proposed method firstly uses the Hankel functions with high-order expansion to characterize the inverse relationship between the near-field components and far-field patterns. Then the high-order derivative functions and inverse matrix are combined to calculate the three-dimensional far-field pattern with under-sampled near-field data. Since the high-order expansion with the steepest descent method appropriately characterizes the backward relationship from far field to near field, the proposed method mitigates the adverse effects of under-sampling on forward spherical near-to-far-field transformation (SNFT). Finally, a pattern calibration method is used to compensate for the offset of the SNFT pattern. Compared with the conventional SNFT algorithm, the proposed method can significantly reduce the computational time and improve the accuracy of the electrically large antenna testing.