Planar array antenna active scattering directional diagram predication method

Abstract

The invention discloses a planar array antenna active scattering directional diagram predication method. The method comprises the following steps: (1) dividing a large-size planar array scattering field into a plurality of small-size planar sub-array scattering fields according to an active directional diagram method; (2) establishing a BP neural network correction model, wherein the structure of the BP neural network correction model is divided into three layers: an input layer, a hidden layer and an output layer; calculating an output error; carrying out reversed feedback on a difference value of an output result and delta, and sequentially revising a revising amount of a weight according to an error gradient descent method; carrying out model training and finishing the training after a network reaches a convergence target or pre-set iteration times; and applying the active directional diagram method based on the trained neural network correction mode to supply a scattering directional diagram of the large-size planar array. By the aid of the planar array antenna active scattering directional diagram predication method, an optimization algorithm of a neural network is used for optimizing a traditional active scattering directional diagram predication method of an array antenna; the error control can be carried out better, and a predication way is provided for grasping the scattering directional diagram of the large-size planar array.

Description

technical field [0001] The invention relates to the communication field, in particular to a method for predicting the active scattering pattern of a planar array antenna. Background technique [0002] Predicting the Scattering Direction of an Array Antenna Figure 1 Generally, numerical algorithms based on finite element and method of moments are used for prediction. However, for larger planar array antennas, the computational time and computational memory resources required by the numerical algorithm increase dramatically, making it difficult to estimate the scattering pattern. Some researchers at home and abroad have proposed an active pattern method for large arrays based on the scattered field calculation of small arrays, which greatly reduces the algorithm's demand for computing time and computing memory resources. However, due to the approximation error of this method, the error of its calculation result is relatively large. Since the approximation error of the activ...

AI Technical Summary

Problems solved by technology

However, due to the approximation error of this method, the error of the calculation result is relatively large.

Since the approximation error of the active pattern method is a nonlinear error, the conventional linear error elimination method and regression method are invalid

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