One-dimensional linear array direction finding method under two-dimensional angle dependence error based on deep learning

• Main Authors: PAN YUJIAN, GAO XIAOXIN, WANG FENG, YAO MIN
• Format: Patent
• Language: Chinese; English
• Published: 22.01.2021
• Subjects: ANALOGOUS ARRANGEMENTS USING OTHER WAVES; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES; MEASURING; PHYSICS; RADIO DIRECTION-FINDING; RADIO NAVIGATION; TESTING

The invention discloses a one-dimensional linear array direction finding method under a two-dimensional angle dependence error based on deep learning. According to the method, based on the characteristic that deep learning is good at approximating a complex nonlinear function, the problem of two-dimensional angle dependent array error calibration is solved through machine learning. In order to process azimuth angle dependence and pitch angle dependence of array errors at the same time, two-dimensional data acquisition is carried out, namely, different azimuth array steering vectors are acquired at different pitch angles. The measurement data are expanded by adopting local array flow pattern interpolation so as to reduce the over-fitting risk of the deep learning model; and deep learning iscarried out on the data with the lowest signal-to-noise ratio to enable the data to adapt to noisy signals. The method is used for improving the precision of one-dimensional linear array direction finding of the two-dimensio