Angle-dependent complex array error calibration method based on deep neural network

Abstract

The invention discloses an angle-dependent complex array error calibration method based on a deep neural network, and aims at the problem that a traditional signal processing method is difficult to process calibration of an angle dependent complex array error. In order to process an off-grid target, an adopted local array flow pattern interpolation method can better adapt to the angle dependent array error based on grid point data measured in a darkroom in comparison with a global array flow pattern interpolation method; moreover, the input characteristics of the deep neural network select phases under complex conditions instead of actual phases so that the jump problem of the phases at + / -pi edges can be avoided; and finally, in order to enable the neural network to adapt to noisy signals, only training data needs to be generated on data of a single signal-to-noise ratio, and training data does not need to be generated on multiple signal-to-noise ratios so that the training data volume is reduced, and the training time is shortened. Compared with the traditional signal processing method, the method has the advantages of being smaller in residual array error after calibration and better in calibration performance.

Description

technical field [0001] The invention belongs to the field of array signal processing, and in particular relates to the calibration of receiver sensor array errors such as communication, sonar, radar, etc., and specifically relates to a calibration method suitable for angle-dependent complex array errors based on a deep neural network. Background technique [0002] Array signal processing is a technique widely used in civilian and military fields such as communications, sonar, radar, seismology, astronomy, and more. Its working method is to use multiple sensors placed in a specific form to detect and estimate the received signal at the same time. In an ideal situation, the response of each sensor is the same and independent, and the position of the sensor is known precisely, so the array steering vector has an exact analytical expression. At this time, the relevant array signal processing algorithm can be directly used to process the signal, such as direction finding and bea...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the difficulty of calibrating angle-dependent complex array errors in existing traditional signal processing methods, the present invention proposes a method for calibrating array errors based on deep neural networks. The specific technical solutions are as follows:

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