A multi-source data fusion water radio interference identification positioning method and system

Abstract

This application relates to the field of intelligent identification and localization of radio interference, specifically to a method and system for identifying and localizing radio interference on water using multi-source data fusion. The method includes the following steps: multi-source data acquisition; multi-source data fusion processing; constructing a rule base and filtering suspected abnormal signals from the spectrum situation map through threshold judgment and rule matching; calling a support vector machine model, using multi-dimensional static signal features as input, constructing a classification hyperplane through radial basis kernel functions, and distinguishing normal signals from abnormal signals among suspected abnormal signals; calling a decision tree model to perform scenario-based judgment on suspected abnormal signals to identify abnormal signals; calling a convolutional neural network model to convert the time-domain signal into a spectrum map through short-time Fourier transform, and extracting texture features through multi-layer convolution to identify abnormal signals; and the steps of abnormal signal screening and localization. This method and system can achieve real-time identification and high-precision localization of interference signals.

Images