Radar State Cognition and Clutter Suppression Method Based on Naive Bayes Learning

Abstract

As the operation environment is increasingly complex, air defense radar is affected by various kinds of clutter, including ground clutter, meteorological clutter, etc. Ground clutter is a type of fixed clutter and can be effectively removed through filters such as MTI. While meteorological clutter usually exists in a complex and diverse motion state, which is difficult to be suppressed, and a large amount of meteorological clutter interference may lead to plots saturation and more false track on radar, thereby affecting the efficiency of radar target detection and accurate tracking. To address the above issues, a meteorological clutter suppression method based on Naive Bayes learning is proposed in this paper, in which test data of meteorological clutter and non-clutter obtained from air defense radar platform is processed using statistical and non-statistical data processing methods to compare the spatial-temporal features of radar clutter and non-clutter, and through Naive Bayes network model establishment and clutter area positioning, state cognition and effective filtering of meteorological clutter interference are realized. Simulation results also indicate that the method mentioned in this paper can realize an effective cognition of radar detection state and filtering of a large amount of meteorological clutter.