Aerial Flight Target Identification And Threat Level Assessment System Based On Radar Data

Target threat assessment is one of the hot and difficult areas in information fusion.Traditional target threat assessment requires experienced researchers to analyze data by radar,which leads to a series of problems such as slow speed and low fault tolerance.With the increase of unmanned intelligent equipment in modern warfare,the traditional threat assessment methods are gradually being eliminated.Therefore,there is an urgent need to establish a practical target threat assessment system to achieve a reasonable allocation of weapon resources for air defense systems,ensure effective interception of various combat targets in complex contexts,and further improve the overall operational performance of weapon systems.Under the conditions of information-based combat,the air combat environment is becoming increasingly complex and the types of targets are diverse,which brings great difficulties to air defense interception.In particular,the threat assessment of air attack targets in air defense operations has gradually become a core issue of concern for air defense command and control systems.This paper mainly studies the design and implementation of airborne target identification and threat assessment system based on radar data,and uses recurrent neural network to solve the problem of low accuracy rate in airborne target identification,which has greatly improved the accuracy rate of airborne target identification.At the same time,an airborne target threat assessment system is proposed,using Bayesian networks and fuzzy sets,as well as an assignment method combining AHP and entropy method,and the Bayesian network inference process is improved and optimized to improve the credibility of the threat assessment results.The details of the study are as follows:(1)For the problem of low accuracy in traditional airborne flight target recognition,a recurrent neural network recognition algorithm model based on Multi-dimensional and Bidirectional Gated Recurrent Unit,referred to as MD-BGRU model,is proposed.The flight altitude,velocity,latitude,longitude and RCS data detected by radar are preprocessed with missing data,preprocessed with anomalous data and smoothed with RCS noise data by Savitzky-Golay filter,and then used as the sequence input data of MD-BGRU.The final result of the experimental analysis concludes that the accuracy of airborne target identification is effectively improved relative to the traditional SVM algorithm,and also provides data support for the target type dimension in the following threat level assessment.(2)To address the credibility and time complexity of the threat assessment of airborne targets,we propose a threat assignment method based on Bayesian networks and fuzzy sets,AHP and the entropy weight method,which makes the dimensions used in the threat assessment both qualitative and quantitative data,and reduces the number of nodes of Bayesian networks,and makes the assignment both subjective and objective.This effectively improves the credibility of the threat assessment.At the same time,the inference process of Bayesian network is improved and optimized,and the Bayesian network is converted into a structured graph according to certain rules,and the optimal variable elimination order is determined through the structured graph,which reduces the number of calculations of each node and reduces the time complexity of the inference process.