Study Of Information Aided Radar Detection And Tracking Coprocessing

Target detection and tracking are usually regarded as two separate units in traditional radar systems.Target tracking information can often be used to improve the radar detection performance.However,current study focuses on target location information provided by the target tracking algorithms,which decreases the decision threshold but introduces unnecessary false alarms when target signal-to-noise ratio(SNR)is sufficiently large.With this consideration,an integrated detection and tracking method based on target SNR information is studied,which takes the SNR information obtained at the target tracking period into account.The information is fed back to a target detector to adjust detection threshold,such that a better balance between miss probability and false alarm rate can be achieved.For this purpose,we first study target SNR prediction methods in the tracking mode,then the SNR characteristics are used to improve the SNR of the subsequent echo by optimizing the frequency and the detection performance.Finally,the main strategy and performance of the integrated detection and tracking method are analyzed.The main content is organized as follows:The first chapter briefly summarizes the domestic and foreign research status of the integrated detection and tracking method based on aided information and the background of our methods.The second chapter mainly explores the target SNR prediction methods in the tracking mode.First,the necessity of SNR prediction and its physical principle are analyzed.The SNR prediction process is deduced in accordance to the radar equation.Second,the target echo correlation model is used to devise five SNR prediction methods,such as the Wiener filtering,Doppler information aided estimation,Autoregressive(AR)model,maximum entropy model and least squares criterion.Finally,the predictive performance between the five methods was compared with numerical results.The third chapter mainly studies the online optimization method of SNR frequency planning.The SNR is related to the radar frequency.Based on this fact,this chapter uses a polynomial fitting method to establish a functional relationship between the SNR and frequency,and obtains a radar frequency with a higher SNR through the optimization function.This method improves radar detection performance and enables effective detection of weak target.The fourth chapter mainly explores the integration of detection and tracking based on SNR information.At the background of a traditional monostatic radar detection system,the target detection strategy in the tracking mode is studied.An integrated detection and tracking method based on SNR information is proposed.The core idea is that when the SNR is high,using a higher threshold which determined by SNR can not only guarantee the detection probability required by the system,but also reduce the false alarm rate.And the threshold determined by SNR will be low when the SNR is low,considering a higher detection threshold determined by location information can ensure the false alarm rate would not be too high.Through this scheme,the balance between detection probability and false alarm rate of the radar detection system is achieved.The fifth chapter is the summary of the full text and the outlook for the future work.