Research On Adaptive Waveform Design Based On The Euclidean Distance Between PDFs

As a product of the primary stage of intelligent radar,cognitive radar has very important applications in military and civilian fields.Adaptive waveform design is one of the key technologies of cognitive radar,and it is the epitome of cognitive radar's intelligence.Since the proposal of cognitive radar,adaptive waveform design has attracted extensive interest among scholars with related fields.This paper addresses the adaptive waveform design issue based on the Euclidean distance between probability density functions.Chapter 1 sorts out the development process and research status of cognitive radar,and unify the existing cognitive radars' architecture.The main mechanism and physical nature of the existing adaptive waveform design algorithms are reclassified and summarized.Based on the ambiguity function,the mathematical model between the transmitted waveform-noise and clutter environment-radar performance is established in chapter 2,which lays a theoretical foundation for the adaptive waveform design based on environmental cognition.Finally,according to the specific noise and clutter environment and combined with the radar task,two adaptive waveform design algorithms are proposed respectively by using the Euclidean distance between the probability density functions as the analysis tool:Aiming at radar resolution in noise environment,chapter 3 proposes an adaptive waveform selection algorithm that optimizes the radar practical resolution based on the Euclidean distance between the probability density functions(PDF-ED).In this chapter,PDF-ED is introduced to represent radar resolution and the adaptive waveform selection algorithm maximizing the resolvability of targets is proposed then.The simulation results validate the effectiveness of the proposed algorithm both in static and dynamic scenarios.Trying to addressing the problem that some small targets tend to be masked by the sidelobes of strong clutter,chapter 4 proposes a code-length joint optimization algorithm based on phase-encoded signal to reduce the clutter.By suppressing the side lobe of the ambiguity function where the strong clutter is located,the signal-to-clutter ratio in the target unit can be improved.Finally,the main work and innovations of this paper is summarized,and the future research direction is pointed out.