Adaptive Waveform Design For Target Recognition In Cognitive Radar

Cognitive radar can improve target recognition performance dramatically byadaptively and dynamically optimizing the waveforms according to the current targetand environment. Adaptive waveform design is a critical problem in the research ofcognitive reader. This dissertation analyzes the relation of the object functions for bothdeterministic signal and stochastic signal to overcome the lack of a general descriptionof the present object functions. Then, we address the impact of the relation of targetand environment to the waveform behavior for its importance in performanceevaluation. After that we study the problem of adaptive waveform design for multipletargets and moving target. At last, we investigate the relationship of recognitionperformance and waveform parameters with fixed modulation but variableparameter.The main scientific contributions of this dissertation are summarized asfollows:In Chapter One, the background and significance of this research is introduced, andits key technologies are summarized. After that, the importance and scientific value aswell as the technology development of the adaptive waveform design for targetrecognition are expatiated. At last, the main contributions of this dissertation aresummed up.In Chapter Two, the optimal waveform design methods based on deterministicsignals detection theory are addressed. First, the relation of radar target recognitionbetween signal detection and waveform design is analyzed, and the model fordeterminant target is introduced. Then, the object function for known determinanttarget in noise based on Neyman-Pearson criterion and minimum probability of errorcriterion are given; A new method for target recognition in clutter based on thedetection performance under Neyman-Pearson theory is proposed, which can obtainthe analytical result of the optimal waveform, and overcomes the disadvantage oftraditional transmit-receive optimization methods, which is neither guaranteed toconverge nor to produce the optimal signal; then object functions for knowndeterminant target recognition in the present articles are analyzed and added into thesystem of object function based on detection theory. At the last part of this chapter, thebehavior of the optimal waveforms is analyzed by simulations, and the impact ofrelation between target and environment on the waveform behavior and its causes arestudied intensively.In Chapter Three, the optimal waveform design methods based on stochasticsignals estimation are addressed. First of all, the stochastic signal model is present,which treats the target to be recognized as a stochastic process. After that, the optimalwaveform design methods based on linear Bayesian estimation theory and information theory are discussed, and a new object function of local SNR is proposed which enrichthe methods of optimal waveform based on linear Bayesian estimation theory. Based onthe results of waveform design in noise, the object functions in clutter based on LinearMinimum Mean Square Error (LMMSE), local SNR and Mutual Information (MI) aregiven. Then the relationship of the object functions both in noise and clutter areestablished, and the behavior of the optimal waveforms is analyzed. Then the impacts ofenvironment on the waveforms obtained by different criteria are studied by large mountof simulations. At last, the method of waveform synthesis is introduced simply. Thischapter and Chapter Two make an intensive collection and analysis on the presentwaveform design methods, and together with the analysis on the waveform behavioraccording to the environment, they will provide a valuable theoretic and technicalsupport for the selection of object function and performance evaluation of optimalwaveforms.In Chapter Four, adaptive waveform design methods for multiple targetsrecognition are studied on the realization that the adaptive waveform design method forsingle target is unsuitable for multiple targets. First of all, the signal model of multipletargets is present, and the measure of recognition goodness is defined, then the problemsof waveform for multiple targets recognition are pointed out. After that, six kinds ofwaveform design methods are proposed from different stages of signal processing. Thefirst one is based on the Weighted Linear Sum of MI (WLS-MI) between theobservation and each target impulse response, which performs well when target numberis less than3. Then to overcome the difficulty of WLS-MI when dealing with moretargets, two methods are proposed based on WLS of Target Impulse Response(WLS-TIR) and the method based on WLS of Autocorrelation Matrix (WLS-ACM)founded in the processing of target impulse responses. Besides, three methods are putforward founded in the processing of optimal waveform, two of which are obtained byWLS of the optimal waveforms for each target, and are called WLS-ST-D andWLS-ST-MI, respectively, according to the kind of criteria of optimal waveform whichare difference maximum and MI maximum, and the left one is to obtain the waveformfor multiple targets by WLS of the optimal signal for each hypothesis based ondifference maximum (WLS-SH-D). Then the algorithm of weight calculation isresearched, based on which an adaptation mechanism for multiple targets recognition isproposed. The simulation results show the performance improvement of the proposedwaveform design methods and the adaptation mechanism relative to the traditionalwaveform.In Chapter Five, the adaptive methods for moving target are researched for thepoor performance of existing methods aims at static target when used for moving target.Two moving scenarios are considered. The first one is the assumption that the target ismoving in a direction different from the radar line of sight, and the second one is the assumption that the target moves parallel to the radar line of sight. The impact of targetmotion on the performance of adaptive waveform is analyzed intensively. After that,two methods of waveform adaptation are proposed aims at the two scenarios. For thefirst scenario, an adaptive waveform method based on the aspect prediction by LeastSquare Support Vector Machine (LSSVM) is proposed; and to improve the recognitionperformance in the second scenario, a method based on information fusion on decisionlevel is proposed, which makes use of the recognition results of both wideband andnarrow band signals. The simulations verify the validity of the proposed method.Besides, the recognition performance improvements of the sensor fusion methodrelative to the wideband signal only method are compared under different energyrestriction. The conclusion about the impact of target to noise ratio on optimalwaveform obtained in the third chapter and the theory of sequential hypothesis test areused to explain the recognition improvements variations.In Chapter Six, the relationship of recognition performance and waveformparameters with fixed modulation but variable parameter is studied. The signal modelwhich is suitable for target with complex structure and moving behavior is depicted, andthe sequential hypothesis test model is present. The average number of observations isdefined as measure of recognition performance in noise, and the relationship of averagenumber of observations for noise of IID (Independent and Identically Distributed) andexponential correlations is studied respectively, then the object functions about thetransmitted signal for both cases are present. After that, KLIN (Kullback-Leiblerinformation numbers) is defined as measure of recognition performance in clutter, andthe relationship of KLIN and transmitted signals are establish based on the analysis ofthe statistical feature of the clutter, and the object function about the transmitted signalis depicted. At the last part, for some given targets, the relationship of the recognitionperformance and parameters (bandwidth, carry frequency and pulse width) fortraditional signals are analyzed by simulation. The research of this chapter providestheoretical references for parameter selection for recognition of given target.In Chapter Seven, the main contributions and innovative work of this dissertationare concluded. And the potential problems and future work to be further researched arepointed out.