波达角(Direction of Arrival，DOA)估计是现代阵列信号处理领域中的重要研究内容，它在通信、雷达、声纳等领域有着非常广泛的应用。为了使探测系统具有对同时到达雷达信号DOA进行估计的能力，本申请主要针对阵列探测系统面临的复杂电磁环境，围绕阵列天线DOA估计在应用所面临的关键问题展开研究。提出以Bootstrap 方法解决DOA估计所面临的众多重点问题，如接收目标脉冲较窄，采样数据较少；接收目标信号长时不平稳等。Bootstrap方法是一种以计算机仿真代替实际实验采样的方法，通过对接收的实际少量数据样本重新组合，重新计算实现对大样本数据的替代，从而实现对参数的估计。该方法在小样本情况下，比传统的最小方差法和最大似然方法更有吸引力，估计精度更高。该类方法正在被国外广大学者应用到各个参数估计领域，国内该方法应用较少，在阵列信号处理领域应用更少，因此研究该方法在DOA估计中的应用

Direction of arrival (DOA) estimation by array is an important research branch in the field of modern signal processing. It has a great variety of applications in many fields, such as communications, radar and sonar, etc. In order to make passive detection system have the ability of measuring multiple radiant signals arrived simultaneously and improve the angle resolution of DOA estimation, application of super-resolution DOA estimation algorithm by array to passive detection system is reseached deeply .. According to the problems faced to DOA estimation,such as signal pulse is time constraints or the process is nonstationary and only small portions of stationary data are considered, the new novel path to be applied in array signal proceesing. Here the bootstrap method is going to introduce into DOA estimation. in simple word,the bootstrap does with a computer what the experimenter would do in proctice.With the bootstrap, the obervations are randomly ressigned ,and the estimatees recomputed, there assignements and recomputions are done thousnads of times and treated as reperted experiments .Bootstrap is an extremely attractive tool in that it requires very little in the way of modeling,assumptions, or analysis. an it canbe applied in an automatic way. the bootstrap is a computer-based method that substitutes considerable amounts of computation in place of theoretical analysis.in an eraof exponentially declining comoputions are done thousands of times and treated as repeated experiments..Use the suitable bootstrap can sovle some of problems in array signal processing.firstly,theoretical and practical work have shown that bootstrap methods are potentially superior to conventional parameter estimation method ,such as least square and MLE in small-sample condition. in some circumstances where standar approaches that invoke strong assumptions are judge inappropriate,the bootstrap may be appliced.so in the circumstances where sample impluse is narrow or signals are.non-stationary ,the bootstrap methods have great research value