复合海杂波背景下的弱小目标检测技术

发布时间：2018-08-29 20:20

【摘要】：海杂波背景下的目标CFAR检测一直是雷达探测技术中的热点问题。传统CFAR检测算法多是利用某一杂波的统计分布模型来自适应设计检测门限,从而保持相对恒定的虚警概率。但是当真实观测模型与假设统计分布模型不一致或者杂波参数估计存在误差时,CFAR检测器的恒虚警性能很难得到保证,甚至恶化严重。论文以复杂海杂波下的CFAR检测技术为研究背景,主要工作包括:复杂海杂波的建模仿真、海杂波背景下的CFAR检测以及基于多帧的CFAR检测技术研究等。论文内容概括如下:第二章首先介绍了海杂波模拟常用的各种统计分布模型,包括Log-Normal分布、Rayleigh分布、Weibull分布,分析了各自特点和适用范围,并进行仿真。在综合考虑时间相关性和空间相关性的基础上,提出一种新的基于ZMNL的复合K分布杂波模型产生方法,仿真证明了该方法的有效性,为后文算法研究提供了一定的数据支撑。最后利用实测数据对几种杂波模型拟合效果进行对比表明,复合K分布模型与此次试验实测海杂波的拟合效果最吻合。第三章主要研究了单帧观测距离-多普勒图的恒虚警检测算法。首先给出了自适应参量CFAR检测算法的模型。然后介绍了基于有序数据变化率的CFAR检测算法,并对性能进行分析。最后引入缝隙值这一高阶分形特征作为对目标存在的判断依据,并据此提出基于分形缝隙率特征的单帧RD图像域检测算法,通过仿真数据和实测数据对上述三种算法进行了对比试验,处理结果验证了算法的有效性。第四章研究了基于多帧观测距离-多普勒图的恒虚警检测方法。首先提出基于连续帧序列相关的多帧检测方法,并证明了该方法可以有效降低虚警率。之后介绍了基于动态规划TBD算法的观测模型和算法流程,对动态规划和SOSCA-CFAR方法的检测性能进行对比和分析。最后提出一种基于Gaussian粒子滤波的TBD改进算法,利用高斯密度对后验概率进行估计,并采用序贯比检验算法对多帧数据进行积累,实测数据结果表明算法具备很好的检测跟踪性能。
[Abstract]:Target CFAR detection under sea clutter background has been a hot issue in radar detection technology. Traditional CFAR detection algorithms use a statistical distribution model of a clutter to adaptively design the detection threshold so as to maintain a relatively constant false alarm probability. However, when the real observation model is inconsistent with the hypothetical statistical distribution model or the clutter parameter estimation error exists, it is difficult to guarantee the CFAR performance of the CFAR detector, or even deteriorate seriously. In this paper, the CFAR detection technology under complex sea clutter is taken as the research background. The main work includes modeling and simulation of complex sea clutter, CFAR detection under sea clutter background and CFAR detection technology based on multi-frame. The contents of this paper are summarized as follows: in chapter 2, we first introduce various statistical distribution models used in sea clutter simulation, including Log-Normal distribution, Rayleigh distribution and Weibull distribution, and analyze their respective characteristics and applicable scope, and carry out simulation. On the basis of considering the temporal and spatial correlation, a new generation method of hybrid K-distributed clutter model based on ZMNL is proposed. The simulation results show that the method is effective and provides a certain data support for the later algorithms. Finally, by comparing the fitting results of several clutter models with the measured data, it is shown that the composite K distribution model is the best fit for the sea clutter measured in this experiment. In chapter 3, the CFAR detection algorithm of single frame range-Doppler image is studied. First, the model of adaptive parametric CFAR detection algorithm is presented. Then, the CFAR detection algorithm based on the rate of change of ordered data is introduced, and the performance is analyzed. Finally, the high-order fractal feature of slot value is introduced as the basis for judging the existence of target, and a single frame RD image domain detection algorithm based on fractal slot rate feature is proposed. The simulation data and the measured data are used to compare the three algorithms, and the results show that the algorithm is effective. In chapter 4, the CFAR detection method based on multi-frame observation range-Doppler image is studied. Firstly, a multi-frame detection method based on successive frame sequence correlation is proposed, and it is proved that the method can effectively reduce the false alarm rate. Then the observation model and algorithm flow based on dynamic programming TBD algorithm are introduced. The detection performance of dynamic programming and SOSCA-CFAR method is compared and analyzed. Finally, an improved TBD algorithm based on Gaussian particle filter is proposed. Gao Si density is used to estimate the posterior probability, and sequential ratio test algorithm is used to accumulate the multi-frame data. The measured data show that the algorithm has good performance of detection and tracking.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.52

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