岸对海侦察雷达舰船小目标检测方法研究

发布时间：2018-06-10 08:49

  本文选题：杂波建模 + 多参数联合二进制检测　； 参考：《哈尔滨工业大学》2017年硕士论文

【摘要】：面对日益复杂的海洋环境,以及以海洋环境为掩护,不断优化升级的武器技术严重威胁着沿海地区的安全,同时也意味着对现代雷达探测性能的要求越来越高,海洋环境下的小目标检测作为现代雷达技术研究中的热点与难点持续吸引着大家的注意力。本文针对海面舰船小目标检测技术,进行了专题研究。提出了一种基于M/N检测器思路的多参数联合二进制检测方法,利用实测数据对该方法的有效性作验证。论文具体工作可分为三部分:第一:海杂波建模与统计特性分析,文中采用瑞利、威布尔、对数正态、K分布、Gamma分布、广义Gamma分布6种模型对杂波进行拟合分析,除K分布外,广义Gamma分布在特定条件下都可以退化为其他4种分布,广义Gamma模型作为三参数模型,能更好地适应高分辨率雷达海面回波。利用高阶矩估计和极大似然估计方法对6种杂波模型进行参数估计,使用卡方检验和K-S检验2种拟合优度方法对6种分布的拟合程度进行评估,确定最优拟合模型。经实测数据分析表明,统计过程中提取的模型形状参数和杂波序列的去相关时间能够有效区分纯杂波单元与目标单元。第二:多参数联合二进制检测,针对单参数检测的局限性,根据M/N检测器思路,本文提出了一种多参数联合二进制检测算法,在文中具体指双门限检测。第一门限采用单元平均恒虚警检测算法,根据杂波建模结果选择合适的检测器。对通过第一门限的距离单元重新编号进行第二门限检测,第二门限使用的多参数包括分形参数Hurst指数、递归参量、统计特征参数等。对各参数设置单门限,统计检测结果,设置二进制检测门限做最后判决。利用IPIX雷达数据对所用参数有效性进行初步验证,受限于IPIX雷达数据距离单位的数目,在使用恒虚警检测时易造成较大的恒虚警损失,因此采用本单位雷达系统在外场试验收集的X波段数据对多参数联合二进制算法进行验证,证实了该方法对海面小目标检测的有效性。第三:基于动态规划的检测前跟踪,利用目标在多帧数据间相关性强于噪声背景,数据积累时目标能量大于噪声背景的特点实现弱目标的检测。根据指标函数确定检测结果,同时利用检测结果回溯航迹。通过仿真和半实测仿真对该方法的有效性进行验证。
[Abstract]:In the face of the increasingly complex marine environment and the continuous optimization and upgrading of weapons technology, which is sheltered by the marine environment, it is a serious threat to the security of coastal areas, and it also means that the performance of modern radar detection is becoming more and more demanding, As a hot and difficult point in the research of modern radar technology, small target detection in marine environment continues to attract people's attention. In this paper, a special research on small target detection technology of sea surface ships is carried out. A multiparameter combined binary detection method based on M- N detector is proposed. The validity of the method is verified by the measured data. The specific work of this paper can be divided into three parts: first, sea clutter modeling and statistical characteristic analysis. In this paper, Rayleigh, Weibull, logarithmic normal K distribution Gamma distribution and generalized Gamma distribution are used to fit and analyze clutter, except K distribution. The generalized Gamma distribution can degenerate into the other four distributions under certain conditions. As a three-parameter model, the generalized Gamma model can better adapt to the high-resolution radar sea surface echo. High-order moment estimation and maximum likelihood estimation are used to estimate the parameters of six clutter models. The fitting degree of the six distributions is evaluated by chi-square test and K-S test, and the optimal fitting model is determined. The analysis of the measured data shows that the model shape parameters extracted in the statistical process and the decorrelation time of the clutter sequence can effectively distinguish the pure clutter unit from the target unit. Second, multi-parameter joint binary detection, aiming at the limitation of single-parameter detection, according to the idea of M / N detector, a multi-parameter joint binary detection algorithm is proposed in this paper, in which double threshold detection is specified. The first threshold adopts the unit average CFAR detection algorithm and selects the appropriate detector according to the clutter modeling results. The second threshold is detected by renumbering the distance unit through the first threshold. The multiparameter used in the second threshold includes the fractal parameter Hurst exponent recursive parameter statistical characteristic parameter and so on. Set a single threshold for each parameter, statistical test results, set the binary detection threshold to make the final decision. Using IPIX radar data to verify the validity of the parameters used, limited by the number of IPIX radar data range units, CFAR detection is easy to cause a large CFAR loss. Therefore, the X-band data collected by the unit radar system in the field experiments are used to verify the multi-parameter combined binary algorithm, and the effectiveness of the method for small target detection on the sea surface is verified. Third, based on dynamic programming, the detection of weak targets is realized by using the feature that the correlation between multi-frame data is stronger than that of noise background, and the energy of target is larger than the noise background when the data is accumulated. The detection results are determined according to the index function, and the track is backtracked by the detection results at the same time. The effectiveness of the method is verified by simulation and semi-test simulation.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：E925;TN957.52
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本文编号：2002641