Development of a plastic landmine detection system based on CMRF model

本研究的目的是基于最新提出的复值神经网络理论，研制一种能自适应地处理空间和频域干涉纹理的杀伤人员塑料地雷可视化雷达系统。该系统由三部分组成：(1)密集集成宽带天线手机；(2)基于复值自组织映射的图像聚类处理模块；(3)基于复值联想记忆的地雷类识别器。(L)密集集成宽带天线手机：我们研制了一种新的天线单元，即Walled-LTSA，它具有更小的口径和更宽的带宽。基于复值自组织映射的图像聚类处理模块：我们还开发了一个自适应纹理分类器…更多是基于CSOM。反射波在空间域(二维)和频率域(一维)具有复振幅信息(共三维)。CSOM通过自适应地考虑复幅纹理来分割三维图像。该模块成功地将塑料雷区与其他土壤区域区分开来，包括石块和金属碎片。(3)基于复值联想记忆的地雷类别识别器：我们必须在CSOM模块的分割区域中识别出塑料地雷类别。该识别模块处理两个阶段的特征向量。首先，该模块在记忆的教师数据中选择与测试观察中的特征向量集最接近的特征向量集。在实验室实验中，我们发现塑料地雷的可视化性能与金属地雷探测器的可视化性能接近。然而，我们也发现，在观测中，性能取决于土壤、湿度等环境条件。因此，下一步，我们将在实验室外进行实地测试，而不是在与雷场几乎相同的场地上进行试验。在我们的计划中，我们将在下一学年开始实地测试。较少

This study was carried out with the purpose of developing an antipersonnel plastic landmine visualization radar system that deals with spatial-and frequency-domain interferometric texture adaptively in a brainlike manner based on the newly proposed theory, i.e., complex-valued neural networks. The developed system consists of three parts : (1)densely integrated wideband antenna handset, (2)iAmage clustering processing module employing complex-valued self-organizing map (CSOM), and (3)landmine-class identifier based on complex-valued associative memory.(l)Densely integrated wideband antenna handset :We have developed a new antenna element, i.e., walled-LTSA, which has a smaller aperture and a wide bandwidth. This antenna array enables us to capture subsurface reflection two-dimensionally with a high spatial resolution in a phase-sensitive manner.(2)Image clustering processing module employing complex-valued self-organizing map(CSOM) :We have also developed an adaptive texture classifier … More based on the CSOM. The reflection has complex-amplitude information in space (two-dimension) and frequency (one-dimension) domains (three-dimension in total). The CSOM segment the three-dimensional image by taking into account the complex-amplitude texture in an adaptive manner. The module successfully distinguishes a plastic landmine area from other soil areas including, e.g., stones and metal fractions.(3)Landmine-class identifier based on complex-valued associative memory :We have to identify plastic landmine class(es) among the segmented areas in the CSOM module. This identifying module deals with the feature vectors on double stages. First, the module chooses a feature-vector sets, in the memorized teacher data, nearest to those in the test observation. Then, it determines the correspondence of respective feature vectors to those in the teacher data.In the developed system, we found that in the laboratory experiment the visualization performance for plastic landmines is near to that of the metal detectors for metal landmines. However, we also find that the performance is dependent on the conditions of soil, humidity, and other environment in the observation. Therefore, in the next step, we are going to have a field test out of the laboratory, but instead, on the field almost identical to that of landmine field next. In our plan, we start the field test in the following academic year. Less