• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Self-organizing clustering neural networks: Comparative study and data fusion applications.
【24h】

Self-organizing clustering neural networks: Comparative study and data fusion applications.

机译：自组织聚类神经网络：比较研究和数据融合应用。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The self-organizing clustering neural network, DIGNET, is compared with ART2 and self-organizing feature maps (SOFM). DIGNET is used for designing a multi-sensor data fusion to achieve multi-radar moving target detection.; SOFM is not applicable in situations where the number of clusters is determined by the system in an autonomous way. Comparative experiments are used to investigate the performance of DIGNET and ART2 on statistical data clustering and detection problems. DIGNET exhibits faster learning and better clustering. With simpler dynamics, DIGNET is more flexible in choosing different metrics as measures of similarity. System parameters in DIGNET are determined from the self-adjusting process. Threshold value in DIGNET can be determined from a lower bound of the desirable signal-to-noise ratio. A simplified ART2 model (SART2), which adopts the structural concepts from DIGNET, exhibits faster learning than ART2 and does not suffer from a "false conviction" syndrome which seems to exist in the "fast learning" ART2.; A two-stage parallel multi-sensor data fusion with DIGNET is applied to a moving target indication (MTI) system. The MTI system consists of three radars with different carrier frequencies. Features of the received data are extracted via digital signal processing. Pulse compression, clutter cancelling, and fast Fourier transform are used to transform data from time-range domain to range-Doppler domain. Map regularization, circular metric, and contrast enhancement are used to resolve the feature misalignment problems. DIGNET performs feature extraction and filtering, and eliminate spurious feature patterns. DIGNET with different metrics, inner-product and hypercube, are used in two approaches. The clustering results from DIGNET on each channel are passed to the fusion DIGNET for a second stage clustering. The well depths in DIGNET act as the indicators of confidence levels. In one approach, well depths are used in the weighted averaging for data combination. In the other approach, well depths are used to determine whether the pattern associated with a winning cluster should be passed to the following processing stage. Experiments show that data fusion with DIGNET successfully detect the moving target embedded in clutter.
机译：将自组织聚类神经网络DIGNET与ART2和自组织特征图（SOFM）进行比较。 DIGNET用于设计多传感器数据融合以实现多雷达运动目标检测。 SOFM在系统以自主方式确定群集数量的情况下不适用。对比实验用于研究DIGNET和ART2在统计数据聚类和检测问题上的性能。 DIGNET展示了更快的学习速度和更好的群集。动态更简单，DIGNET可以更灵活地选择不同的度量作为相似性度量。 DIGNET中的系统参数由自调整过程确定。 DIGNET中的阈值可以从所需信噪比的下限确定。简化的ART2模型（SART2），采用了DIGNET的结构概念，显示出比ART2更快的学习速度，并且没有“快速学习” ART2中似乎存在的“虚假信念”综合症。将带有DIGNET的两阶段并行多传感器数据融合应用于移动目标指示（MTI）系统。 MTI系统由三个具有不同载波频率的雷达组成。接收数据的特征是通过数字信号处理提取的。脉冲压缩，杂波消除和快速傅立叶变换可用于将数据从时域转换到距离多普勒域。地图正则化，圆形度量和对比度增强用于解决特征未对准问题。 DIGNET执行特征提取和过滤，并消除虚假特征模式。两种方法使用具有不同指标（内部产品和超立方体）的DIGNET。每个通道上来自DIGNET的聚类结果将传递给融合DIGNET，以进行第二阶段聚类。 DIGNET中的井深充当置信度指标。在一种方法中，在数据组合的加权平均中使用井深。在另一种方法中，井深用于确定与获胜群集关联的模式是否应传递到下一个处理阶段。实验表明，与DIGNET进行数据融合可以成功地检测出嵌入杂波中的运动目标。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号