• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters
【24h】

Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters

机译：使用随机训练的最小二乘和压缩二次相关滤波器快速检测压缩感测的红外目标

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

Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time.
机译：夜间潜在威胁的目标检测可以部署在高分辨率的昂贵红外焦平面阵列上。由于红外图像块的可压缩性，可以在保留目标检测功能的情况下降低高分辨率要求。因此，已经开发了具有低分辨率焦平面阵列的压缩中波红外成像仪（MWIR）。由于可以从训练数据中学到红外图像补丁支持集的最可能系数指标，因此我们开发了随机训练的最小二乘（STLS）用于MWIR图像重建。二次相关滤波器（QCF）已被证明对目标检测有效，并且有几种设计滤波器的方法。使用与STLS中相同的测量矩阵，我们构造了一个压缩二次相关滤波器（CQCF），该滤波器采用了用于压缩红外目标检测的滤波器设计。我们将CQCF应用于美国陆军夜视和电子传感器局数据集。数值模拟表明，我们的算法的识别性能与标准的完全重建方法相匹配，但是执行时间却很少。

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