首页> 外文期刊>Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic >Low-observable target detection in sea clutter based on the adaptive 3D-IFS algorithm
【24h】

Low-observable target detection in sea clutter based on the adaptive 3D-IFS algorithm

机译:基于自适应3D-IFS算法的海杂波低观测目标检测

获取原文
获取原文并翻译 | 示例
           

摘要

This paper mainly studies the application of the fractal self-affine theory for weak target detection in sea clutter. In this paper, the adaptive 3D-IFS (3D-IFS: three-dimensional iterated function system) algorithm is presented and a novel weak target detection model is proposed based on the algorithm. To accurately extract the weak target from the complicated background of sea clutter, we use the radar echo model of the LFM radar and the target detection model to calculate the prediction error of radar echoes. Furthermore, based on extensive analysis and simulation, we identify the scale factor, polarization model and target state as three key factors that affect the detection performance of our proposed model. Using the real data from IPIX radar and C-band radar for simulations, we can see that the model has a significant performance improvement for weak target detection compared to the traditional 3D-IFS algorithm. The detection probability of the model reaches 70% at the SCR near -10 dB, and signal processing time of the model is approximately 0.3 s at the SCR near -6 dB, thus it meets the requirement for low-observable (SCR> -8 dB) and radial velocity about 600 m/s high speed weak target detection in sea clutter. (C) 2015 Elsevier GmbH. All rights reserved.
机译:本文主要研究分形自仿射理论在海杂波弱目标检测中的应用。本文提出了一种自适应3D-IFS(3D-IFS:三维迭代函数系统)算法,并提出了一种基于该算法的新型弱目标检测模型。为了从复杂的海浪背景中准确地提取出弱目标,我们使用了LFM雷达的雷达回波模型和目标检测模型来计算雷达回波的预测误差。此外,在广泛的分析和仿真的基础上,我们将比例因子,极化模型和目标状态确定为影响所提出模型检测性能的三个关键因素。使用来自IPIX雷达和C波段雷达的真实数据进行仿真,我们可以看到,与传统3D-IFS算法相比,该模型在弱目标检测方面具有显着的性能提升。在-10 dB附近的SCR处模型的检测概率达到70%,在-6 dB附近的SCR处模型的信号处理时间约为0.3 s,因此满足低可观察性(SCR> -8)的要求分贝)和径向速度约600 m / s的海杂波中的高速弱目标检测。 (C)2015 Elsevier GmbH。版权所有。

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