...
首页> 外文期刊>International Journal of Distributed Sensor Networks >Detection of weak multi-target with adjacent frequency based on chaotic system
【24h】

Detection of weak multi-target with adjacent frequency based on chaotic system

机译:基于混沌系统的相邻频率弱多目标检测

获取原文
   

获取外文期刊封面封底 >>

       

摘要

As a promising technology in signal detection, the chaotic detection system can significantly improve the accuracy of weak signal detection in strong background noise. It benefits from its characteristics of the sensitivity to the initial condition and the immunity to the Additive White Gaussian Noise. However, the fundamental challenges of the existing chaotic detection system are the sensitivity to narrow-band noise and the influences of multi-target detection with adjacent frequency, which bring great difficulties in the real application. To address these problems, in this article, we focus on the weak multi-target detection with adjacent frequency under the narrow-band noise, and a novel chaotic detection system that integrates the detection algorithm based on period-chaos duration ratio is proposed. In order to enhance the robustness to narrow-band noise, the Melnikov method is used to analyze the Duffing difference system. To realize the detection of weak multi-target with adjacent frequency, we proposed the detection system using the rule named general critical state. Furthermore, simulation results corroborate that the proposed system based on period-chaos duration ratio can achieve satisfactory performance in terms of the weak multi-target detection under narrow-band noise, and it is well investigated by extensive simulation for testing its effectiveness.
机译:混沌检测系统作为信号检测中的一项有前途的技术,可以显着提高在强背景噪声下弱信号检测的准确性。它受益于其对初始条件的敏感性和对加性高斯白噪声的抗扰性。然而,现有混沌检测系统的根本挑战是对窄带噪声的敏感度以及相邻频率的多目标检测的影响,这给实际应用带来了很大的困难。为了解决这些问题,本文将重点放在窄带噪声下具有相邻频率的弱多目标检测,并提出了一种新的混沌检测系统,该系统集成了基于周期-混沌持续时间比的检测算法。为了增强对窄带噪声的鲁棒性,采用梅尔尼科夫方法分析了达芬差分系统。为了实现对具有相邻频率的弱多目标的检测,我们提出了一种基于通用临界状态的检测系统。此外,仿真结果证实了所提出的基于周期-混沌持续时间比率的系统在窄带噪声下的弱多目标检测方面可以达到令人满意的性能,并且通过广泛的仿真进行了充分的研究以测试其有效性。

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