在复杂环境中使用双输出神经网络进行基于信号属性信息的目标检测
Lu Shen1, Hongtao Su1, Zhi Mao1
1National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
一种新的深度神经网络探测器,即单输入双输出网络探测器 (SIDOND),可以改善复杂环境中的雷达目标检测. 它使用动态值来实现比传统方法更好的性能.
科学领域:
- 雷达信号处理 雷达信号处理
- 深度学习应用程序
- 目标检测算法 目标检测算法
背景情况:
- 传统的恒定误报比率 (CFAR) 算法因背景噪声估计不准确而难以处理复杂的环境,多个目标 (MT) 和杂乱边缘 (CE).
- 由于固定的值机制,单输入单输出神经网络在动态场景中表现下降.
研究的目的:
- 引入一种新的单输入双输出网络探测器 (SIDOND),克服传统和单输出网络探测器的局限性.
- 通过使用数据驱动深度神经网络 (DNN) 在具有挑战性的雷达场景中提高目标检测的稳定性和准确性.
主要方法:
- 开发了一种使用深度神经网络 (DNN) 的单输入双输出网络探测器 (SIDOND).
- 实现了一个输出来基于信号属性信息 (SPI) 估计检测足够的统计数据.
- 利用第二个输出来建立基于值影响因子 (TIF) 的动态智能值机制.
主要成果:
- 与传统的基于模型和单个输出网络探测器相比,拟议的SIDOND显示出更高的稳定性和性能.
- 实验验证证了SIDOND在具有多个目标和杂乱边缘的复杂环境中的有效性.
- 值影响因子 (TIF) 有效地简化了动态值的目标和背景环境信息.
结论:
- 在雷达目标检测方面,SIDOND提供了显著的进步,特别是在复杂和动态的环境中.
- 双输出网络架构和动态值提供了更具适应性和精确的检测解决方案.
- 使用视觉解释技术来阐明SIDOND的操作原则.
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