一个新的框架用于重建和成像的目标散射中心通过雷达网络的广角事件在雷达网络
Ge Zhang1, Weimin Shi2, Qilong Miao1
1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了利用雷达传感器网络重建目标散射中心的新框架. 该方法通过自适应细分模型和整合回声信息来提高目标识别,以获得准确的估计.
科学领域:
- 雷达信号处理是指处理雷达信号的过程.
- 目标识别和识别目标识别和识别
- 传感器网络是一个传感器网络.
背景情况:
- 精确重建目标散射中心 (TSC) 对于识别不合作的目标至关重要.
- 雷达传感器网络 (RSN) 提供多角度照明,但由于目标的复杂性和角度多样性而面临挑战.
- 对于移动平台的RSN,精确的时空注册和连贯处理的不可行性需要智能算法.
研究的目的:
- 为RSN提出一个新的协作TSC重建框架.
- 解决RSN中角度多样性和非连贯处理的挑战.
- 提高非合作目标的TSC估计的准确性和稳定性.
主要方法:
- 开发了一个使用对广角高分辨率范围配置文件 (HRRPs) 的相似性评估框架,用于自适应角度细分.
- 整合了预期最大化 (EM) 算法与增强的北极松鼠优化 (EAPO) 算法.
- 使用非连贯的方法来整合来自RSN的回声信息用于TSC估计.
主要成果:
- 拟议的框架实现了TSC模型的自适应角度细分.
- 组合EM和EAPO算法有效地以非连贯的方式集成回声信息.
- 在估计准确性和稳定性方面,与现有方法相比,表现优越.
结论:
- 这种新型框架可以从RSN进行准确的TSC估计,克服传统方法的局限性.
- 实验结果验证了框架的稳定性和适应复杂目标散射的适应性.
- 拟议的方法为特征提取和识别不合作目标提供了实际价值.
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