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基于基于空间ADS-B的自我注意神经网络的重叠信号分离算法
Ziwei Liu1, Shuyi Tang1, Yehua Cao1
1School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
新的深度学习模型SplitNet-2和SplitNet-3有效地解决了基于太空的自动依赖监控广播 (ADS-B) 系统中的信号碰撞. 这些先进的模型通过增强信号分离,提高了飞机监视可靠性和全球覆盖率.
科学领域:
- 航空航天工程 航空航天工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 基于太空的自动依赖监视广播 (ADS-B) 系统有望对全球飞机进行监视.
- 来自多个异步传输的严重信号碰撞在卫星接收中构成了重大挑战.
- 现有的碰撞减轻技术是计算密集的或需要特定的硬件,限制了卫星的适用性.
研究的目的:
- 开发新的深度学习模型,以减轻基于太空的ADS-B系统中的信号碰撞.
- 在卫星场景中克服传统信号分离方法的局限性.
- 提高基于卫星的ADS-B监控的可靠性和覆盖范围.
主要方法:
- 拟议的SplitNet-2:一种以变压器为灵感的自我注意模型,用于分离两个重叠的ADS-B信号.
- 拟议的SplitNet-3:一个卷积的残余U形网络,用于分离三个同时发送的ADS-B信号.
- 在现实的卫星接收条件下进行了广泛的模拟.
主要成果:
- 在信号分离方面,SplitNet-2和SplitNet-3显著优于传统方法.
- 与现有技术相比,实现了较低的比特错误率 (BER).
- 对于碰撞的ADS-B信号,证明了改进的解调精度.
结论:
- 拟议的深度学习模型为基于太空的ADS-B接收中未确定信号分离提供了实际解决方案.
- 分网-2和分网-3提高了基于卫星的ADS-B监控的可靠性,并扩大了其覆盖范围.
- 这些进步为更强大的全球飞机监控系统铺平了道路.
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