使用量子卷积自编码器进行最佳卫星选择,用于低成本的GNSS接收器应用
Nalineekumari Arasavali1, Mogadala Vinod Kumar2, Sasibhushana Rao Gottapu3
1Department of Electronics & Communication Engineering, Dadi Institute of Engineering & Technology(A), Visakhapatnam, Andhra Pradesh, India.
Scientific reports
|March 14, 2025
概括
本研究介绍了一种量子卷积自编码器,用于在低成本全球导航卫星系统 (GNSS) 接收器中进行最佳卫星选择. 与传统方法相比,该方法显著提高了定位精度,并减少了计算负载.
科学领域:
- 卫星导航系统 卫星导航系统
- 量子计算应用程序 量子计算应用程序
- 机器学习在优化中的应用.
背景情况:
- 全球导航卫星系统 (GNSS) 是必不可少的,但由于高精度的几何稀释 (GDOP),低成本的接收器难以达到最佳性能.
- 有效的卫星选择对于提高GNSS应用中的定位精度和可靠性至关重要.
研究的目的:
- 使用量子计算和机器学习开发低成本GNSS接收器的最佳卫星选择方法.
- 为了最大限度地减少精度的几何稀释 (GDOP),并优化四面体体积函数,以提高定位精度.
主要方法:
- 提出了一种基于量子卷积自编码器 (QCAE) 的最佳卫星选择方法.
- 利用了2022年3月10日从北16.33°和东经80.62°的接收器收集的卫星数据.
- 将精度的几何稀释 (GDOP) 设置为成本函数,以确定最佳的定位卫星.
主要成果:
- 在四颗卫星中,QCAE方法实现了1.384米的圆形误差概率 (CEP) 和1.759米的球形误差概率 (SEP),超过了PSOSSM (5.937米CEP,6.691米SEP).
- 对于9颗卫星,QCAE的CEP为1.287米,SEP为1.713米,而PSOSSM的CEP为5.725米和SEP为6.385米.
- 与使用所有可见卫星相比,QCAE的计算减少了超过64% (730次乘法,713次加法) (2034次乘法,2017年加法).
结论:
- 基于QCAE的方法为经济高效的实时GNSS实现提供了最佳的导航解决方案.
- 这项研究为利用先进的机器学习技术对卫星选择策略提供了新的见解.
- 该方法通过提高准确性和计算效率来提高低成本GNSS接收器的性能.
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