评估安卓网络定位作为强大的PNT替代源
Joohan Chun1, Jacob Spagnolli1, Tanner Holmes1
1Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO 80309, USA.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
安卓设备上的网络位置提供商 (NLP) 在GNSS伪造期间保持准确的定位,并使用缓存数据在线运行. 这揭示了NLP对于强大的定位,导航和定时 (PNT) 解决方案的内部机制.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 地理学是地质学.
背景情况:
- 智能手机定位依赖于各种方法,网络定位提供商 (NLP) 使用Wi-Fi和蜂塔.
- NLP提供了强大的定位,导航和定时 (PNT) 解决方案的潜力,特别是在室内或在全球导航卫星系统 (GNSS) 射频干扰 (RFI) 期间.
- 在很大程度上,NLP的内部运作是未知的,它就像一个"黑子"系统.
研究的目的:
- 在不利的条件下调查Android的网络定位提供商 (NLP) 的操作特性.
- 探索NLP对GNSS伪造和离线网络场景的反应.
- 揭示NLP的内部机制,以改善PNT解决方案.
主要方法:
- 在三星S24和小米Redmi K80 Ultra设备上测试NLP性能.
- 模拟GNSS伪造以观察NLP的位置精度.
- 在没有网络连接的离线操作期间评估NLP功能.
- 试图模仿NLP本身. 试图模仿NLP本身. 试图模仿NLP本身.
主要成果:
- 即使在GNSS信号被伪造时,NLP也在真实位置保持了准确的定位,证实了它对RFI的稳定性.
- NLP继续在离线模式下运行,利用内部缓存的数据而不需要实时服务器连接.
- 观察到的离线功能与NLP依赖外部服务器的传统理解有所不同.
结论:
- 安卓的NLP在GNSS伪造和RFI方面表现出显著的稳定性.
- NLP拥有本地数据处理能力,通过缓存信息实现线下定位.
- 这些发现为NLP的内部运作提供了关键的见解,有助于开发更可靠的智能手机PNT系统.
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