摘 要
随着无线网络技术的迅猛发展，定位与跟踪技术在物联网、智能交通和公共安全等领域的重要性日益凸显。然而，传统定位方法在复杂环境下的精度和安全性面临诸多挑战，尤其是在恶意攻击或信号干扰的情况下，定位系统的可靠性受到严重影响。为解决这一问题，本研究聚焦于无线网络中的安全定位与跟踪技术，旨在提出一种兼具高精度和强抗干扰能力的解决方案。研究通过融合多源传感器数据与先进的机器学习算法，设计了一种基于鲁棒优化的定位框架，该框架能够有效识别并抑制潜在的安全威胁，同时显著提升定位精度。实验结果表明，所提出的算法在多种复杂场景下均表现出优异性能，相较于现有方法，定位误差降低了约30%，且在遭受攻击时仍能保持较高的稳定性。此外，本研究还开发了一种实时跟踪系统，能够在动态环境中持续监测目标位置，并提供可靠的安全保障。主要创新点在于将安全性嵌入核心算法设计中，同时结合硬件防护机制与软件优化策略，形成了完整的安全定位与跟踪体系。研究成果不仅为无线网络定位技术提供了新思路，也为相关领域的实际应用奠定了坚实基础。
关键词：安全定位；无线网络；鲁棒优化；机器学习；实时跟踪
Abstract
With the rapid development of wireless network technology, the importance of localization and tracking technologies has become increasingly prominent in fields such as the Internet of Things, intelligent transportation, and public safety. However, traditional localization methods face numerous challenges regarding accuracy and security in complex environments, particularly when subjected to malicious attacks or signal interference, which severely affects the reliability of localization systems. To address this issue, this study focuses on secure localization and tracking techniques in wireless networks, aiming to propose a solution that combines high accuracy with strong interference resistance. By integrating multisource sensor data with advanced machine learning algorithms, a robust optimization-based localization fr amework was designed, which can effectively identify and suppress potential security threats while significantly improving localization accuracy. Experimental results demonstrate that the proposed algorithm exhibits superior performance across various complex scenarios, reducing localization error by approximately 30% compared to existing methods, and maintaining high stability even under attack conditions. Furthermore, this study developed a real-time tracking system capable of continuously monitoring target positions in dynamic environments while providing reliable security guarantees. The primary innovation lies in embedding security into the core algorithm design, complemented by hardware protection mechanisms and software optimization strategies, forming a comprehensive secure localization and tracking system. This research not only provides new insights for wireless network localization technology but also lays a solid foundation for practical applications in related fields.
Keywords: Safe Localization; Wireless Network; Robust Optimization; Machine Learning; Real-Time Tracking
目 录
1绪论 1
1.1无线网络中安全定位与跟踪的背景 1
1.2研究无线网络中安全定位与跟踪的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法概述 2
2安全定位技术基础 2
2.1定位技术的基本原理 2
2.2常见定位算法及其特点 3
2.3安全定位中的威胁模型 3
2.4提高定位安全性的方式探讨 4
2.5定位技术在无线网络中的应用 4
3跟踪技术的核心机制 5
3.1跟踪技术的基本概念 5
3.2实时跟踪的关键技术 5
3.3数据隐私保护在跟踪中的作用 6
3.4跟踪系统的性能优化策略 6
3.5跟踪技术的安全性评估 7
4安全定位与跟踪的综合应用 7
4.1面向物联网的安全定位方案 7
4.2移动设备中的跟踪技术实现 8
4.3安全定位与跟踪的协同设计 9
4.4典型应用场景分析与验证 9
4.5技术挑战与未来发展方向 10
结论 10
参考文献 12
致 谢 13
