基于扩展卡尔曼动态编码的智慧航道系统船舶定位安全保护

唐风建; 闫霞; 孙泽仪; 朱钊伟; 杨文

doi:10.11999/JEIT250846

基于扩展卡尔曼动态编码的智慧航道系统船舶定位安全保护

doi: 10.11999/JEIT250846 cstr: 32379.14.JEIT250846

1.
长江上海航道处上海 200011
2.
华东理工大学信息科学与工程学院上海 200237
3.
江苏国信靖江发电有限公司江苏 214500

基金项目: 国家重点研发计划(2023YFF1204805)，国家自然科学基金重点项目(62336005)

详细信息

作者简介:
唐风建：男，高级工程师，研究方向为航道与港口工程

闫霞：女，工程师，研究方向为数字孪生船舶

孙泽仪：女，研究生，研究方向为信息融合与网络信息安全

朱钊伟：男，工程师，研究方向为智能航运系统数据安全与隐私保护

杨文：女，教授，研究方向为工业互联网信息安全与多源信息融合

通讯作者:
杨文　weny@ecust.edu.cn

中图分类号: TP309.7
计量
- 文章访问数: 18
- HTML全文浏览量: 11
- PDF下载量: 2
- 被引次数: 0
出版历程
- 修回日期: 2025-11-17
- 录用日期: 2025-11-17
- 网络出版日期: 2025-11-26

Security Protection for Vessel Positioning in Smart Waterway Systems Based on Extended Kalman Dynamic Encoding

1.
Shanghai Channel Department of Changjiang Waterway Bureau, Shanghai 200011, China
2.
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
3.
Jiangsu Guoxin Jingjiang Power Generation Co., Ltd., Jingjiang 214500, China

Funds: National Key Research and Development Program of China (2023YFF1204805),Key Program of the National Natural Science Foundation of China (62336005)

摘要

摘要: 随着智能航运系统的快速发展，船舶定位数据在无线传输过程中面临严重的隐私泄露风险。传统隐私保护方法如差分隐私和同态加密存在数据失真、计算开销大或依赖高成本通信链路等问题，难以在保证数据完整性的同时实现高效防护。本文针对船舶稳定系统的特点，提出一种基于时间扰动增强的动态编码方案。该方案结合扩展卡尔曼滤波(EKF)，在编码过程中引入不稳定的时间扰动项，利用接收方对发送方发出的信息进行确认这一机制(ACK反馈)实现参考时间同步，并利用共享随机种子独立生成同步的扰动项。理论分析与仿真实验表明，该方案能够在合法接收方实现近乎零精度损失的状态估计的同时，使窃听者在单次丢包后解码误差随时间呈指数增长趋势，有效阻断单通道与多通道窃听攻击。方案采用共享随机种子同步机制，避免了复杂的密钥管理，显著降低了通信与计算开销，适用于资源受限的海上无线传感器网络环境，为船舶安全定位提供了有效保障。
- 船舶定位 /
- 隐私保护 /
- 扩展卡尔曼滤波 /
- 动态编码 /
- 分布式估计
Abstract: Objective With the rapid development of intelligent shipping systems, vessel positioning data faces severe privacy leakage risks during wireless transmission. Traditional privacy-preserving methods such as differential privacy and homomorphic encryption suffer from issues like data distortion, high computational overhead, or reliance on costly communication links, making it difficult to achieve both data integrity and efficient protection. This paper addresses the characteristics of vessel stabilization systems and proposes a dynamic encoding scheme enhanced by time-varying perturbations. By integrating Extended Kalman Filter (EKF) and introducing unstable temporal perturbations during encoding, the scheme utilizes receiver-side acknowledgments (ACK feedback) to achieve reference time synchronization and independently generates synchronized perturbations via a shared random seed. Theoretical analysis and simulations demonstrate that the proposed method enables nearly zero precision loss in state estimation for legitimate receivers while causing eavesdroppers’ decoding errors to grow exponentially after a single packet loss, effectively countering both single and multi-channel eavesdropping attacks. The shared seed synchronization mechanism avoids complex key management and significantly reduces communication and computational costs, making it suitable for resource-constrained maritime wireless sensor networks. Methods The proposed dynamic encoding scheme incorporates a time-varying perturbation term into the encoding process. The perturbation is governed by an unstable matrix to ensure exponential error growth for eavesdroppers. The encoding mechanism uses the difference between the current state estimate and a time-scaled reference state, plus the perturbation term. A shared random seed between legitimate parties enables deterministic and synchronized generation of the perturbation sequence without online key exchange. The decoding process at the legitimate receiver cancels out the perturbation, enabling accurate state recovery. The system employs Extended Kalman Filtering for local state estimation at each sensor node, and the entire communication process is reinforced by acknowledgment-based synchronization to maintain consistency between sender and receiver. Results and Discussions Simulations were conducted in a wireless sensor network with four sensors tracking vessel states such as position, velocity, and heading. The results show that legitimate receivers achieve nearly zero estimation error (Fig.3), while eavesdroppers experience exponentially growing errors after a single packet loss (Fig.4). The error growth rate correlates with the instability of the perturbation matrix, confirming the theoretical divergence. In multi-channel scenarios, independent perturbation sequences per channel prevent cross-channel correlation attacks (Fig.5). The scheme maintains low communication and computational overhead, making it practical for maritime environments. Furthermore, the method demonstrates strong adaptability to packet loss and channel variations, fulfilling SOLAS requirements for data integrity and reliability. Conclusions This paper proposes a dynamic encoding scheme with time-varying perturbations for privacy-preserving vessel state estimation. The method integrates EKF with an unstable perturbation mechanism to ensure high precision for legitimate users and exponential error growth for eavesdroppers. Key contributions include: (1) A novel encoding framework that ensures zero precision loss for legitimate receivers; (2) A lightweight synchronization mechanism based on shared seeds, eliminating complex key management; (3) Theoretical guarantees of exponential error divergence for eavesdroppers under single or multi-channel attacks. The scheme is robust against packet loss and channel asynchrony, complies with SOLAS data integrity requirements, and is suitable for resource-limited maritime networks. Future work will extend the method to nonlinear vessel dynamics, adaptive perturbation optimization, and real-world maritime communication validation.
- Vessel positioning /
- Privacy protection /
- Extended Kalman Filter (EKF) /
- Dynamic encoding /
- Distributed estimation

HTML全文

图 1 ：系统框架示意图

下载: 全尺寸图片幻灯片

图 3 估计误差范数

下载: 全尺寸图片幻灯片

图 2 船舶位置、速度和航向角的估计误差

下载: 全尺寸图片幻灯片

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