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

TANG Fengjian; YAN Xia; SUN Zeyi; ZHU Zhaowei; YANG Wen

doi:10.11999/JEIT250846

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TANG Fengjian, YAN Xia, SUN Zeyi, ZHU Zhaowei, YANG Wen. Security Protection for Vessel Positioning in Smart Waterway Systems Based on Extended Kalman Filter–Based Dynamic Encoding[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250846

Citation:

TANG Fengjian, YAN Xia, SUN Zeyi, ZHU Zhaowei, YANG Wen. Security Protection for Vessel Positioning in Smart Waterway Systems Based on Extended Kalman Filter–Based Dynamic Encoding[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250846

Citation:

TANG Fengjian, YAN Xia, SUN Zeyi, ZHU Zhaowei, YANG Wen. Security Protection for Vessel Positioning in Smart Waterway Systems Based on Extended Kalman Filter–Based Dynamic Encoding[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250846

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

doi: 10.11999/JEIT250846 cstr: 32379.14.JEIT250846

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)

Accepted Date: 2025-11-17
Rev Recd Date: 2025-11-17

Available Online: 2025-11-26

Abstract

Abstract

Objective With the rapid development of intelligent shipping systems, vessel positioning data face severe privacy leakage risks during wireless transmission. Traditional privacy-preserving methods, such as differential privacy and homomorphic encryption, suffer from data distortion, high computational overhead, or reliance on costly communication links, making it difficult to achieve both data integrity and efficient protection. This study addresses the characteristics of vessel stabilization systems and proposes a dynamic encoding scheme enhanced by time-varying perturbations. By integrating the Extended Kalman Filter (EKF) and introducing unstable temporal perturbations during encoding, the scheme uses receiver-side acknowledgments (ACK feedback) to achieve reference-time synchronization and independently generates synchronized perturbations through a shared random seed. Theoretical analysis and simulations show that the proposed method achieves nearly zero precision loss in state estimation for legitimate receivers, whereas decoding errors of eavesdroppers 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 reduces communication and computational costs, making the scheme suitable for resource-constrained maritime wireless sensor networks. Methods The proposed dynamic encoding scheme introduces a time-varying perturbation term into the encoding process. The perturbation is governed by an unstable matrix to induce exponential error growth for eavesdroppers. The encoded signal is constructed from the difference between the current state estimate and a time-scaled reference state, combined with the perturbation term. A shared random seed between legitimate parties enables deterministic and synchronized generation of the perturbation sequence without online key exchange. At the legitimate receiver, the perturbation is canceled during decoding, enabling accurate state recovery. Local state estimation at each sensor node is performed using EKF, and the overall communication process is reinforced by acknowledgment-based synchronization to maintain consistency between the sender and receiver. Results and Discussions Simulations are conducted in a wireless sensor network with four sensors tracking vessel states, including position, velocity, and heading. The results indicate that legitimate receivers achieve nearly zero estimation error (Fig. 3), whereas eavesdroppers exhibit exponentially increasing errors after a single packet loss (Fig. 4). The error growth rate depends on the instability of the perturbation matrix, confirming the theoretical divergence. In multi-channel scenarios, independent perturbation sequences for each 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 shows strong robustness to packet loss and channel variations, satisfying SOLAS requirements for data integrity and reliability. Conclusions A dynamic encoding scheme with time-varying perturbations is proposed for privacy-preserving vessel state estimation. By integrating EKF with an unstable perturbation mechanism, the method ensures high estimation precision for legitimate users and exponential error growth for eavesdroppers. The main contributions are as follows: (1) an encoding framework that achieves zero precision loss for legitimate receivers; (2) a lightweight synchronization mechanism based on shared random seeds, which removes complex key management; and (3) theoretical guarantees of exponential error divergence for eavesdroppers under single- or multi-channel attacks. The scheme is robust to 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 validation in real maritime communication environments.
- Vessel positioning,
- Privacy protection,
- Extended Kalman Filter (EKF),
- Dynamic encoding,
- Distributed estimation

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References(26)

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