Trust Adaptive Event-triggered Robust Extended Kalman Fusion Filtering for Target Tracking

ZHU Hongbo; JIN Jiahui

doi:10.11999/JEIT250103

Volume 47 Issue 8

Aug. 2025

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ZHU Hongbo, JIN Jiahui. Trust Adaptive Event-triggered Robust Extended Kalman Fusion Filtering for Target Tracking[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2694-2702. doi: 10.11999/JEIT250103

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ZHU Hongbo, JIN Jiahui. Trust Adaptive Event-triggered Robust Extended Kalman Fusion Filtering for Target Tracking[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2694-2702. doi: 10.11999/JEIT250103

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Trust Adaptive Event-triggered Robust Extended Kalman Fusion Filtering for Target Tracking

doi: 10.11999/JEIT250103 cstr: 32379.14.JEIT250103

ZHU Hongbo,
JIN Jiahui^,

School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

Funds: The Natural Science Research Project of Anhui Educational Committee (2023AH040157), The National Natural Science Foundation of China (62003001)

Received Date: 2025-02-21
Rev Recd Date: 2025-05-25

Available Online: 2025-06-07

Publish Date: 2025-08-27

Abstract

Abstract

Objective Mobile Wireless Sensor Networks (MWSNs) with dynamic topology exhibit considerable application value across various fields, making target tracking a critical area of research. Although conventional filtering algorithms and event-triggered schemes have enabled basic target tracking, they remain limited in addressing motion modeling errors, Received Signal Strength (RSS) quantization inaccuracies, and adaptation to dynamic network conditions. To overcome these limitations, this study proposes a trust-adaptive event-triggered mechanism combined with an improved Extended Kalman Filter (EKF). The mechanism dynamically schedules a suitable number of trust anchor nodes based on network conditions, while the robust EKF estimates the motion state of the mobile target. This approach ensures stable, accurate, and consistent estimation even under time-varying process and measurement noise covariance. The proposed method offers an effective solution for RSS-based tracking in resource-constrained MWSNs by reducing power, computation, and bandwidth consumption, while improving tracking accuracy and maintaining robustness against measurement uncertainty and faulty nodes. Methods In the resource-constrained MWSN environment, a robust extended Kalman fusion filtering method with trust-adaptive event triggering is proposed for target tracking. This method incorporates a trust-adaptive, event-driven anchor node scheduling and information exchange mechanism. It dynamically adjusts to the spatial distribution of trusted anchor nodes near the target, schedules a number of anchor nodes close to the optimal value, and streamlines communication between these nodes and the mobile target. This design substantially reduces power, computational, and bandwidth demands while maintaining measurement credibility. To address uncertainties arising from motion modeling and RSS quantization, a robust extended Kalman trust fusion filtering algorithm based on mean drift is developed. The algorithm estimates the actual covariance by randomly sampling uniformly distributed process and measurement noise covariance matrices, thereby compensating for discrepancies between model predictions and observations. Additionally, only measurements from nodes identified as reliable are incorporated via adaptive weighted fusion, which enhances the stability, robustness, and accuracy of target tracking. Results and Discussions The proposed trust-adaptive event-triggered robust extended Kalman fusion filtering method substantially improves target tracking performance in resource-constrained MWSNs. By integrating a dynamic anchor node scheduling mechanism with a dual-layer noise compensation strategy, the method adjusts the response radius in real time through trust-adaptive event triggering. Therefore, the average number of trust response anchors remains stable at a preset target—for example, ANoTRA = 5.0583 when $ {{N}}_{\text{t}} $ = 5—while reducing communication resource consumption by 53.8% compared with the fixed threshold method (Fig. 2; Table 2). Furthermore, the use of uniformly distributed random sampling enables the algorithm to account for system uncertainty when the process noise covariance q is within [0.25, 1.5]. The introduction of a mean-shift algorithm helps to eliminate abnormal measurements, leading to a 42.6% reduction in tracking Root-Mean-Square Error (RMSE) compared with traditional approaches (Fig. 3, Fig. 4, Fig. 5). Under complex environmental conditions, with parameters set as q ∈ [0.25, 1.5], H = 10, L = 6, $ {{N}}_{\text{t}} $ = 5, and m = 8, the method demonstrates high accuracy and robustness. These results indicate that the proposed approach not only enhances tracking precision but also significantly improves the efficiency of resource utilization. Conclusions This study addresses the problem of mobile target tracking in resource-constrained MWSNs by integrating a trust-adaptive event-triggering mechanism with a robust extended Kalman fusion filtering algorithm. The proposed method leverages the advantages of trust-based adaptive triggering and robust filtering to achieve high tracking accuracy while reducing power, computational, and communication overhead. Simulation results demonstrate that (1) the robust EKF reduces the tracking root mean square error by 42.6% compared with the conventional EKF, and (2) the trust-adaptive event-triggering mechanism reduces communication resource consumption by 53.8% relative to static schemes such as non-trust-based adaptive triggering. This work focuses on tracking under low-noise conditions. Future research will extend the method to more complex nonlinear systems and explore the integration of statistical approaches and deep learning techniques for enhanced outlier identification and suppression under high interference.
- Target tracking,
- Mobile Wireless Sensor Network(MWSN),
- Event-triggered,
- Extended Kalman fusion filtering,
- Received Signal Strength(RSS)

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References(26)

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