Volume 26 Issue 4
Apr.  2026
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2026  >  26(4): 90-107
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ZHANG Jian-ping, LUO Chuang, ZHANG Guang-yuan, WANG Zhi-yuan, CHEN Yun-xiang. Real-time track anomaly detection and prediction correction model for low-altitude traffic control platform[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 90-107. doi: 10.19818/j.cnki.1671-1637.2026.166
Citation: ZHANG Jian-ping, LUO Chuang, ZHANG Guang-yuan, WANG Zhi-yuan, CHEN Yun-xiang. Real-time track anomaly detection and prediction correction model for low-altitude traffic control platform[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 90-107. doi: 10.19818/j.cnki.1671-1637.2026.166
shu

Real-time track anomaly detection and prediction correction model for low-altitude traffic control platform

doi: 10.19818/j.cnki.1671-1637.2026.166
  • 1.

    School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

  • 2.

    Low-altitude Traffic Intelligent Control Key Laboratory of Sichuan Province, Chengdu 610031, Sichuan, China

Funds:

National Key R&D Program of China 2022YFB4300903

Key Program of National Natural Science Foundation of China Civil Aviation Joint Research Fund U2433217

National Natural Science Foundation of China 52472332

Sichuan Provincial Major Science and Technology Special Project 2024ZDZX0044

Sichuan Provincial Natural Science Foundation of China 2025ZNSFSCO394

More Information
  • Corresponding author: ZHANG Guang-yuan, senior engineer, PhD, E-mail: gyzhang@swjtu.edu.cn
  • Received Date: 2025-08-25
  • Accepted Date: 2026-01-23
  • Rev Recd Date: 2025-12-09
  • Publish Date: 2026-04-28
    Abstract
  • An end-to-end integrated model of track anomaly detection and correction for low-altitude traffic control platforms was developed. Four types of behavioral components, such as, statistical outliers, physical envelopes, morphological patterns, and sequence residuals, were fused to form a unified anomaly score, and anomaly identification was achieved by combining a feature fusion module, adaptive thresholding, and weight optimization. A unidirectional two-layer long short-term memory prediction network combined with an attention mechanism was employed as the temporal backbone, and a differentiable physical integrator, adaptive noise estimation, and Kalman update were incorporated to obtain predicted reconstruction sequences. A back-propagatable loss function was designed, and consistency distillation was adopted to align the outputs of different branches, ultimately forming an end-to-end physics-aware Kalman long short-term memory network model. The results show that in the anomaly detection task, compared with deep baseline models, the harmonic mean score (F1), area under the average precision-recall curve (AUPRC), and area under the ROC curve (AUROC) of the developed model under a fixed threshold increase by 5.95%, 4.16%, and 2.38%, respectively. In the prediction correction task, compared with a filtering-only method and a prediction-only model, the root mean square error (RMSE) decreases by 15.2% and 21.7%, respectively. In terms of real-time deployment, when the optimal window size is 32, the computational latency decreases by 76.9% compared with the window size of 64 corresponding to the best F1 value, while the F1 value decreases by only 0.57%. The model can balance the anomaly detection reliability and real-time trajectory correction ability under millisecond-level latency constraints and can provide effective methodological support for the development of real-time trajectory anomaly detection and prediction correction functions of low-altitude traffic control platforms.

     

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    • References(22)
  • [1]
    TAO Ye, TANG Jin-hui, YAN Zhen, et al. A trajectory imputation method integrating representation transformation and pattern regression[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(1): 272-293.
    [2]
    DONG X, TIAN Y, DAI L, et al. A new accurate aircraft trajectory prediction in terminal airspace based on spatio-temporal attention mechanism[J]. Aerospace, 2024, 11(9): 718. doi: 10.3390/aerospace11090718
    [3]
    ZHANG Ding-kai, WANG Yi-zhe, WANG Peng-fei, et al. Expressway traffic anomaly event detection model based on dual spatio-temporal masks and bidirectional Mamba state space modeling[J]. Journal of Traffic and Transportation Engineering, 2025, 25(4): 311-327. doi: 10.19818/j.cnki.1671-1637.2025.04.022
    [4]
    PANG G S, SHEN C H, JIN H D, et al. Deep weakly-supervised anomaly detection[C]//ACM. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2023: 1795-1807.
    [5]
    CHEN B, REN Z, CHENG L. Conformalized time series with semantic features[J]. Advances in Neural Information Processing Systems, 2024, 37: 121449-121474.
    [6]
    LEE J, XU C, XIE Y. Kernel-based optimally weighted conformal time-series prediction[C]//ICLR. The Thirteenth International Conference on Learning Representations. Singapore: ICLR, 2025: 1-31.
    [7]
    ZENG G X, CHEN S X, WU H, et al. Continuous-discrete extended Kalman filtering based on the neural ordinary differential equations method[J]. Engineering Applications of Artificial Intelligence, 2024, 133: 108611. doi: 10.1016/j.engappai.2024.108611
    [8]
    NI X, REVACH G, SHLEZINGER N. Adaptive KalmanNet: Data-driven Kalman filter with fast adaptation[C]//IEEE. ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2024: 5970-5974.
    [9]
    SHEN S Y, CHEN J C, YU G F, et al. KalmanFormer: Using transformer to model the Kalman Gain in Kalman Filters[J]. Frontiers in Neurorobotics, 2025, 18: 1460255. doi: 10.3389/fnbot.2024.1460255
    [10]
    CHEN X J, LI Y P. An overview of differentiable particle filters for data-adaptive sequential Bayesian inference[EB/OL]. (2023-12-14). https://arxiv.org/abs/2302.09639
    [11]
    WAN Z Y, ZHAO L. DnD filter: Differentiable state estimation for dynamic systems using diffusion models[C]//IEEE. 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York: IEEE, 2025: 13851-13858.
    [12]
    REIS M J C S, REIS A J D. Edge-based real-time fault detection in UAV systems via B-spline telemetry reconstruction and lightweight hybrid AI[J]. Sensors, 2025, 25(16): 4944. doi: 10.3390/s25164944
    [13]
    AHMAD R, ALKHAMMASH E H. Online adaptive Kalman filtering for real-time anomaly detection in wireless sensor networks[J]. Sensors, 2024, 24(15): 5046. doi: 10.3390/s24155046
    [14]
    POL A A, GOVORKOVA E, GRONROOS S, et al. Knowledge distillation for anomaly detection[EB/OL]. (2023-10-09). https://arxiv.org/abs/2310.06047.
    [15]
    YAO L Y, GAO S B. Dual-student knowledge distillation networks for unsupervised anomaly detection[EB/OL]. (2024-02-01). https://arxiv.org/abs/2402.00448.
    [16]
    KINGMA D P, BA J. Adam: A method for stochastic optimization[EB/OL]. (2014-12-22). https://arxiv.org/abs/1412.6980.
    [17]
    LOSHCHILOV I, HUTTER F. Decoupled weight decay regularization[EB/OL]. (2017-11-14). https://arxiv.org/abs/1711.05101.
    [18]
    ZHANG Miao, HE Yi-juan, YANG Bo-yu, et al. Trajectory prediction method for high-speed train group tracking operation based on LSTM-KF model[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 296-310. doi: 10.19818/j.cnki.1671-1637.2024.03.021
    [19]
    SHRESTHA R, MOHAMMADI M, SINAEI S, et al. Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid[J]. Journal of Parallel and Distributed Computing, 2024, 193: 104951. doi: 10.1016/j.jpdc.2024.104951
    [20]
    LI J Z, SHI Y, ZHANG T, et al. Radar precipitation nowcasting based on ConvLSTM model in a small watershed in North China[J]. Natural Hazards, 2024, 120(1): 63-85. doi: 10.1007/s11069-023-06193-6
    [21]
    XIE T M, XU Q F, JIANG C X. Anomaly detection for multivariate times series through the multi-scale convolutional recurrent variational autoencoder[J]. Expert Systems with Applications, 2023, 231: 120725.
    [22]
    OLIVE X, KRUMMER J, FIGUET B, et al. Filtering techniques for ADS-B trajectory preprocessing[J]. Journal of Open Aviation Science, 2024, 2(2): 7882-7899.
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