Skip to main content
SpringerLink
Log in

Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles (UAVs) and has attracted extensive attention from scholars. Knowledge-based approaches rely on prior knowledge, while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems (UASs). Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models, they often lack parameter selection and are limited by the cost of labeling anomalous data. Furthermore, flight data with random noise pose a significant challenge for anomaly detection. This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder (STC-LSTM-AE) neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data. First, UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model. Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge. Then, the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner. Finally, the method’s effectiveness is validated on real UAV flight data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ullah S, Kim K I, Kim K H, et al. UAV-enabled healthcare architecture: Issues and challenges. Future Gen Comput Syst, 2019, 97: 425–432

    Article  Google Scholar 

  2. Aggarwal S, Kumar N, Alhussein M, et al. Blockchain-based UAV path planning for healthcare 4.0: Current challenges and the way ahead. IEEE Network, 2021, 35: 20–29

    Article  Google Scholar 

  3. Butilă E V, Boboc R G. Urban traffic monitoring and analysis using unmanned aerial vehicles (UAVs): A systematic literature review. Remote Sens, 2022, 14: 620

    Article  Google Scholar 

  4. Li C, Li S, Zhang A, et al. A Siamese hybrid neural network framework for few-shot fault diagnosis of fixed-wing unmanned aerial vehicles. J Comput Des Eng, 2022, 9: 1511–1524

    Google Scholar 

  5. Liu Y, Ding W. A KNNS based anomaly detection method applied for UAV flight data stream. In: 2015 Prognostics and System Health Management Conference (PHM). Beijing, 2015. 1–8

  6. Zhao W, Li L, Alam S, et al. An incremental clustering method for anomaly detection in flight data. Trans Res Part C-Emerging Tech, 2021, 132: 103406

    Article  Google Scholar 

  7. Hawkins D M. Identification of Outliers. London: Chapman and Hall, 1980

    Book  MATH  Google Scholar 

  8. Keogh E, Lin J, Lee S H, et al. Finding the most unusual time series subsequence: Algorithms and applications. Knowl Inf Syst, 2007, 11: 1–27

    Article  Google Scholar 

  9. Djenouri Y, Belhadi A, Lin J C W, et al. A survey on urban traffic anomalies detection algorithms. IEEE Access, 2019, 7: 12192–12205

    Article  Google Scholar 

  10. Zhao C, Chang X, Xie T, et al. Unsupervised anomaly detection based method of risk evaluation for road traffic accident. Appl Intell, 2023, 53: 369–384

    Article  Google Scholar 

  11. Summerville D H, Zach K M, Chen Y. Ultra-lightweight deep packet anomaly detection for Internet of Things devices. In: 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC). Nanjing, 2015. 1–8

  12. Wang X, Garg S, Lin H, et al. Toward accurate anomaly detection in industrial internet of things using hierarchical federated learning. IEEE Internet Things J, 2021, 9: 7110–7119

    Article  Google Scholar 

  13. Anandakrishnan A, Kumar S, Statnikov A, et al. Anomaly detection in finance: Editors’ introduction. In: KDD 2017 Workshop on Anomaly Detection in Finance. Halifax, 2018. 1–7

  14. Li L, Hansman R J, Palacios R, et al. Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring. Trans Res Part C-Emerging Tech, 2016, 64: 45–57

    Article  Google Scholar 

  15. Schumann J, Rozier K Y, Reinbacher T, et al. Towards real-time, onboard, hardware-supported sensor and software health management for unmanned aerial systems. Intl J Prog Health Man, 2015, 6: 1–27

    Google Scholar 

  16. Gupta M, Gao J, Aggarwal C C, et al. Outlier detection for temporal data: A survey. IEEE Trans Knowl Data Eng, 2013, 26: 2250–2267

    Article  Google Scholar 

  17. Puranik T G, Mavris D N. Identifying instantaneous anomalies in general aviation operations. In: 17th AIAA Aviation Technology, Integration, and Operations Conference. Denver, 2017. 3779–3794

  18. Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM Comput Surv, 2009, 41: 1–58

    Article  Google Scholar 

  19. Zhong J, Zhang Y, Wang J, et al. Unmanned aerial vehicle flight data anomaly detection and recovery prediction based on spatio-temporal correlation. IEEE Trans Rel, 2021, 71: 457–468

    Article  Google Scholar 

  20. Qi J, Zhao X, Jiang Z, et al. An adaptive threshold neural-network scheme for rotorcraft UAV sensor failure diagnosis. In: International Symposium on Neural Networks. Berlin, 2007. 589–596

  21. Bu J, Sun R, Bai H, et al. Integrated method for the UAV navigation sensor anomaly detection. IET Radar Sonar Navigat, 2017, 11: 847–853

    Article  Google Scholar 

  22. Abbaspour A, Aboutalebi P, Yen K K, et al. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV. ISA Trans, 2017, 67: 317–329

    Article  Google Scholar 

  23. López-Estrada F R, Ponsart J C, Theilliol D, et al. LPV model-based tracking control and robust sensor fault diagnosis for a quadrotor UAV. J Intell Robot Syst, 2016, 84: 163–177

    Article  Google Scholar 

  24. Alos A M, Dahrouj Z, Dakkak M. A novel technique to assess UAV behavior using PCA-based anomaly detection algorithm. Int J Mech Eng Robot Res, 2020, 9: 721–726

    Google Scholar 

  25. Ma G, Xu S, Jiang B, et al. Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning. Energy Environ Sci, 2022, 15: 4083–4094

    Article  Google Scholar 

  26. Yuan Y, Ma G, Cheng C, et al. A general end-to-end diagnosis framework for manufacturing systems. Natl Sci Rev, 2020, 7: 418–429

    Article  Google Scholar 

  27. Bronz M, Baskaya E, Delahaye D, et al. Real-time fault detection on small fixed-wing UAVs using machine learning. In: 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). San Antonio, 2020. 1–10

  28. Duan Y, Xu Y Q, Zhao Y P, et al. Unmanned aerial vehicle sensor data anomaly detection using kernel principle component analysis. In: 2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). Yangzhou, 2017. 241–246

  29. Wang B, Wang Z, Liu L, et al. Data-driven anomaly detection for UAV sensor data based on deep learning prediction model. In: 2019 Prognostics and System Health Management Conference (PHM-Paris). Paris, 2019. 286–290

  30. Wang B, Liu D, Peng Y, et al. Multivariate regression-based fault detection and recovery of UAV flight data. IEEE Trans Instrum Meas, 2019, 69: 3527–3537

    Article  Google Scholar 

  31. Lee H, Li G, Rai A, et al. Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft. Adv Eng Inf, 2020, 44: 101071

    Article  Google Scholar 

  32. Sun G, Li J, Dai J, et al. Feature selection for IoT based on maximal information coefficient. Future Gener Comput Syst, 2018, 89: 606–616

    Article  Google Scholar 

  33. Xia X Z, Cheng L. Adaptive Takagi-Sugeno fuzzy model and model predictive control of pneumatic artificial muscles. Sci China Tech Sci, 2021, 64: 2272–2280

    Article  Google Scholar 

  34. Van Houdt G, Mosquera C, Nápoles G. A review on the long short-term memory model. Artif Intell Rev, 2020, 53: 5929–5955

    Article  Google Scholar 

  35. Su L, Zhang S Y, Ji Y, et al. A novel approach for flip chip inspection based on improved SDELM and vibration signals. Sci China Tech Sci, 2022, 65: 1087–1097

    Article  Google Scholar 

  36. Taylor B. Thor Flight 69. Retrieved from the University of Minnesota Digital Conservancy, 2012. https://hdl.handle.net/11299/174347

  37. Han J, Kamber M. Data Mining: Concepts and Techniques. 2nd ed. Waltham: Morgan Kaufmann, 2006

    MATH  Google Scholar 

  38. Liu Y, Dang B, Li Y, et al. Applications of Savitzky-Golay filter for seismic random noise reduction. Acta Geophys, 2016, 64: 101–124

    Article  Google Scholar 

  39. Sun Q, Jiang B, Zhu H, et al. Hard thresholding regression. Scand J Statist, 2019, 46: 314–328

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Guizhou University, Guiyang, 550025, China

    Lei Yang, ShaoBo Li, ChuanJiang Li, CaiChao Zhu, AnSi Zhang & GuoQiang Liang

  2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China

    ShaoBo Li & AnSi Zhang

  3. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    CaiChao Zhu

Authors
  1. Lei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ShaoBo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ChuanJiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. CaiChao Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. AnSi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. GuoQiang Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ShaoBo Li.

Additional information

This work was supported by the National Key Research and Development Program of China (Grant No. 2020YFB1713300), the Guizhou Provincial Colleges and Universities Talent Training Base Project (Grant No. [2020] 009), the Guizhou Province Science and Technology Plan Project (Grant Nos. [2015]4011, [2017]5788), the Guizhou Provincial Department of Education Youth Science and Technology Talent Growth Project (Grant No. [2022]142), the Scientific Research Project for Introducing Talents from Guizhou University (Grant No. (2021)74), and the Guizhou Province Higher Education Integrated Research Platform Project (Grant No. [2020]005).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, L., Li, S., Li, C. et al. Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation. Sci. China Technol. Sci. 66, 1304–1316 (2023). https://doi.org/10.1007/s11431-022-2312-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-022-2312-8

Search

Navigation