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Trajectory prediction of flying vehicles based on deep learning methods

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Abstract

To deal with the threat from flying vehicles, it is of great significance to accurately predict the flight trajectory of flying vehicles using the known historical position data. In this paper, we investigate eight typical types of maneuvers of flying vehicles. Then, according to the kinematics model of the flying vehicle, eight kinds of typical maneuver trajectory equations are introduced. Next, the data set for neural network training is created by varying the critical parameters of the trajectory equation. Accordingly, we train the offline Long-Short Term Memory (LSTM) using 1/12 of the dataset of trajectories, and results show that the trained network can classify types of trajectories accurately. Based on the results of classification using the offline LSTM, we propose two trajectory prediction methods, and both of them achieve excellent prediction with and without noise interference. Furthermore, we propose an online prediction method, and it can accurately predict the subsequent trajectory of flying vehicles when the type of trajectory is not clear. And we propose a filter to improve the accuracy of the online prediction method for trajectory prediction with noise. The simulation results verify that all three previously proposed methods can accurately predict the next move based on historical data.

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References

  1. Makridakis S, Hibon M (2000) The m3-competition: results, conclusions and implications. Int J Forecast 16(4):451–476

    Article  Google Scholar 

  2. Bright DR, Mullen SL (2002) Short-range ensemble forecasts of precipitation during the southwest monsoon. Weather Forecast 17(5):1080–1100

    Article  Google Scholar 

  3. Boulares M, Barnawi A (2021) A novel uav path planning algorithm to search for floating objects on the ocean surface based on object’s trajectory prediction by regression. Robot Auton Syst 135:103673

    Article  Google Scholar 

  4. Xie G, Shangguan A, Fei R, Ji W, Ma W, Hei X (2020) Motion trajectory prediction based on a cnn-lstm sequential model. Sci China Inf Sci 63(11):1–21

    Article  Google Scholar 

  5. Fang L, Zhu H, Lv B, Liu Z, Meng W, Yu Y, Ji S, Cao Z (2020) Handitext: Handwriting recognition based on dynamic characteristics with incremental lstm. ACM Trans Data Sci 1(4):1–18

    Article  Google Scholar 

  6. Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107–144

    Article  MathSciNet  Google Scholar 

  7. Baydogan MG, Runger G, Tuv E (2013) A bag-of-features framework to classify time series. IEEE Trans Pattern Anal Mach Intell 35(11):2796–2802

    Article  Google Scholar 

  8. Schäfer P (2016) Scalable time series classification. Data Min Knowl Disc 30(5):1273–1298

    Article  MathSciNet  MATH  Google Scholar 

  9. Liu C, Hedrick JK (2017) Model predictive control-based target search and tracking using autonomous mobile robot with limited sensing domain. In: 2017 American control conference (ACC), pp 2937–2942. IEEE

  10. Meng W, He Z, Su R, Yadav PK, Teo R, Xie L (2016) Decentralized multi-uav flight autonomy for moving convoys search and track. IEEE Trans Control Syst Technol 25(4):1480–1487

    Article  Google Scholar 

  11. Xie G, Sun L, Wen T, Hei X, Qian F (2019) Adaptive transition probability matrix-based parallel imm algorithm. IEEE Trans Syst Man Cybern Syst 51(5):2980–2989

    Article  Google Scholar 

  12. Deo N, Trivedi MM (2018) Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms. In: 2018 IEEE Intelligent vehicles symposium (IV), pp 1179–1184. IEEE

  13. Rodrigues-Jr JF, Gutierrez MA, Spadon G, Brandoli B, Amer-Yahia S (2021) Lig-doctor Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Inform Sci 545:813–827

    Article  Google Scholar 

  14. Cai Y, Dai L, Wang H, Chen L, Li Y, Sotelo MA, Li Z (2021) Pedestrian motion trajectory prediction in intelligent driving from far shot first-person perspective video. IEEE Trans Intell Trans Syst

  15. Liu Q, Chuai G, Wang J, Pan J (2020) Proactive mobility management with trajectory prediction based on virtual cells in ultra-dense networks. IEEE Trans Veh Technol 69(8):8832– 8842

    Article  Google Scholar 

  16. Barata C, Nascimento JC, Lemos JM, Marques JS (2021) Sparse motion fields for trajectory prediction. Pattern Recogn 110:107631

    Article  Google Scholar 

  17. Song X, Chen K, Li X, Sun J, Hou B, Cui Y, Zhang B, Xiong G, Wang Z (2020) Pedestrian trajectory prediction based on deep convolutional lstm network. IEEE Trans Intell Transp Syst 22(6):3285–3302

    Article  Google Scholar 

  18. Pei Z, Qi X, Zhang Y, Ma M, Yang YH (2019) Human trajectory prediction in crowded scene using social-affinity long short-term memory. Pattern Recogn 93:273–282

    Article  Google Scholar 

  19. Shen C, Shi Y, Buckham B (2016) Integrated path planning and tracking control of an auv: a unified receding horizon optimization approach. IEEE/ASME Trans Mechatron 22(3):1163–1173

    Article  Google Scholar 

  20. Zhang T, Liu S, He X, Huang H, Hao K (2020) Underwater target tracking using forward-looking sonar for autonomous underwater vehicles. Sensors 20(1):102

    Article  Google Scholar 

  21. Nielsen LD, Sung I, Nielsen P (2019) Convex decomposition for a coverage path planning for autonomous vehicles: Interior extension of edges. Sensors 19(19):4165

    Article  Google Scholar 

  22. Yoon J, Lee AH, Lee H (2019) Rendezvous: Opportunistic data delivery to mobile users by uavs through target trajectory prediction. IEEE Trans Veh Technol 69(2):2230–2245

    Article  Google Scholar 

  23. Seker MY, Tekden AE, Ugur E (2019) Deep effect trajectory prediction in robot manipulation. Robot Auton Syst 119:173–184

    Article  Google Scholar 

  24. Goodie AS, Meisel MK, Ceren R, Hall DB, Doshi P (2016) Evaluating and improving probability assessment in an ambiguous, sequential environment. Curr Psychol 35(4):667–673

    Article  Google Scholar 

  25. Yang H, Hu B, Wang L (2017) A deep learning based handover mechanism for uav networks. In: 2017 20th International symposium on wireless personal multimedia communications (WPMC), pp 380–384. IEEE

  26. Pinto MF, Coelho FO, De Souza JP, Melo AG, Marcato AL, Urdiales C (2018) Ekf design for online trajectory prediction of a moving object detected onboard of a uav. In: 2018 13th APCA International conference on automatic control and soft computing (CONTROLO), pp 407–412. IEEE

  27. Malviya V, Kala R (2021) Trajectory prediction and tracking using a multi-behaviour social particle filter. Appl Intell, pp 1–43

  28. Qiao S, Tang C, Jin H, Long T, Dai S, Ku Y, Chau M (2010) Putmode: prediction of uncertain trajectories in moving objects databases. Appl Intell 33(3):370–386

    Article  Google Scholar 

  29. Yu Y, Tian N, Hao X, Ma T, Yang C (2021) Human motion prediction with gated recurrent unit model of multi-dimensional input. Appl Intell, pp 1–13

  30. Zhang G, Zhang C, Zhang W (2020) Evolutionary echo state network for long-term time series prediction: on the edge of chaos. Appl Intell 50(3):893–904

    Article  Google Scholar 

  31. Bibik P, Narkiewicz J, Zasuwa M, Żugaj M (2016) Quadrotor dynamics and control for precise handling. In: Innovative Simulation Systems, pp 335–351. Springer

  32. Dorobantu A, Murch A, Mettler B, Balas G (2013) System identification for small, low-cost, fixed-wing unmanned aircraft. J Aircr 50(4):1117–1130

    Article  Google Scholar 

  33. Baspinar B, Koyuncu E (2018) Aerial combat simulation environment for one-on-one engagement. In: 2018 AIAA Modeling and simulation technologies conference, pp 0432

  34. Hall J, McLain T (2008) Aerobatic maneuvering of miniature air vehicles using attitude trajectories, vol 7257

  35. Wei S, Zhang L, Liu H, Wang K (2020) Signal-domain kalman filtering: an approach for maneuvering target surveillance with wideband radar. Signal Process 177:107724

    Article  Google Scholar 

  36. van de Merwe R, Doucet A, de Freitas N, Wan E (2000) The unscented particle filter, advances in neural information processing systems

  37. Simon D (2006) Optimal state estimation: Kalman, H Infinity, and Nonlinear Approaches. John Wiley & Sons

  38. Schutz B, Tapley B, Born GH (2004) Statistical orbit determination. Elsevier

  39. Maybeck PS (1982) Stochastic models, estimation, and control. Academic Press

  40. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  41. Zhao X, Shi P, Zheng X, Zhang J (2015) Intelligent tracking control for a class of uncertain high-order nonlinear systems. IEEE Trans Neural Netw Learn Syst 27(9):1976–1982

    Article  MathSciNet  Google Scholar 

  42. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314

    Article  MathSciNet  MATH  Google Scholar 

  43. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366

    Article  MATH  Google Scholar 

  44. Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4 (2):251–257

    Article  MathSciNet  Google Scholar 

  45. Hu Z, Balakrishnan SN (2005) Parameter estimation in nonlinear systems using hopfield neural networks. J Aircr 42(1):41–53

    Article  Google Scholar 

  46. Mehr AD, Kahya E, Yerdelen C (2014) Linear genetic programming application for successive-station monthly streamflow prediction. Comput Geosci 70:63–72

    Article  Google Scholar 

  47. Neyshabur B, Tomioka R, Srebro N (2014) In search of the real inductive bias: On the role of implicit regularization in deep learning arXiv:1412.6614

  48. Ma L, Tian S (2020) A hybrid cnn-lstm model for aircraft 4d trajectory prediction. IEEE Access 8:134668–134680

    Article  Google Scholar 

  49. Park J, Jeong JS, Park YS (2021) Ship trajectory prediction based on bi-lstm using spectral-clustered ais data. J Mar Sci Eng 9(9):1037

    Article  Google Scholar 

  50. Siami-Namini S, Tavakoli N, Namin A.S (2019) The performance of lstm and bilstm in forecasting time series. In: 2019 IEEE International conference on big data (Big Data), pp 3285–3292. IEEE

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Authors and Affiliations

  1. School of Astronautics, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, 710072, Xi’an, China

    Minghu Tan, Hong Shen, Kang Xi & Bin Chai

  2. National Key Laboratory of Aerospace Flight Dynamics, 127 West Youyi Road, Xi’an, 710072, Xi’an, China

    Minghu Tan, Hong Shen, Kang Xi & Bin Chai

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  1. Minghu Tan
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  2. Hong Shen
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  3. Kang Xi
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  4. Bin Chai
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Correspondence to Hong Shen.

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Hong Shen, Kang Xi and Bin Chai contributed equally to this work.

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Tan, M., Shen, H., Xi, K. et al. Trajectory prediction of flying vehicles based on deep learning methods. Appl Intell 53, 13621–13642 (2023). https://doi.org/10.1007/s10489-022-04098-8

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