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Attention based trajectory prediction method under the air combat environment

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Abstract

In close-range air combat, highly reliable trajectory prediction results can help pilots to win victory to a great extent. However, traditional trajectory prediction methods can only predict the precise location that the target aircraft may reach, which cannot meet the requirements of high-precision, real-time trajectory prediction for highly maneuvering targets. To this end, this paper proposes an attention-based convolution long sort-term memory (AttConvLSTM) network to calculate the arrival probability of each space in the reachable area of the target aircraft. More specifically, by segmenting the reachable area, the trajectory prediction problem is transformed into a classification problem for solution. Second, the AttConvLSTM network is proposed as an efficient feature extraction method, and combined with the multi-layer perceptron (MLP) to solve this classification problem. Third, a novel loss function is designed to accelerate the convergence of the proposed model. Finally, the flight trajectories generated by experienced pilots are used to evaluate the proposed method. The results indicate that the mean absolute error of the proposed method is no more than 45.73m, which is of higher accuracy compared to other state-of-the-art algorithms.

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References

  1. Akabane AT, Pazzi RW, Madeira ER, Villas LA (2017) Modeling and prediction of vehicle routes based on hidden markov model, IEEE, VTC-Fall

  2. Alahi A, Goel K, Ramanathan V, Robicquet A, Fei-Fei L, Savarese S (2016) Social lstm: Human trajectory prediction in crowded spaces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 961–971

  3. Barratt ST, Kochenderfer MJ, Boyd SP (2018) Learning probabilistic trajectory models of aircraft in terminal airspace from position data. IEEE Trans Intell Transp Syst 20(9):3536–3545

    Article  Google Scholar 

  4. Bolstad BM, Irizarry RA, ÅStrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193

    Article  Google Scholar 

  5. Bukhari AH, Raja MAZ, Sulaiman M, Islam S, Shoaib M, Kumam P (2020) Fractional neuro-sequential arfima-lstm for financial market forecasting. IEEE Access 8:71326–71338

    Article  Google Scholar 

  6. Chen K, Wang J, Chen LC, Gao H, Xu W, Nevatia R (2015) Abc-cnn: An attention based convolutional neural network for visual question answering. arXiv preprint arXiv:151105960

  7. Chen L, Zhang H, Xiao J, Nie L , Shao J, Liu W, Chua TS (2017) Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5659–5667

  8. Chen P, Sun Z, Bing L, Yang W (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 452–461

  9. Chimmula VKR, Zhang L (2020) Time series forecasting of covid-19 transmission in Canada using lstm networks. Chaos, Solitons & Fractals 135:109864

    Article  MATH  Google Scholar 

  10. Enami S, Shiomoto K (2019) Spatio-temporal human mobility prediction based on trajectory data mining for resource management in mobile communication networks. In: 2019 IEEE 20Th international conference on high performance switching and routing. IEEE, HPSR, pp 1–6

  11. Fuglede B, Topsoe F (2004) Jensen-shannon divergence and hilbert space embedding. In: International Symposium onInformation Theory, vol 2004. Proceedings., IEEE, ISIT, p 31

  12. Gambs S, Killijian MO, del Prado Cortez MN (2012) Next place prediction using mobility markov chains. In: Proceedings of the first workshop on measurement, privacy, and mobility, pp 1–6

  13. Gan S, Liang S, Li K, Deng J, Cheng T (2016) Ship trajectory prediction for intelligent traffic management using clustering and ann. In: 2016 UKACC 11Th international conference on control. IEEE, CONTROL, pp 1–6

  14. Han P, Wang W, Shi Q, Yue J (2021) A combined online-learning model with k-means clustering and gru neural networks for trajectory prediction. Ad Hoc Netw 117:102476

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Inoue M, Yamashita T, Nishida T (2019) Robot path planning by lstm network under changing environment. In: Advances in Computer Communication and Computational Sciences, Springer, pp 317–329

  17. Ju C, Wang Z, Long C, Zhang X, Chang DE (2020) Interaction-aware kalman neural networks for trajectory prediction. In: In, vol 2020. IEEE Intelligent Vehicles Symposium (IV), IEEE, pp 1793–1800

  18. Karevan Z, Suykens JA (2020) Transductive lstm for time-series prediction: an application to weather forecasting. Neural Netw 125:1–9

    Article  Google Scholar 

  19. Kim TY, Cho SB (2019) Predicting residential energy consumption using cnn-lstm neural networks. Energy 182:72–81

    Article  Google Scholar 

  20. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980

  21. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural computation 1(4):541–551

    Article  Google Scholar 

  22. Li J, Lu L, Liu C, Gong Y (2018) Exploring layer trajectory lstm with depth processing units and attention. In: IEEE Spoken Language Technology Workshop (SLT), vol 2018, IEEE, pp 456–462

  23. Li M, Lu F, Zhang H, Chen J (2020) Predicting future locations of moving objects with deep fuzzy-lstm networks. Transportmetrica A:, Transport Science 16(1):119–136

    Article  Google Scholar 

  24. Li Y, Liang R, Wei W, Wang W, Zhou J, Li X (2021) Temporal pyramid network with spatial-temporal attention for pedestrian trajectory prediction. IEEE Transactions on Network Science and Engineering

  25. Lin Y, Wang C, Song H, Li Y (2021) Multi-head self-attention transformation networks for aspect-based sentiment analysis. IEEE Access 9:8762–8770

    Article  Google Scholar 

  26. Liu P, Ma Y (2017) A deep reinforcement learning based intelligent decision method for ucav air combat. In: Asian Simulation Conference, Springer, pp 274–286

  27. Luo Q, Deng Q, Gong G, Zhang L, Han W, Li K (2020) An efficient memetic algorithm for distributed flexible job shop scheduling problem with transfers. Expert Syst Appl 160:113721

    Article  Google Scholar 

  28. Lymperopoulos I, Lygeros J (2010) Sequential monte carlo methods for multi-aircraft trajectory prediction in air traffic management. International Journal of Adaptive Control and Signal Processing 24(10):830–849

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  30. Ma Z, Yao M, Hong T, Li B (2019) Aircraft surface trajectory prediction method based on lstm with attenuated memory window. In: Journal of Physics: Conference Series, IOP Publishing, vol 1215, p 012003

  31. Mathew W, Raposo R, Martins B (2012) Predicting future locations with hidden markov models. In: Proceedings of the 2012 ACM conference on ubiquitous computing, pp 911–918

  32. Messaoud K, Yahiaoui I, Verroust A, Nashashibi F (2020) Attention based vehicle trajectory prediction. IEEE Transactions on Intelligent Vehicles

  33. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv:180403999

  34. Park K, Choi Y, Choi WJ, Ryu HY, Kim H (2020) Lstm-based battery remaining useful life prediction with multi-channel charging profiles. IEEE Access 8:20786–20798

    Article  Google Scholar 

  35. 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 

  36. Rudenko A, Palmieri L, Herman M, Kitani KM, Gavrila DM, Arras KO (2020) Human motion trajectory prediction: a survey. The International Journal of Robotics Research 39(8):895–935

    Article  Google Scholar 

  37. Sadeghian A, Kosaraju V, Sadeghian A, Hirose N, Rezatofighi H, Savarese S (2019) Sophie: An attentive gan for predicting paths compliant to social and physical constraints. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 1349–1358

  38. Song Y, Cheng P, Mu C (2012) An improved trajectory prediction algorithm based on trajectory data mining for air traffic management. In: 2012 IEEE International Conference on Information and Automation, IEEE, pp 981–986

  39. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv:170603762

  40. Wang Y, Shen J, Liu X (2017) Dynamic obstacles trajectory prediction and collision avoidance of usv. In: 2017 36Th chinese control conference. IEEE, CCC, pp 2910–2914

  41. Wiest J, Höffken M, Kreßel U, Dietmayer K (2012) Probabilistic trajectory prediction with gaussian mixture models. In: 2012 IEEE Intelligent Vehicles Symposium, IEEE, pp 141–146

  42. Wu C, Xiong Q, Gao M, Li Q, Yu Y, Wang K (2021) A relative position attention network for aspect-based sentiment analysis. Knowl Inf Syst 63(2):333–347

    Article  Google Scholar 

  43. Wu ZJ, Tian S, Ma L (2020) A 4d trajectory prediction model based on the bp neural network. J Intell Syst 29(1):1545–1557

    Article  Google Scholar 

  44. Xie J, Yang Q, Dai S, Wang W, Zhang J (2020) Air combat maneuver decision based on reinforcement genetic algorithm. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38 (6):1330–1338

    Article  Google Scholar 

  45. Ximeng X, Rennong Y, Tao Z, Bin Y (2019) Trajectory prediction of target aircraft in air combat based on ga-oif-elman neural network, IEEE, ICAICA

  46. Yang Q, Zhang J, Shi G, Hu J, Wu Y (2019) Maneuver decision of uav in short-range air combat based on deep reinforcement learning. IEEE Access 8:363–378

    Article  Google Scholar 

  47. Yin W, Schütze H, Xiang B, Zhou B (2016) Abcnn: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics 4:259–272

    Article  Google Scholar 

  48. Zhang A, Shuida B, Fei G, Wenhao B (2019) A novel strong tracking cubature kalman filter and its application in maneuvering target tracking. Chin J Aeronaut 32(11):2489–2502

    Article  Google Scholar 

  49. Zhang P, Xue J, Lan C, Zeng W, Gao Z, Zheng N (2018) Adding attentiveness to the neurons in recurrent neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 135–151

  50. Zhang P, Ouyang W, Zhang P, Xue J, Zheng N (2019) Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12085–12094

  51. Zhang P, Xue J, Zhang P, Zheng N, Ouyang W (2020) Social-aware pedestrian trajectory prediction via states refinement lstm. IEEE transactions on pattern analysis and machine intelligence

  52. Zhang S, Wang L, Zhu M, Chen S, Zhang H, Zeng Z (2021) A bi-directional lstm ship trajectory prediction method based on attention mechanism. In: 2021 IEEE 5Th advanced information technology, electronic and automation control conference (IAEAC), IEEE, vol 5, pp 1987–1993

  53. Zhang Z, Huang C, Ding D, Tang S, Han B, Huang H (2019) Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking. Nonlinear Dynamics 97(2):1227–1243

    Article  Google Scholar 

  54. Zhenjiang D, Jia D, Xiaohui J, Yongli W (2017) Rtmatch: Real-time location prediction based on trajectory pattern matching. In: International Conference on Database Systems for Advanced Applications, Springer, pp 103–117

  55. Zhou X, Kuang D, Zhao W, Xu C, Feng J, Wang C (2021) Lane-changing decision method based nash q-learning with considering the interaction of surrounding vehicles. IET Intell Transp Syst 14(14):2064–2072

    Article  Google Scholar 

  56. Zhu Y, Liu J, Guo C, Song P, Zhang J, Zhu J (2020) Prediction of battlefield target trajectory based on lstm, IEEE, ICCA

  57. Zyner A, Worrall S, Nebot E (2019) Naturalistic driver intention and path prediction using recurrent neural networks. IEEE transactions on intelligent transportation systems 21(4):1584–1594

    Article  Google Scholar 

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant Nos. 61903305 and 62073267), the Aeronautical Science Foundation of China (Grant No. 201905053001), the Research Funds for Interdisciplinary Subject, NWPU.

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  1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China

    An Zhang, Baichuan Zhang, Wenhao Bi & Zeming Mao

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  1. An Zhang
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Correspondence to Baichuan Zhang.

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Zhang, A., Zhang, B., Bi, W. et al. Attention based trajectory prediction method under the air combat environment. Appl Intell 52, 17341–17355 (2022). https://doi.org/10.1007/s10489-022-03292-y

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