access icon openaccess Research on radar clutter recognition method based on LSTM

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 19/02/2019, Accepted 02/05/2019, Published 30/05/2019

Considering the increasingly complex electromagnetic environment of radar detection, it still contains a lot of clutter after target detection, which is a challenging problem for subsequent target tracking. A radar clutter recognition method based on long short-term memory (LSTM) neural network is proposed here. This method can further identify clutter points after target detection and improve the quality of target tracking. LSTM neural network is trained and compared with K nearest neighbour method and support vector machine. It is found that the clutter recognition accuracy of this method is up to 86.3%, which is 12.2 and 4.1% higher than the latter two methods. At the same time, the target loss rate of this method is only 16.6%, which is 18.3 and 5.7% lower than the latter two methods respectively. The experimental results show that the radar clutter recognition method based on LSTM neural network is effective.

Inspec keywords: radar detection; recurrent neural nets; object detection; radar clutter; radar signal processing; learning (artificial intelligence); target tracking

Other keywords: target tracking; target detection; radar detection; LSTM neural network; radar clutter recognition method; clutter recognition accuracy; complex electromagnetic environment; support vector machine; short-term memory neural network; K nearest neighbour method; clutter points

Subjects: Signal processing and detection; Radar equipment, systems and applications; Digital signal processing; Neural computing techniques

References

    1. 1)
      • 10. Pan, J., Zhuang, Y., Fong, S.: ‘The impact of data normalization on stock market prediction: using SVM and technical indicators’. Int. Conf. on Soft Computing in Data Science, 2016, vol. 9, no. 18, pp. 72–88.
    2. 2)
      • 9. Zhou, C., Sun, C., Liu, Z., et al: ‘A C-LSTM neural network for text classification’, Comput. Sci., 2015, 1, (4), pp. 39–44.
    3. 3)
      • 4. Fang, X.L., Liang, D.N.: ‘Radar clutter recognition based on feature extraction by adaptiveα truncation set’, Signal Process., 2005, 10, (30), pp. 439–442.
    4. 4)
      • 1. Xu, S., Tang, C., Jing, P., et al: ‘Efficient centralized track initiation method for multistatic radar’. Int. Conf. on Inf. Fusion, 2014, vol. 10, no. 7, pp. 1–7.
    5. 5)
      • 2. Xu, T., Tharmarasa, R., Mcdonald, M., et al: ‘Multiple detection-aided low-observable track initialization using ML-PDA’, IEEE Trans. Aerosp. Electron. Syst., 2017, 2, (6), pp. 722–735.
    6. 6)
      • 5. Jin, Z.L., Pan, Q., Liang, Y., et al: ‘SVM-based land/sea clutter identification with multi-features’. Proc of the 31st Chinese Control Conf, 2012, vol. 7, no. 25, pp. 3903–3908.
    7. 7)
      • 8. Duan, Y., Lv, Y., Wang, F.Y.: ‘Ravel time prediction with LSTM neural network’. IEEE Int. Conf. on Intelligent Transportation Systems, 2016, vol. 12, no. 26, pp. 1053–1058.
    8. 8)
      • 6. Li, Y., He, M., Zhang, N.: ‘An ionospheric clutter recognition method based on machine learning’. IEEE Int. Symp. on Antennas and Propagation & USNC/URSI National Radio Science Meeting, 2017, vol. 10, no. 19, pp. 1637–1638.
    9. 9)
      • 11. Xiong, Z.Z., Xiong, F.: ‘Application of BP neural network in PM2.5 data forecasting’, Telecom Power Technol, 2017, 5, (25), pp. 77–79.
    10. 10)
      • 3. Ma, X., Fang, X., Chen, S.: ‘Radar clutter recognition based on feature extraction by α truncation-set’. Proc. 2001 CIE Int. Conf. on Radar, 2001, vol. 8, no. 7, pp. 436–439.
    11. 11)
      • 7. Fan, Y.Y., Qian, Y.R., Yang, L., et al: ‘Cotton recognition method for remote sensing image based on BP neural network’, Comput. Eng. Design, 2017, 5, (16), pp. 1356–1360.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2019.0242
Loading

Related content

content/journals/10.1049/joe.2019.0242
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading