Research on the Motion Features Model for Underwater Targets with Multiple Highlights and Multiple Micro-Motion Forms
Abstract
Motion characterization, including Doppler and micro-Doppler, is crucial for the detection and identification of high-speed underwater targets. Under high-frequency and short-range conditions, underwater targets cannot be simply regarded as single highlight targets as they exhibit a complex structure with multiple scattering centers accompanied by distinct micro-motions. To address this multi-highlight and multi-micro-motion scenario, a model is proposed to characterize the motion features of underwater targets. Firstly, a mathematical model is established to represent the micro-Doppler features based on the single-highlight model. Subsequently, considering the overlap of multiple highlight echoes caused by the high-speed translation of the target and the long pulse detection signal, precise representation is achieved by setting motion positions and calculating time delays within the model. The results represent the echoes of moving targets with multiple highlights and micromotions. Finally, a time-frequency analysis method is employed to extract motion features and estimate target parameters, thereby validating the accuracy and effectiveness of the proposed model. This research provides a theoretical foundation for the modeling of underwater moving targets.Keywords:
micro-motion, complex motion, micro-Doppler, underwater micro-motion model, multi-highlight modelReferences
1. Chen G. (2014), Micro-Doppler signature analysis of radar targets and its applications [in Chinese], Ph.D. Thesis, Xidian University in Xi’an, China, https://doi.org/10.7666/d.D725864
2. Chen V.C., Li F., Ho S.S., Wechsler H. (2003), Analysis of micro-Doppler signatures, [in:] IEE Proceedings- Radar, Sonar and Navigation, 150(4): 271–276, https://doi.org/10.1049/ip-rsn%3A20030743
3. Clemente C., Balleri A., Woodbridge K., Soraghan J.J. (2013), Developments in target micro-Doppler signatures analysis: Radar imaging, ultrasound and through-the-wall radar, EURASIP Journal on Advances in Signal Processing, 2013: 47, https://doi.org/10.1186/1687-6180-2013-47
4. Dong Z., Li Y., Chen X. (2013), Submarine echo simulation method based on highlight model [in Chinese], Computer Simulation, 30(6): 38–41, https://doi.org/10.3969/j.issn.1006-9348.2013.06.009
5. Gao H., Xie L., Wen S., Kuang Y. (2010), Micro-Doppler signature extraction from ballistic target with micro-motions, IEEE Transactions on Aerospace and Electronic Systems, 46(4): 1969–1982, https://doi.org/10.1109/TAES.2010.5595607
6. Gong Z., Li C., Jiang F., Zheng J. (2020), AUV-aided localization of underwater acoustic devices based on Doppler shift measurements, IEEE Transactions on Wireless Communications, 19(4): 2226–2239, https://doi.org/10.1109/TWC.2019.2963296
7. Han L., Feng C. (2020), Micro-Doppler-based space target recognition with a one-dimensional parallel network, International Journal of Antennas and Propagation, 2020: 8013802, https://doi.org/10.1155/2020/8013802
8. Han M., Wang C., Sun Q., Wang W., Lu Y. (2020), Measurement and analysis of ambient noise in the South China Sea based on underwater acoustic buoy [in Chinese], Journal of Applied Acoustics, 39(4): 536–542, https://doi.org/10.11684/j.issn.1000-310X.2020.04.006
9. Hanif A., Muaz M., Hasan A., Adeel M. (2022), Micro-Doppler based target recognition with radars: A review, IEEE Sensors Journal, 22(4): 2948–2961, https://doi.org/10.1109/JSEN.2022.3141213
10. Kashyap R., Singh I., Ram S.S. (2015), Micro-Doppler signatures of underwater vehicles using acoustic radar, [in:] 2015 IEEE Radar Conference (Radar-Con), pp. 1222–1227, https://doi.org/10.1109/RADAR.2015.7131181
11. Kim Y., Ling H. (2009), Human activity classification based on micro-Doppler signatures using a support vector machine, IEEE Transactions on Geoscience and Remote Sensing, 47(5): 1382–1337, https://doi.org/10.1109/TGRS.2009.2012849
12. Kim Y., Moon T. (2016), Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks, IEEE Geoscience and Remote Sensing Letters, 13(1): 8–12, https://doi.org/10.1109/LGRS.2015.2491329
13. Kou S., Feng X. (2022), Angle-micro-Doppler frequency image of underwater target multi-highlight combining with sparse reconstruction, Applied Acoustics, 188: 108563, https://doi.org/10.1016/j.apacoust.2021.108563
14. Kulhandjian H., Ramachandran N., Kulhandjian M.K., D’Amours C. (2020), Human activity classification in underwater using sonar and deep learning, [in:] Proceedings of the 14th International Conference on Underwater Networks & Systems, pp. 1–5, https://doi.org/10.1145/3366486.3366509
15. Li S., Liu S. (2016), A modeling and simulation based on k-distribution model [in Chinese], Ship Science and Technology, 38(s1): 158–161, https://doi.org/10.3404/j.issn.1672-7619.2016.S1.029
16. Saffari A., Zahiri S.-H., Khishe M. (2023), Automatic recognition of sonar targets using feature selection in micro-Doppler signature, Defence Technology, 20: 58–71, https://doi.org/10.1016/j.dt.2022.05.007
17. Tang W. (1994), Highlight model of echoes from sonar targets, Acta Acustica, 19(2): 92–100.
18. Tang Y., Wang X., Li H., Gao C., Miao X. (2020), Experimental research on interior field noise and the vibration characteristics of composite reinforced sheet-beam structures, Applied Acoustics, 160: 107154, https://doi.org/10.1016/j.apacoust.2019.107154
19. Wang Z., Ren A., Zhang Q., Zahid A., Abbasi Q.H. (2023), Recognition of approximate motions of human based on micro-Doppler features, IEEE Sensors Journal, 23(11): 12388–12397, https://doi.org/10.1109/JSEN.2023.3267820
20. Wu Y., Luo M., Li S. (2022), Measurement and extraction of micro-Doppler feature of underwater rotating target echo, [in:] OCEANS 2022 – Chennai, pp. 1–5, https://doi.org/10.1109/OCEANSChennai45887.2022.9775247
21. Xu L. (2016), Study on Doppler parameter estimation of underwater acoustic signal and its application [in Chinese], Ph.D. Thesis, Northwestern Polytechnical University in Xi’an, China, https://doi.org/10.7666/d.D01304090
22. Yang Y., Fan J., Wang B. (2023), Research on scattering feature extraction of underwater moving cluster targets based on the highlight model, Archives of Acoustics, 48(2): 235–247, https://doi.org/10.24425/aoa.2023.145235
23. Zhang B., Hu Y., Wang H., Zhuang Z. (2018), Underwater source localization using TDOA and FDOA measurements with unknown propagation speed and sensor parameter errors, IEEE Access, 6: 36645–36661, https://doi.org/10.1109/ACCESS.2018.2852636
24. Zhang R., Wang Y., Yeh C., Lu X. (2023), Precession parameter estimation of warhead with fins based on micro-Doppler effect and radar network, IEEE Transactions on Aerospace and Electronic Systems, 59(1): 443–459, https://doi.org/10.1109/TAES.2022.3182635
25. Zhao Y., Su Y. (2023), Estimation of micro-Doppler parameters with combined null space pursuit methods for the identification of LSS UAVs, IEEE Transactions on Geoscience and Remote Sensing, 61: 1–11, https://doi.org/10.1109/TGRS.2023.3264643
2. Chen V.C., Li F., Ho S.S., Wechsler H. (2003), Analysis of micro-Doppler signatures, [in:] IEE Proceedings- Radar, Sonar and Navigation, 150(4): 271–276, https://doi.org/10.1049/ip-rsn%3A20030743
3. Clemente C., Balleri A., Woodbridge K., Soraghan J.J. (2013), Developments in target micro-Doppler signatures analysis: Radar imaging, ultrasound and through-the-wall radar, EURASIP Journal on Advances in Signal Processing, 2013: 47, https://doi.org/10.1186/1687-6180-2013-47
4. Dong Z., Li Y., Chen X. (2013), Submarine echo simulation method based on highlight model [in Chinese], Computer Simulation, 30(6): 38–41, https://doi.org/10.3969/j.issn.1006-9348.2013.06.009
5. Gao H., Xie L., Wen S., Kuang Y. (2010), Micro-Doppler signature extraction from ballistic target with micro-motions, IEEE Transactions on Aerospace and Electronic Systems, 46(4): 1969–1982, https://doi.org/10.1109/TAES.2010.5595607
6. Gong Z., Li C., Jiang F., Zheng J. (2020), AUV-aided localization of underwater acoustic devices based on Doppler shift measurements, IEEE Transactions on Wireless Communications, 19(4): 2226–2239, https://doi.org/10.1109/TWC.2019.2963296
7. Han L., Feng C. (2020), Micro-Doppler-based space target recognition with a one-dimensional parallel network, International Journal of Antennas and Propagation, 2020: 8013802, https://doi.org/10.1155/2020/8013802
8. Han M., Wang C., Sun Q., Wang W., Lu Y. (2020), Measurement and analysis of ambient noise in the South China Sea based on underwater acoustic buoy [in Chinese], Journal of Applied Acoustics, 39(4): 536–542, https://doi.org/10.11684/j.issn.1000-310X.2020.04.006
9. Hanif A., Muaz M., Hasan A., Adeel M. (2022), Micro-Doppler based target recognition with radars: A review, IEEE Sensors Journal, 22(4): 2948–2961, https://doi.org/10.1109/JSEN.2022.3141213
10. Kashyap R., Singh I., Ram S.S. (2015), Micro-Doppler signatures of underwater vehicles using acoustic radar, [in:] 2015 IEEE Radar Conference (Radar-Con), pp. 1222–1227, https://doi.org/10.1109/RADAR.2015.7131181
11. Kim Y., Ling H. (2009), Human activity classification based on micro-Doppler signatures using a support vector machine, IEEE Transactions on Geoscience and Remote Sensing, 47(5): 1382–1337, https://doi.org/10.1109/TGRS.2009.2012849
12. Kim Y., Moon T. (2016), Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks, IEEE Geoscience and Remote Sensing Letters, 13(1): 8–12, https://doi.org/10.1109/LGRS.2015.2491329
13. Kou S., Feng X. (2022), Angle-micro-Doppler frequency image of underwater target multi-highlight combining with sparse reconstruction, Applied Acoustics, 188: 108563, https://doi.org/10.1016/j.apacoust.2021.108563
14. Kulhandjian H., Ramachandran N., Kulhandjian M.K., D’Amours C. (2020), Human activity classification in underwater using sonar and deep learning, [in:] Proceedings of the 14th International Conference on Underwater Networks & Systems, pp. 1–5, https://doi.org/10.1145/3366486.3366509
15. Li S., Liu S. (2016), A modeling and simulation based on k-distribution model [in Chinese], Ship Science and Technology, 38(s1): 158–161, https://doi.org/10.3404/j.issn.1672-7619.2016.S1.029
16. Saffari A., Zahiri S.-H., Khishe M. (2023), Automatic recognition of sonar targets using feature selection in micro-Doppler signature, Defence Technology, 20: 58–71, https://doi.org/10.1016/j.dt.2022.05.007
17. Tang W. (1994), Highlight model of echoes from sonar targets, Acta Acustica, 19(2): 92–100.
18. Tang Y., Wang X., Li H., Gao C., Miao X. (2020), Experimental research on interior field noise and the vibration characteristics of composite reinforced sheet-beam structures, Applied Acoustics, 160: 107154, https://doi.org/10.1016/j.apacoust.2019.107154
19. Wang Z., Ren A., Zhang Q., Zahid A., Abbasi Q.H. (2023), Recognition of approximate motions of human based on micro-Doppler features, IEEE Sensors Journal, 23(11): 12388–12397, https://doi.org/10.1109/JSEN.2023.3267820
20. Wu Y., Luo M., Li S. (2022), Measurement and extraction of micro-Doppler feature of underwater rotating target echo, [in:] OCEANS 2022 – Chennai, pp. 1–5, https://doi.org/10.1109/OCEANSChennai45887.2022.9775247
21. Xu L. (2016), Study on Doppler parameter estimation of underwater acoustic signal and its application [in Chinese], Ph.D. Thesis, Northwestern Polytechnical University in Xi’an, China, https://doi.org/10.7666/d.D01304090
22. Yang Y., Fan J., Wang B. (2023), Research on scattering feature extraction of underwater moving cluster targets based on the highlight model, Archives of Acoustics, 48(2): 235–247, https://doi.org/10.24425/aoa.2023.145235
23. Zhang B., Hu Y., Wang H., Zhuang Z. (2018), Underwater source localization using TDOA and FDOA measurements with unknown propagation speed and sensor parameter errors, IEEE Access, 6: 36645–36661, https://doi.org/10.1109/ACCESS.2018.2852636
24. Zhang R., Wang Y., Yeh C., Lu X. (2023), Precession parameter estimation of warhead with fins based on micro-Doppler effect and radar network, IEEE Transactions on Aerospace and Electronic Systems, 59(1): 443–459, https://doi.org/10.1109/TAES.2022.3182635
25. Zhao Y., Su Y. (2023), Estimation of micro-Doppler parameters with combined null space pursuit methods for the identification of LSS UAVs, IEEE Transactions on Geoscience and Remote Sensing, 61: 1–11, https://doi.org/10.1109/TGRS.2023.3264643
