A convolutional neural network based approach to sea clutter suppression for small boat detection

Zhejiang University Press - Tập 21 - Trang 1504-1520 - 2020
Guan-qing Li1, Zhi-yong Song1, Qiang Fu1
1National Key Laboratory of Science and Technology on ATR, College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

Tóm tắt

Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios. In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm, to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface. Dual activation has two steps. First, we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer. Through this, we can obtain the class activation maps (CAMs), which correspond to the positive region of the sea clutter. Second, we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum. Then, we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps. In addition, we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy. Measurement on real datasets verified the effectiveness of the proposed method.

Tài liệu tham khảo

Adolfsson L, Rahm M, 2018. Machine Learning for Categorization of Small Boats and Sea Clutter. MS Thesis, Chalmers University of Technology, Göteborg, Sweden. Angelov A, Robertson A, Murray-Smith R, et al., 2018. Practical classification of different moving targets using automotive radar and deep neural networks. IET Radar Sonar Navig, 12(10):1082–1089. https://doi.org/10.1049/iet-rsn.2018.0103 Conte E, de Maio A, 2004. Mitigation techniques for non-Gaussian sea clutter. IEEE J Ocean Eng, 29(2):284–302.https://doi.org/10.1109/joe.2004.826901 Cui XD, Goel V, Kingsbury B, 2015. Data augmentation for deep neural network acoustic modeling. IEEE/ACM Trans Audio Speech Lang Process, 23(9):1469–1477. https://doi.org/10.1109/taslp.2015.2438544 Del-Rey-Maestre N, Jarabo-Amores MP, Mata-Moya D, 2018. Machine learning techniques for coherent CFAR detection based on statistical modeling of UHF passive ground clutter. IEEE J Sel Top Signal Process, 12(1):104–118. https://doi.org/10.1109/jstsp.2017.2780798 de Maio A, Foglia G, Conte E, 2005. CFAR behavior of adaptive detectors: an experimental analysis. IEEE Trans Aerosp Electron Syst, 41(1):233–251. https://doi.org/10.1109/taes.2005.1413759 Dong Y, 2012. Optimal coherent radar detection in a K-distributed clutter environment. IET Radar Sonar Navig, 6(5):283–292. https://doi.org/10.1049/iet-rsn.2011.0273 Farina A, Gini F, Greco MV, et al., 1997. High resolution sea clutter data: statistical analysis of recorded live data. IEE Proc Radar Sonar Navig, 144(3):121–130. https://doi.org/10.1049/ip-rsn:19971107 Fernández JRM, Vidal JDLCB, 2018. Fast selection of the sea clutter preferential distribution with neural networks. Eng Appl Artif Intell, 70:123–129. https://doi.org/10.1016/j.engappai.2018.01.008 Gilbert AC, Indyk P, Iwen M, et al., 2014. Recent developments in the sparse Fourier transform: a compressed Fourier transform for big data. IEEE Signal Process Mag, 31(5):91–100. https://doi.org/10.1109/msp.2014.2329131 Gini F, Greco MV, Diani M, et al., 2000. Performance analysis of two adaptive radar detectors against non-Gaussian real sea clutter data. IEEE Trans Aerosp Electron Syst, 36(4): 1429–1439. https://doi.org/10.1109/7.892695 Gini F, Farina A, Montanari M, 2002. Vector subspace detection in compound-Gaussian clutter. Part II: performance analysis. IEEE Trans Aerosp Electron Syst, 38(4):1312–1323. https://doi.org/10.1109/taes.2002.1145752 Greco M, Gini F, Rangaswamy M, 2006. Statistical analysis of measured Polarimetric clutter data at different range resolutions. IEE Proc Radar Sonar Navig, 153(6):473–481. https://doi.org/10.1049/ip-rsn:20060045 Greco M, Stinco P, Gini F, 2010. Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance. IEEE Trans Aerosp Electron Syst, 46(3):1502–1513. https://doi.org/10.1109/taes.2010.5545205 Guan J, Chen XL, Huang Y, et al., 2012. Adaptive fractional Fourier transform-based detection algorithm for moving target in heavy sea clutter. IET Radar Sonar Navig, 6(5): 389–401. https://doi.org/10.1049/iet-rsn.2011.0030 Guo Q, Yu X, Ruan GQ, 2019. LPI radar waveform recognition based on deep convolutional neural network transfer learning. Symmetry, 11(4):540. https://doi.org/10.3390/sym11040540 Hao CP, Orlando D, Foglia G, et al., 2014. Persymmetric adaptive detection of distributed targets in partially-homogeneous environment. Dig Signal Process, 24:42–51. https://doi.org/10.1016/j.dsp.2013.10.007 Hassanien H, Indyk P, Katabi D, et al., 2012. Simple and practical algorithm for sparse Fourier transform. Proc 23rd Annual ACM-SIAM Symp on Discrete Algorithms, p.17–19. https://doi.org/10.11371/9781611973099.93 Herselman PL, de Wind HJ, 2008. Improved covariance matrix estimation in spectrally inhomogeneous sea clutter with application to adaptive small boat detection. Proc IEEE Int Confon Radar, p.26–30. https://doi.org/10.1109/radar.2008.4653898 Herselman PL, Baker CJ, de Wind HJ, 2008. An analysis of X-band calibrated sea clutter and small boat reflectivity at medium-to-low grazing angles. Int J Navig Observ, 2008: 347518. https://doi.org/10.1155/2008/347518 Jafarzadehpour F, Molahosseini MS, Zarandi AAE, et al., 2019. Efficient modular adder designs based on thermometer and one-hot coding. IEEE Trans VLSI Syst, 27(9):2142–2155. https://doi.org/10.1109/tvlsi.2019.2919609 Jay E, Ovarlez JP, Declercq D, et al., 2002. Bayesian optimum radar detector in non-Gaussian noise. Proc 26th Int Conf on Acoustics, p.13–17. https://doi.org/10.1109/ICASSP.2002.5744038 Khan A, Sohail A, Zahoora U, et al., 2019. A survey of the recent architectures of deep convolutional neural networks. https://arxiv.org/abs/1901.06032 Kong SH, Kim M, Hoang LM, et al., 2018. Automatic LPI radar waveform recognition using CNN. IEEE Access, 6:4207–4219. https://doi.org/10.1109/access.2017.2788942 Lamont-Smith T, 2008. Azimuth dependence of Doppler spectra of sea clutter at low grazing angle. IET Radar Sonar Navig, 2(2):97–103. https://doi.org/10.1049/iet-rsn:20070099 Lei YM, Tian YK, Shan HM, et al., 2020. Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping. Med Image Anal, 60:101628. https://doi.org/10.1016/j.media.2019.101628 Li Y, He MK, Zhang N, 2017. An ionospheric clutter recognition method based on machine learning. Proc IEEE Int Symp on Antennas and Propagation & USNC/URSI National Radio Science Meeting, p.9–14. https://doi.org/10.1109/apusncursinrsm.2017.8072861 Li YZ, Xie PC, Tang ZS, et al., 2019. SVM-based sea-surface small target detection: a false-alarm-rate-controllable approach. IEEE Geosci Remote Sens Lett, 16(8):1225–1229. https://doi.org/10.1109/lgrs.2019.2894385 Liu C, Wang J, Liu XM, et al., 2019. Deep CM-CNN for spectrum sensing in cognitive radio. IEEE J Sel Areas Commun, 37(10):2306–2321. https://doi.org/10.1109/jsac.2019.2933892 Liu J, Zhang ZJ, Yang Y, 2012. Performance enhancement of subspace detection with a diversely polarized antenna. IEEE Signal Process Lett, 19(1):4–7. https://doi.org/10.1109/lsp.2011.2173485 Liu NB, Xu YN, Ding H, et al., 2019. High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network. Comput Commun, 147:76–84. https://doi.org/10.1016/j.comcom.2019.08.016 Liu S, Huang WM, Zhang Z, 2020. Person re-identification using hybrid task convolutional neural network in camera sensor networks. Ad Hoc Netw, 97:102018. https://doi.org/10.1016/j.adhoc.2019.102018 Long J, Shelhamer E, Darreil T, 2015. Fully convolutional networks for semantic segmentation. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.8–10. https://doi.org/10.1109/cvpr.2015.7298965 Lv MJ, Zhou C, 2019. Study on sea clutter suppression methods based on a realistic radar dataset. Remote Sens, 11(23):2721. https://doi.org/10.3390/rs11232721 Ma LW, Wu JJ, Zhang JP, et al., 2020. Research on sea clutter reflectivity using deep learning model in Industry 4.0. IEEE Trans Ind Inform, 16(9):5929–5937. https://doi.org/10.1109/tii.2019.2957379 Mahdi A, Qin J, 2019. An extensive evaluation of deep featuresof convolutional neural networks for saliency prediction of human visual attention. J Vis Commun Image Represent, 65:102662. https://doi.org/10.1016/j.jvcir.2019.102662 McDonald AM, de Wind HJ, Cilliers JE, 2010. Performance prediction for a coherent X-band radar in a maritime environment with K-distributed sea clutter. Proc IEEE Int Confon Radar, p.1208–1213. https://doi.org/10.1109/radar.2010.5494436 McDonald MK, Cerutti-Maori D, 2016. Coherent radar processing in sea clutter environments, part 2: adaptive normalised matched filter versus adaptive matched filter performance. IEEE Trans Aerosp Electron Syst, 52(4): 1818–1833. https://doi.org/10.1109/taes.2016.140898 Pang CS, Liu SH, Han Y, 2018. High-speed target detection algorithm based on sparse Fourier transform. IEEE Access, 6:37828–37836. https://doi.org/10.1109/access.2018.2853180 Ritchie M, Stove A, Woodbridge K, et al., 2016. NetRAD: monostatic and bistatic sea clutter texture and Doppler spectra characterization at S-band. IEEE Trans Geosci Remote Sens, 54(9):5533–5543. https://doi.org/10.1109/tgrs.2016.2567598 Rosenberg L, Watts S, Greco MS, 2019. Modeling the statistics of microwave radar sea clutter. IEEE Aerosp Electron Syst Mag, 34(10):44–75. https://doi.org/10.1109/maes.2019.2901562 Sangston KJ, Gini F, Greco MS, 2012. Coherent radar target detection in heavy-tailed compound-Gaussian clutter. IEEE Trans Aerosp Electron Syst, 48(1):64–77. https://doi.org/10.1109/taes.2012.6129621 Sekine M, Musha T, Tomita Y, et al., 1983. Weibull-distributed sea clutter. IEE Proc F Commun Radar Signal Process, 130(5):476. https://doi.org/10.1049/ip-f-1.1983.0076 Shi SN, Liang X, Shui PL, et al., 2019. Low-velocity small target detection with Doppler-guided retrospective filter in high-resolution radar at fast scan mode. IEEE Trans Geosci Remote Sens, 57(11):8937–8953. https://doi.org/10.1109/tgrs.2019.2923790 Shnidman DA, 1999. Generalized radar clutter model. IEEE Trans Aerosp Electron Syst, 35(3):857–865. https://doi.org/10.1109/7.784056 Shui PL, Liu M, 2016. Subband adaptive GLRT-LTD for weak moving targets in sea clutter. IEEE Trans Aerosp Electron Syst, 52(1):423–437. https://doi.org/10.1109/taes.2015.140783 Shui PL, Shi YL, 2012. Subband ANMF detection of moving targets in sea clutter. IEEE Trans Aerosp Electron Syst, 48(4):3578–3593. https://doi.org/10.1109/taes.2012.6324742 Su NY, Chen XL, Guan J, et al., 2019. Deep CNN-based radar detection for real maritime target under different sea states and polarizations. Proc 4 Int Conf on Cognitive Systems and Signal Processing, p.321–331. https://doi.org/10.1007/978-981-13-7986-4_29 Trunk GV, George SF, 1970. Detection of targets in non-Gaussian sea clutter. IEEE Trans Aerosp Electron Syst, ASE-6(5):620–628. https://doi.org/10.1109/taes.1970.310062 Walker D, 2000. Experimentally motivated model for low grazing angle radar Doppler spectra of the sea clutter at small grazing angles. IEE Proc Radar Sonar Navig, 147(3):114–120. https://doi.org/10.1049/ip-rsn:20000386 Walker D, 2001. Doppler modelling of radar sea clutter. IEE Proc Radar Sonar Navig, 148(2):73–80. https://doi.org/10.1049/ip-rsn:20010182 Wang C, Wang J, Zhang XD, 2017. Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.5–9. https://doi.org/10.1109/icassp.2017.7952594 Wang L, Tang J, Liao QM, 2019. A study on radar target detection based on deep neural networks. IEEE Sens Lett, 3(3):7000504. https://doi.org/10.1109/lsens.2019.2896072 Wang SG, Patel VM, Petropulu A, 2016. RSFT: a realistic high dimensional sparse Fourier transform and its application in radar signal processing. Proc IEEE Military Communications Conf, p.1–3. https://doi.org/10.1109/milcom.2016.7795442 Wang WP, Feng Y, Shan T, 2019. A sea clutter suppression method using improved time-frequency filtering method. J Signal Process, 35(2):208–216 (in Chinese). https://doi.org/10.16798/j.issn.1003-0530.2019.02.006 Ward KD, 1981. Compound representation of high resolution sea clutter. Electr Lett, 17(16):561–563. https://doi.org/10.1049/el:19810394 Watts S, 1996. Cell-averaging CFAR gain in spatially correlated K-distributed clutter. IET Radar Sonar Navig, 143(5):321–327. https://doi.org/10.1049/ip-rsn:19960745 Watts S, Ward KD, 1987. Spatial correlation in K-distributed sea clutter. IEE Proc F Commun Radar Signal Process, 134(6):526–532. https://doi.org/10.1049/ip-f-1.1987.0090 Weinberg GV, 2012. Suboptimal coherent radar detection in a KK-distributed clutter environment. Signal Process, 2012: 614653. https://doi.org/10.5402/2012/614653 Wu J, Wang T, Meng X, et al., 2010. Clutter suppression for airborne non-sidelooking radar using ERCB-STAP algorithm. IET Radar Sonar Navig, 4(4):497–506. https://doi.org/10.1049/iet-rsn.2009.0121 Yang H, Min K, 2019. A deep approach for classifying artistic media from artworks. KSII Trans Int Inform Syst, 13(5): 2558–2573. https://doi.org/10.3837/tiis.2019.05.018 Yasotharan A, Thayaparan T, 2006. Time-frequency method for detecting an accelerating target in sea clutter. IEEE Trans Aeros Electron Syst, 42(4):1289–1310. https://doi.org/10.1109/taes.2006.314573 Yu XH, Chen XL, Huang Y, et al., 2019. Radar moving target detection in clutter background via adaptive dual-threshold sparse Fourier transform. IEEE Access, 7:58200–58211. https://doi.org/10.1109/access.2019.2914232 Zhang L, You W, Wu QMJ, et al., 2018. Deep learning-based automatic clutter/interference detection for HFSWR. Remote Sens, 10(10): 1517. https://doi.org/10.3390/rs10101517 Zhang RY, Cao SY, 2019. Real-time human motion behavior detection via CNN using mmWave radar. IEEE Sens Lett, 3(2):3500104. https://doi.org/10.1109/lsens.2018.2889060 Zhao JF, Jiang RK, Wang XT, et al., 2019. Robust CFAR detection for multiple targets in K-distributed sea clutter based on machine learning. Symmetry, 11(12):1482. https://doi.org/10.3390/sym11121482 Zhao JR, Wen BY, Tian YW, et al., 2019. Sea clutter suppression for shipborne HF radar using cross-loop/monopole array. IEEE Geosci Remote Sens Lett, 16(6): 879–893. https://doi.org/10.1109/lgrs.2018.2884507 Zhou BL, Khosla A, Lapedriza A, et al., 2016. Learning deep features for discriminative localization. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2921–2929. https://doi.org/10.1109/cvpr.2016.319