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Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition

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Abstract

In recent years, the performances of radar resolution, coverage, and detection accuracy have been significantly improved through the use of ultra-wideband, synthetic aperture and digital signal processing technologies. High-resolution radars (HRRs) utilize wideband signals and synthetic apertures to enhance the range and angular resolutions of tracking, respectively. They also generate one-, two-, and even threedimensional high-resolution images containing the feature information of targets, from which the targets can be precisely classified and identified. Advanced signal processing algorithms in HRRs obtain important information such as range-Doppler imaging, phase-derived ranging, and micro-motion features. However, the advantages and applications of HRRs are restricted by factors such as the reduced signal-to-noise ratio (SNR) of multi-scatter point targets, decreased tracking accuracy of multi-scatter point targets, high demands of motion compensation, and low sensitivity of the target attitude. Focusing on these problems, this paper systematically introduces the novel technologies of HRRs and discusses the issues and solutions relevant to detection, tracking, imaging, and recognition. Finally, it reviews the latest progress and representative results of HRR-based research, and suggests the future development of HRRs.

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References

  1. Fabrizio G A. High Frequency Over-the-Horizon Radar: Fundamental Principles, Signal Processing, and Practical Applications. New York: McGraw-Hill, 2013

    Google Scholar 

  2. van Trees H L. Detection, Estimation, and Modulation Theory, Part IV: Optimum Array Processing. Hoboken: Wiley & Sons, 2002

    Google Scholar 

  3. Farina A. Antenna-Based Signal Processing Techniques for Radar Systems. Norwood: Artech House, 1992

    Google Scholar 

  4. Fenn A J, Temme D H, Delaney W P, et al. The development of phased-array radar technology. Lincoln Lab J, 2000, 12: 321–340

    Google Scholar 

  5. Brookner E. Phased arrays around the world-progress and future trends. In: Proceedings of IEEE International Symposium on Phased Array Systems and Technology, 2003

    Google Scholar 

  6. Wang D C. Discussion on the theoretical foundation of development and innovation for radar technical system. Modern Radar, 2014, 26: 142–148

    Google Scholar 

  7. North D O. An analysis of the factors which determine signal/noise discrimination in pulsed-carrier systems. Proc IEEE, 1963, 51: 1016–1027

    Article  Google Scholar 

  8. Woodward P M. Probability and Information Theory with Application to Radar. London: Pergamon Press, 1953

    MATH  Google Scholar 

  9. Dicke R H. Object detection system. US Patent, 2624876, 1953

    Google Scholar 

  10. Shirman Y D, Leshchenko S P, Orlenko V M. Advantages and problems of wideband radar. In: Proceedings of IEEE International Radar Conference, Portland, 2003. 15–21

    Google Scholar 

  11. Wehner D R. High Resolution Radar. Norwood: Artech House, 1995

    Google Scholar 

  12. Eaves J L, Reedy E K. Principles of Modern Radar. New York: Van Nostrand Reinhold, 2010

    Google Scholar 

  13. Le Chevalier F. Principles of Radar and Sonar Signal Processing. Norwood: Artech House, 2002

    Google Scholar 

  14. Long T, Liu Q H, Chen X L. Wideband Radar. Beijing: National Defense Industry Press, 2017

    Google Scholar 

  15. Lindsay J E. Angular glint and the moving, rotating, complex radar target. IEEE Trans Aerosp Electron Syst, 1968, 4: 164–173

    Article  Google Scholar 

  16. Barton D K. Radar system analysis and modeling. IEEE Aerosp Electron Syst Mag, 2005, 20: 23–25

    Article  Google Scholar 

  17. Skolnik M I. Radar Handbook. 3rd ed. New York: McGraw-Hill, 2008

    Google Scholar 

  18. Brookner E. Aspects of Modern Radar. Norwood: Artech House, 1988

    Google Scholar 

  19. Li N J, Zhang Y T. A survey of radar ECM and ECCM. IEEE Trans Aerosp Electron Syst, 1995, 31: 1110–1120

    Article  Google Scholar 

  20. Greco M, Gini F, Farina A. Radar detection and classification of jamming signals belonging to a cone class. IEEE Trans Signal Process, 2008, 56: 1984–1993

    Article  MathSciNet  MATH  Google Scholar 

  21. Howard D. High range-resolution monopulse tracking radar. IEEE Trans Aerosp Electron Syst, 1975, 11: 749–755

    Article  Google Scholar 

  22. Rycroft M J. Book review: understanding synthetic aperture radar images. J Atmos Sol-Terr Phys, 1999, 61: 424

    Article  Google Scholar 

  23. Zeng T, Liu T D, Ding Z G, et al. A novel DEM reconstruction strategy based on multi-frequency InSAR in highly sloped terrain. Sci China Inf Sci, 2017, 60: 088301

    Article  Google Scholar 

  24. Li Y C, Jin Y Q. Target decomposition and recognition from wide-angle SAR imaging based on a Gaussian amplitudephase model. Sci China Inf Sci, 2017, 60: 062305

    Article  Google Scholar 

  25. Hu C, Li Y H, Dong X C, et al. Optimal 3D deformation measuring in inclined geosynchronous orbit SAR differential interferometry. Sci China Inf Sci, 2017, 60: 060303

    Article  Google Scholar 

  26. Fuster R M, Usón M F, Ibars A B. Interferometric orbit determination for geostationary satellites. Sci China Inf Sci, 2017, 60: 060302

    Article  Google Scholar 

  27. Zheng W J, Hu J, Zhang W, et al. Potential of geosynchronous SAR interferometric measurements in estimating three-dimensional surface displacements. Sci China Inf Sci, 2017, 60: 060304

    Article  Google Scholar 

  28. Yin W, Ding Z G, Lu X J, et al. Beam scan mode analysis and design for geosynchronous SAR. Sci China Inf Sci, 2017, 60: 060306

    Article  Google Scholar 

  29. Ding Z G, Xiao F, Xie Y Z, et al. A modified fixed-point chirp scaling algorithm based on updating phase factors regionally for spaceborne SAR real-time imaging. IEEE Trans Geosci Remote Sens, 2018, 56: 7436–7451

    Article  Google Scholar 

  30. Steudel F. An improved process for phase-derived-range measurements. World Intellectual Property Organization, WO 2005/017553 A1, 2005

    Google Scholar 

  31. Steudel F. Process for phase-derived range measurements. US Patent, WO 2005/030222 A1, 2005

    Google Scholar 

  32. Song C, Wu Y R, Zhou L J, et al. A multicomponent micro-Doppler signal decomposition and parameter estimation method for target recognition. Sci China Inf Sci, 2019, 62: 029304

    Article  MathSciNet  Google Scholar 

  33. Li K, Liang X J, Zhang Q, et al. Micro-Doppler signature extraction and ISAR imaging for target with micromotion dynamics. IEEE Geosci Remote Sens Lett, 2011, 8: 411–415

    Article  Google Scholar 

  34. Liu Y X, Zhu D K, Li X, et al. Micromotion characteristic acquisition based on wideband radar phase. IEEE Trans Geosci Remote Sens, 2014, 52: 3650–3657

    Article  Google Scholar 

  35. Guo L Y, Fan H Y, Liu Q H, et al. Analysis of micro-motion feature in ISAR imaging via phase-derived velocity measurement technique. In: Proceedings of IEEE Radar Conference, Seattle, 2017. 0783–0787

    Google Scholar 

  36. Roth K R, Austin M E, Frediani D J, et al. The Kiernan reentry measurements system on Kwajalein Atoll. Lincoln Lab J, 1989, 2: 247–276

    Google Scholar 

  37. Xia X W, Jing W X, Li C Y. Assessment on GMD’s deployment status and operational capability. Mod Defence Technol, 2008, 36: 11–18

    Google Scholar 

  38. Zhou W X. Development and prospect of ISAR imaging system and imaging technique. Mod Radar, 2012, 34: 1–7

    Google Scholar 

  39. Gerlach K. Spatially distributed target detection in non-Gaussian clutter. IEEE Trans Aerosp Electron Syst, 1999, 35: 926–934

    Article  Google Scholar 

  40. He Y, Gu X F, Jian T, et al. A M out of N detector based on scattering density. In: Proceedings of IET International Radar Conference, Guilin, 2009

    Google Scholar 

  41. Chen X L, Wang L, Liu S L. Research on extended target detection for high resolution radar (in Chinese). Sci Sin Inform, 2012, 42: 1007–1018

    Article  Google Scholar 

  42. Conte E, de Maio A, Ricci G. GLRT-based adaptive detection algorithms for range-spread targets. IEEE Trans Signal Process, 2001, 49: 1336–1348

    Article  Google Scholar 

  43. Conte E, de Maio A. Distributed target detection in compound-Gaussian noise with Rao and Wald tests. IEEE Trans Aerosp Electron Syst, 2003, 39: 568–582

    Article  Google Scholar 

  44. Bandiera F, de Maio A, Greco A S, et al. Adaptive radar detection of distributed targets in homogeneous and partially homogeneous noise plus subspace interference. IEEE Trans Signal Process, 2007, 55: 1223–1237

    Article  MathSciNet  MATH  Google Scholar 

  45. He Y, Jian T, Su F G, et al. Novel range-spread target detectors in non-Gaussian clutter. IEEE Trans Aerosp Electron Syst, 2010, 46: 1312–1328

    Article  Google Scholar 

  46. Dai F Z, Liu H W, Shui P L, et al. Adaptive detection of wideband radar range spread targets with range walking in clutter. IEEE Trans Aerosp Electron Syst, 2012, 48: 2052–2064

    Article  Google Scholar 

  47. Jian T, He Y, Su F, et al. Cascaded detector for range-spread target in non-Gaussian clutter. IEEE Trans Aerosp Electron Syst, 2012, 48: 1713–1725

    Article  Google Scholar 

  48. Aubry A, de Maio A, Pallotta L, et al. Radar detection of distributed targets in homogeneous interference whose inverse covariance structure is defined via unitary invariant functions. IEEE Trans Signal Process, 2013, 61: 4949–4961

    Article  MathSciNet  MATH  Google Scholar 

  49. Ciuonzo D, de Maio A, Orlando D. A unifying framework for adaptive radar detection in homogeneous plus structured interference–part II: detectors design. IEEE Trans Signal Process, 2016, 64: 2907–2919

    Article  MathSciNet  MATH  Google Scholar 

  50. Shui P L, Liu H W, Bao Z. Range-spread target detection based on cross time-frequency distribution features of two adjacent received signals. IEEE Trans Signal Process, 2009, 57: 3733–3745

    Article  MathSciNet  MATH  Google Scholar 

  51. Xu S W, Shui P L, Yan X Y. CFAR detection of range-spread target in white Gaussian noise using waveform entropy. Electron Lett, 2010, 46: 647–649

    Article  Google Scholar 

  52. Xu S W, Shui P L. Range-spread target detection in white Gaussian noise via two-dimensional non-linear shrinkage map and geometric average integration. IET Radar Sonar Navig, 2012, 6: 90–98

    Article  Google Scholar 

  53. Shui P L, Xu S W, Liu H W. Range-spread target detection using consecutive HRRPs. IEEE Trans Aerosp Electron Syst, 2011, 47: 647–665

    Article  Google Scholar 

  54. Zuo L, Li M, Zhang X W, et al. CFAR detection of range-spread targets based on the time-frequency decomposition feature of two adjacent returned signals. IEEE Trans Signal Process, 2013, 61: 6307–6319

    Article  MathSciNet  MATH  Google Scholar 

  55. Long T, Zheng L, Li Y, et al. Improved double threshold detector for spatially distributed target. IEICE Trans Commun, 2012, 95: 1475–1478

    Article  Google Scholar 

  56. Luo Y, Zhang Q, Qiu C W, et al. Micro-Doppler effect analysis and feature extraction in ISAR imaging with stepped-frequency chirp signals. IEEE Trans Geosci Remote Sens, 2010, 48: 2087–2098

    Article  Google Scholar 

  57. Zhu D K, Liu Y X, Huo K, et al. A novel high-precision phase-derived-range method for direct sampling LFM radar. IEEE Trans Geosci Remote Sens, 2016, 54: 1131–1141

    Article  Google Scholar 

  58. Fan H Y, Ren L X, Long T, et al. A high-precision phase-derived range and velocity measurement method based on synthetic wideband pulse Doppler radar. Sci China Inf Sci, 2017, 60: 082301

    Article  Google Scholar 

  59. Fan H Y, Ren L X, Mao E K, et al. A high-precision method of phase-derived velocity measurement and its application in motion compensation of ISAR imaging. IEEE Trans Geosci Remote Sens, 2018, 56: 60–77

    Article  Google Scholar 

  60. Fan H Y, Ren L X, Mao E K. A micro-motion measurement method based on wideband radar phase derived ranging. In: Proceedings of IET International Radar Conference, Xi’an, 2013

    Google Scholar 

  61. Guo L Y, Fan H Y, Liu Q H, et al. A novel high-accuracy phase-derived velocity measurement method for wideband LFM radar. IEEE Geosci Remote Sens Lett, 2018. doi: 10.1109/LGRS.2018.2879491

    Google Scholar 

  62. Blackman S S. Multiple hypothesis tracking for multiple target tracking. IEEE Aerosp Electron Syst Mag, 2004, 19: 5–18

    Article  Google Scholar 

  63. Aslan M S, Saranli A. A tracker-aware detector threshold optimization formulation for tracking maneuvering targets in clutter. Signal Process, 2011, 91: 2213–2221

    Article  MATH  Google Scholar 

  64. Barshalom Y, Daum F, Huang J. The probabilistic data association filter. IEEE Control Syst, 2009, 29: 82–100

    Article  Google Scholar 

  65. Boers Y, Driessen H. Results on the modified Riccati equation: target tracking applications. IEEE Trans Aerosp Electron Syst, 2006, 42: 379–384

    Article  Google Scholar 

  66. Brekke E F, Hallingstad O, Glattetre J. The modified Riccati equation for amplitude-aided target tracking in heavytailed clutter. IEEE Trans Aerosp Electron Syst, 2011, 47: 2874–2886

    Article  Google Scholar 

  67. Li X R, Bar-Shalom Y. Stability evaluation and track life of the PDAF for tracking in clutter. IEEE Trans Autom Control, 1991, 36: 588–602

    Article  Google Scholar 

  68. Zhang X, Willett P, Bar-Shalom Y. Dynamic cramer-rao bound for target tracking in clutter. IEEE Trans Aerosp Electron Syst, 2005, 41: 1154–1167

    Article  Google Scholar 

  69. Bar-Shalom Y, Zhang X, Willett P. Simplification of the dynamic Cram´er-Rao bound for target tracking in clutter. IEEE Trans Aerosp Electron Syst, 2011, 47: 1481–1482

    Article  Google Scholar 

  70. Aslan M S, Saranli A, Baykal B. Tracker-aware adaptive detection: an efficient closed-form solution for the Neyman- Pearson case. Digit Signal Process, 2010, 20: 1468–1481

    Article  Google Scholar 

  71. Qin Y L, Wang H Q, Wang J T, et al. Dynamic waveform selection for manoeuvering target tracking in clutter. IET Radar Sonar Nav, 2013, 7: 815–825

    Article  Google Scholar 

  72. Jin B, Su T, Zhang W, et al. Joint optimization of predictive model and transmitted waveform for extended target tracking. In: Proceedings of International Conference on Signal Processing, Hangzhou, 2014. 1914–1918

    Google Scholar 

  73. Cabrera J B. Tracker-based adaptive schemes for optimal waveform selection. In: Proceedings of IEEE Radar Conference, Cincinnati, 2014. 0298–0302

    Google Scholar 

  74. Kyriakides I, Morrell D, Papandreou-Suppappola A. Adaptive highly localized waveform design for multiple target tracking. EURASIP J Adv Signal Process, 2012, 2012: 180

    Article  Google Scholar 

  75. Nguyen N H, Dogancay K, Davis L M, et al. Joint transmitter waveform and receiver path optimization for target tracking by multistatic radar system. In: Proceedings of IEEE Signal Processing Workshop on Statistical Signal Processing, Gold Coast, 2014. 444–447

    Google Scholar 

  76. Willett P, Niu R X, Barshalom Y. A modified PDAF based on a Bayesian detector. In: Proceedings of American Control Conference, Chicago, 2000. 2230–2234

    Google Scholar 

  77. Willett P, Niu R, Bar-Shalom Y. Integration of Bayes detection with target tracking. IEEE Trans Signal Process, 2001, 49: 17–29

    Article  Google Scholar 

  78. Aslan M S, Saranli A. Threshold optimization for tracking a nonmaneuvering target. IEEE Trans Aerosp Electron Syst, 2011, 47: 2844–2859

    Article  Google Scholar 

  79. Zeng T, Zheng L, Li Y, et al. Offline performance prediction of PDAF with Bayesian detection for tracking in clutter. IEEE Trans Signal Process, 2013, 61: 770–781

    Article  MathSciNet  MATH  Google Scholar 

  80. Zheng L, Zeng T, Liu Q H, et al. Optimization and analysis of PDAF with Bayesian detection. IEEE Trans Aerosp Electron Syst, 2016, 52: 1986–1995

    Article  Google Scholar 

  81. Koch J W. Bayesian approach to extended object and cluster tracking using random matrices. IEEE Trans Aerosp Electron Syst, 2008, 44: 1042–1059

    Article  Google Scholar 

  82. Salmond D J. Mixture reduction algorithms for point and extended object tracking in clutter. IEEE Trans Aerosp Electron Syst, 2009, 45: 667–686

    Article  Google Scholar 

  83. Ceylan S, Efe M. Performance of PMHT based algorithms for underwater target tracking. In: Proceedings of Signal Processing and Communications Applications Conference, Antalya, 2009. 89–92

    Google Scholar 

  84. Streit R L, Luginbuhl T E. A probabilistic multi-hypothesis tracking algorithm without enumeration and pruning. In: Proceedings of the 6th Joint Service Data Fusion Symposium, 1993. 1015–1024

    Google Scholar 

  85. Drummond O E. Integration of features and attributes into target tracking. In: Proceedings of International Society for Optical Engineering, 2000. 610–622

    Google Scholar 

  86. Drummond O E. On categorical feature-aided target tracking. In: Proceedings of International Society for Optical Engineering, 2004. 544–558

    Google Scholar 

  87. Wieneke M, Koch W. Probabilistic tracking of multiple extended targets using random matrices. In: Proceedings of International Society for Optical Engineering, 2010

    Google Scholar 

  88. Willett P, Coraluppi S. MLPDA and MLPMHT applied to some MSTWG data. In: Proceedings of International Conference on Information Fusion, 2006

    Google Scholar 

  89. Georgescu R, Willett P. Predetection fusion with Doppler measurements and amplitude information. IEEE J Ocean Eng, 2012, 37: 56–65

    Article  Google Scholar 

  90. Challa S, Pulford G W. Joint target tracking and classification using radar and ESM sensors. IEEE Trans Aerosp Electron Syst, 2001, 37: 1039–1055

    Article  Google Scholar 

  91. Lu Q, Domrese K, Willett P, et al. A bootstrapped PMHT with feature measurements. IEEE Trans Aerosp Electron Syst, 2017, 53: 2559–2571

    Article  Google Scholar 

  92. Feldmann M, Franken D. Tracking of extended objects and group targets using random matrices: a new approach. In: Proceedings of International Conference on Information Fusion, 2008

    Google Scholar 

  93. Feldmann M, Franken D, Koch W. Tracking of extended objects and group targets using random matrices. IEEE Trans Signal Process, 2011, 59: 1409–1420

    Article  Google Scholar 

  94. Lan J, Li X R. Tracking of extended object or target group using random matrix–part II: irregular object. In: Proceedings of International Conference on Information Fusion, 2012. 2185–2192

    Google Scholar 

  95. Davey S, Gray D, Streit R. Tracking, association, and classification: a combined PMHT approach. Digit Signal Process, 2002, 12: 372–382

    Article  Google Scholar 

  96. Davey S, Gray D. Integrated track maintenance for the PMHT via the hysteresis model. IEEE Trans Aerosp Electron Syst, 2007, 43: 93–111

    Article  Google Scholar 

  97. Long T, Zheng L, Chen X L, et al. Improved probabilistic multi-hypothesis tracker for multiple target tracking with switching attribute states. IEEE Trans Signal Process, 2011, 59: 5721–5733

    Article  MathSciNet  MATH  Google Scholar 

  98. Lu G Y, Bao Z. Compensation of scatterer migration through resolution cell in inverse synthetic aperture radar imaging. IEE Proc Radar Sonar Navig, 2000, 147: 80–85

    Article  Google Scholar 

  99. Chen V C, Martorella M. Inverse Synthetic Aperture Radar Imaging: Principles, Algorithms and Applications. Edison: SciTech Publishing, 2014

    Book  Google Scholar 

  100. Fan L, Shi S, Liu Y, et al. A novel range-instantaneous-Doppler isar imaging algorithm for maneuvering targets via adaptive Doppler spectrum extraction. Prog Electrom Res C, 2015, 56: 109–118

    Article  Google Scholar 

  101. Du L, Su G. Adaptive inverse synthetic aperture radar imaging for nonuniformly moving targets. IEEE Geosci Remote Sens Lett, 2005, 2: 247–249

    Article  Google Scholar 

  102. Xing M, Wu R, Li Y, et al. New ISAR imaging algorithm based on modified Wigner-Ville distribution. IET Radar Sonar Navig, 2009, 3: 70–80

    Article  Google Scholar 

  103. Tao R, Zhang N, Wang Y. Analysing and compensating the effects of range and Doppler frequency migrations in linear frequency modulation pulse compression radar. IET Radar Sonar Navig, 2011, 5: 12–22

    Article  Google Scholar 

  104. Thayaparan T, Brinkman W, Lampropoulos G. Inverse synthetic aperture radar image focusing using fast adaptive joint time-frequency and three-dimensional motion detection on experimental radar data. IET Signal Process, 2010, 4: 382

    Article  Google Scholar 

  105. Brinkman W, Thayaparan T. Focusing inverse synthetic aperture radar images with higher-order motion error using the adaptive joint-time-frequency algorithm optimised with the genetic algorithm and the particle swarm optimisation algorithm–comparison and results. IET Signal Process, 2010, 4: 329–342

    Article  Google Scholar 

  106. Kang B S, Bae J H, Lee S J, et al. ISAR rotational motion compensation algorithm using polynomial phase transform. Microw Opt Technol Lett, 2016, 58: 1551–1557

    Article  Google Scholar 

  107. Kang B S, Kang M S, Choi I O, et al. Efficient autofocus chain for ISAR imaging of non-uniformly rotating target. IEEE Senss J, 2017, 17: 5466–5478

    Article  Google Scholar 

  108. Liu L, Qi M S, Zhou F. A novel non-uniform rotational motion estimation and compensation method for maneuvering targets ISAR imaging utilizing particle swarm optimization. IEEE Senss J, 2018, 18: 299–309

    Article  Google Scholar 

  109. Ye C M, Xu J, Peng Y N, et al. Improved Doppler centroid tracking for ISAR based on target extraction. In: Proceedings of IEEE Radar Conference, 2008

    Google Scholar 

  110. Pellizzari C J, Bos J, Spencer M F, et al. Performance characterization of phase gradient autofocus for inverse synthetic aperture LADAR. In: Proceedings of IEEE Aerospace Conference, Place, 2014

    Google Scholar 

  111. Zhou S, Xing M D, Xia X G, et al. An azimuth-dependent phase gradient autofocus (APGA) algorithm for airborne/ stationary BiSAR imagery. IEEE Geosci Remote Sens Lett, 2013, 10: 1290–1294

    Article  Google Scholar 

  112. Wang J F, Kasilingam D, Liu X Z, et al. ISAR minimum-entropy phase adjustment. In: Proceedings of IEEE Radar Conference, 2004. 197–200

    Google Scholar 

  113. Cao P, Xing M D, Sun G C, et al. Minimum entropy via subspace for ISAR autofocus. IEEE Geosci Remote Sens Lett, 2010, 7: 205–209

    Article  Google Scholar 

  114. Cai J J, Xu J, Wang G, et al. An effective ISAR autofocus algorithm based on single eigenvector. In: Proceedings of IEEE International Radar Conference, Guangzhou, 2016

    Google Scholar 

  115. Lee S H, Bae J H, Kang M S, et al. Efficient ISAR autofocus technique using eigenimages. IEEE J Sel Top Appl Earth Observations Remote Sens, 2017, 10: 605–616

    Article  Google Scholar 

  116. Xu J, Cai J J, Sun Y H, et al. Efficient ISAR phase autofocus based on eigenvalue decomposition. IEEE Geosci Remote Sens Lett, 2017, 14: 2195–2199

    Article  Google Scholar 

  117. Liu H W, Chen F, Du L, et al. Robust radar automatic target recognition algorithm based on HRRP signature. Front Electr Electron Eng China, 2012, 7: 49–55

    Article  Google Scholar 

  118. Jing C, Tao Z, Mian P, et al. Radar HRRP recognition based on discriminant deep autoencoders with small training data size. Electron Lett, 2016, 52: 1725–1727

    Article  Google Scholar 

  119. Feng B, Chen B, Liu H W. Radar HRRP target recognition with deep networks. Pattern Recogn, 2017, 61: 379–393

    Article  Google Scholar 

  120. Duan P P, Li H. The radar target recognition research based on improved neural network algorithm. In: Proceedings of International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), 2014. 1074–1077

    Google Scholar 

  121. Penacaballero C, Cantu E, Rodriguez J. Automatic target recognition of aircraft using inverse synthetic aperture radar. 2017. ArXiv:1711.04901

    Google Scholar 

  122. Jiang Y, Xu J, Peng S B, et al. Identification-while-scanning of a multi-aircraft formation based on sparse recovery for narrowband radar. Sensors, 2016, 16: 1972

    Article  Google Scholar 

  123. Pan M, Jiang J, Kong Q P, et al. Radar HRRP target recognition based on t-SNE segmentation and discriminant deep belief network. IEEE Geosci Remote Sens Lett, 2017, 14: 1609–1613

    Article  Google Scholar 

  124. Dai WL, Zhang G, Zhang Y. HRRP classification based on multi-scale fusion sparsity preserving projections. Electron Lett, 2017, 53: 748–750

    Article  Google Scholar 

  125. Du L, Wang P H, Liu H W, et al. Bayesian spatiotemporal multitask learning for radar HRRP target recognition. IEEE Trans Signal Process, 2011, 59: 3182–3196

    Article  MathSciNet  MATH  Google Scholar 

  126. Zhou D Y, Shen X F, YangWL. Radar target recognition based on fuzzy optimal transformation using high-resolution range profile. Pattern Recogn Lett, 2013, 34: 256–264

    Article  Google Scholar 

  127. Du L, Liu H W, Bao Z, et al. Radar automatic target recognition using complex high-resolution range profiles. IET Radar Sonar Navig, 2007, 1: 18–26

    Article  Google Scholar 

  128. Du L, Liu H W, Bao Z. Radar HRRP statistical recognition: parametric model and model selection. IEEE Trans Signal Process, 2008, 56: 1931–1944

    Article  MathSciNet  MATH  Google Scholar 

  129. Schölkopf B, Smola A J, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput, 1998, 10: 1299–1319

    Article  Google Scholar 

  130. Kim K T, Seo D K, Kim H T. Efficient radar target recognition using the MUSIC algorithm and invariant features. IEEE Trans Antennas Propagat, 2002, 50: 325–337

    Article  Google Scholar 

  131. Aldhubaib F, Shuley N V. Radar target recognition based on modified characteristic polarization states. IEEE Trans Aerosp Electron Syst, 2010, 46: 1921–1933

    Article  Google Scholar 

  132. Wang F Y, Guo R J, Huang Y H. Radar target recognition based on some invariant properties of the polarization scattering matrix. In: Proceedings of IEEE International Radar Conference, Place, 2011. 626–629

    Google Scholar 

  133. Li X, Lin L S, Shao X H, et al. A target polarization recognition method for radar echoes. In: Proceedings of International Conference on Microwave and Millimeter Wave Technology, Place, 2010. 1644–1647

    Google Scholar 

  134. Long T, Mao E K, He P K. Analysis and processing of modulated frequency stepped radar signal. Acta Electron Sin, 1998, 12: 84–88

    Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61771050) and 111 Project of the China Ministry of Education (MOE) (Grant No. B14010).

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  1. Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Teng Long, Zhennan Liang & Quanhua Liu

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Long, T., Liang, Z. & Liu, Q. Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition. Sci. China Inf. Sci. 62, 40301 (2019). https://doi.org/10.1007/s11432-018-9811-0

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