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SCIENCE CHINA Information Sciences, Volume 62, Issue 4: 040301(2019) https://doi.org/10.1007/s11432-018-9811-0

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

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  • ReceivedJan 12, 2019
  • AcceptedMar 4, 2019
  • PublishedMar 11, 2019

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 three-dimensional 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.


Acknowledgment

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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  • Figure 1

    (Color online) Flowchart of GLRT-DT detector.

  • Figure 2

    (Color online) Performance comparison of four detectors (integral, $M/N$, SDD-GLRT, and GLRT-DT).protect łinebreak (a) Model 1: sparse uniform distribution; (b) model 2: sparse nonuniform distribution; (c) model 3: dense uniform distribution; (d) model 4: dense nonuniform distribution.

  • Figure 3

    (Color online) Implementation of high-precision phase-derived range and velocity measurements.

  • Figure 4

    (Color online) Simulated accuracies of EVMs in different SNR environments. (a) SNR range: 13–35 dB;protect łinebreak (b) SNR range: 16–35 dB.

  • Figure 5

    (Color online) Accuracy of PDRMs. (a) SNR range: 13–35 dB; (b) SNR range: 16–35 dB.

  • Figure 6

    (Color online) Interrelationships among HRR characteristics.

  • Figure 7

    (Color online) Data associations in LRRs and HRRs.

  • Figure 8

    (Color online) Traditional detection and tracking.

  • Figure 9

    (Color online) Integrated detection and tracking algorithm based on Bayesian detection.

  • Figure 10

    (Color online) Schematic of the range-spread target-data association method.

  • Figure 11

    (Color online) Flowchart of the attribute-aided tracking method based on EM.

  • Figure 12

    (Color online) Flowchart of the proposed method.

  • Figure 13

    (Color online) Results of measured airplane data. (a) Envelope alignment by ACM; (b) first 80 eigenvalues of the covariance matrix; (c) DCT; (d) PGA; (e) M-SEA; (f) M-MESA.

  • Figure 14

    (Color online) Implementation process of radar automatic target recognition.

  • Figure 15

    (Color online) HRRP recognition using a convolution neural network.

  • Figure 16

    (Color online) Photograph of the X-band high-resolution monopulse precision-tracking test radar.

  • Figure 17

    (Color online) Real-time closed-loop tracking of a civil aircraft.

  • Figure 18

    (Color online) Analysis of the measured civil aircraft envelope velocities. (a) Velocity measurements; (b) velocity fluctuation errors.

  • Figure 19

    (Color online) Analysis of the phase-derived velocity measurements of the civil aircraft. (a) Velocity measurements; (b) velocity fluctuation errors.

  • Figure 20

    (Color online) ISAR imaging results of the civil aircraft.

  • Figure 21

    (Color online) FOD radar.

  • Figure 22

    (Color online) One-dimensional images captured by FOD radar on an airfield. (a) Aircraft; (b) vehicles;protect łinebreak (c) foreign objects.

  • Figure 23

    (Color online) Measured dual-polarized one-dimensional images of houses (a) and vehicles (b), and their polarization features (c).

  • Table 1   Scattering center of target
    Model number Name Scattering energy distribution
    Model 1 Sparse uniform distribution The target is composed of 5 scatterers, each containing 20% of the target energy.
    Model 2 Sparse nonuniform distribution The target is composed of 1 strong scatterer, 1 weak scatterer, and 3 weaker scatterers containing 70%, 20%, and 3.33% (each) of the target energy, respectively.
    Model 3 Dense uniform distribution The target is composed of 128 weak scatterers, each containing 0.78% of the target energy.
    Model 4 Dense nonuniform distribution The target is composed of 2 strong scatterers and 126 weak scatterers, each containing 20% and 0.48% of the target energy, respectively.
  • Table 2   Contrast comparison of different autofocus methods
    Method DCT PGA M-SEA M-MESA
    Contrast 10.5218 14.0038 23.9199 24.3422
  • Table 3   Typical HRRP features
    Feature categoryExample
    3*Waveform characteristics Scale characteristics Length, number of scattering points
    Fluctuation characteristics Mean, variance
    Structure characteristics Symmetry, wave, entropy descaling
    3*Transformation characteristics Spectral characteristics Spectrum, bispectrum, high order spectrum
    Multiscale feature Wavelet transform
    Subspace characteristics PCA, LLE
  • Table 4   Parameters of the X-band high-resolution monopulse precision-tracking test radar
    Parameter Value
    Frequency band X
    Signal bandwidth $1$ GHz
    Peak power 4 kW
    Antenna aperture 2.5 m
    Azimuth $0^\circ$–$360^\circ$
    Elevation $-3^\circ$–$180^\circ$

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