Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
Abstract
:1. Introduction
2. Principle of the Algorithm
- Extraction of a small data block from the RC radar data in time domain.
- Transformation of the data block to range-Doppler domain via azimuth fast Fourier transform (FFT).
- Normalization over Doppler for achieving a “flat” spectrum.
- Estimation of the ocean clutter statistics.
- Computation of a CFAR detection threshold based on the ocean clutter statistics.
- Clustering of multiple detections to a single “physical object”.
- Tracking of the clusters, i.e., of the cluster centroid positions.
2.1. Importance of Clutter Normalization
2.2. Algorithm Block Diagram
3. Training Data Selection
3.1. Target Pre-Detection
- RC radar data extraction in time domain (cf. green region in Figure 4).
- Incoherent summation over azimuth.
- Range-dependent adaptive threshold computation.
- Target peak detection and cancellation.
3.2. Clutter Normalization
3.3. Importance of Training Data Update
4. Clutter Statistics and Detection
4.1. K-Distribution
4.1.1. Method of Moments (MoM)
4.1.2. Non-Linear Least Squares Method (NLLSQ)
4.2. Chi Square Distribution
4.3. Tri-Modal Discrete (3MD) Texture Model
4.4. K-Rayleigh Distribution
5. Clustering and Tracking
6. Experimental Results and Discussion
6.1. Clutter Model Fitting and Performance Evaluation
6.1.1. Threshold Error
6.1.2. False Alarm Rate Error
6.2. Detection Results
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Range-Doppler Image | Target SCNR [dB] |
---|---|
Before normalization (Figure 16a) | 24.02 |
After normalization but without pre-detection (Figure 16c) | 14.20 |
After normalization with pre-detection and target cancellation (Figure 16e) | 23.08 |
Acquisition Parameters | Linear | Circular |
---|---|---|
Average platform velocity [m/s] | 91.4 | 83.55 |
Platform altitude above ground [m] | 5638 | 5637 |
Total observation time along azimuth [s] | 90 | 400 |
Number of SAR image(s) used | 1 | 1 |
Azimuth spacing [m] | 0.038 | 0.034 |
Chirp bandwidth [MHz] for X- and L-band | 384, 150 | |
Incidence angle range [°] | 15–60 | |
Radar wavelength [m] for X- and L-band | 0.0306, 0.226 | |
Pulse repetition frequency [Hz] | 2403.85 | |
Total number of range samples | 17,723 | |
Ground swath [km] | 8 | |
Range Resolution [m] for X- and L-band | 0.39, 1.0 | |
Range sample spacing [m] for X- and L-band | 0.3, 0.6 | |
Azimuth antenna length [m] for X- and L-band | 0.3 m (Transmit), 0.2 m (Receive) (X-band) | |
0.3 m (Transmit), 0.3 m (Receive) (L-band) | ||
Geographical coordinates | Shown in Figure 23a |
Clutter Models | Near Range | Mid-Range | Far Range | |
---|---|---|---|---|
CCDF | ||||
K-NLLSQ | 3.97 | 8.01 | −10.34 | −2.16 |
K-Vstat | 2.41 | 6.89 | - | |
K-Xstat | 3.23 | 7.61 | - | |
Chi-square | 6.98 | 8.87 | −10.34 | −4.98 |
3MD | 5.62 | 8.27 | −10.34 | −5.17 |
K-Rayleigh | −5.79 | −0.26 | - |
Clutter Models | Near Range | Mid-Range | Far Range |
---|---|---|---|
CCDF | |||
K-NLLSQ | 6.89 | 6.02 | −5.86 |
K-Vstat | 5.19 | 4.60 | - |
K-Xstat | 6.19 | 5.49 | - |
Chi-square | 9.70 | 7.73 | −11.65 |
3MD | 8.94 | 6.94 | −10.86 |
K-Rayleigh | −6.68 | 2.27 | - |
Distribution Functions | Near Range | Mid-Range | Far Range |
---|---|---|---|
K-NLLSQ | 80.5 | 112.1 | 3.08 |
K-Vstat | 35.1 | 57.1 | - |
K-Xstat | 56.9 | 86.8 | - |
Chi-square | 277.4 | 242.9 | 2.43 |
3MD | 149.2 | 135.9 | 1.56 |
K-Rayleigh | 1.31 | 1.68 | - |
Distribution Functions | Near Range | Mid-Range | Far Range |
---|---|---|---|
K-NLLSQ | 63.3 | 74.6 | 12.2 |
K-Vstat | 30.6 | 38.9 | - |
K-Xstat | 46.9 | 55.9 | - |
Chi-square | 422.4 | 234.9 | 9.43 |
3MD | 257.9 | 154.7 | 7.73 |
K-Rayleigh | 2.03 | 2.08 | - |
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Share and Cite
Joshi, S.K.; Baumgartner, S.V.; da Silva, A.B.C.; Krieger, G. Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection. Remote Sens. 2019, 11, 1270. https://doi.org/10.3390/rs11111270
Joshi SK, Baumgartner SV, da Silva ABC, Krieger G. Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection. Remote Sensing. 2019; 11(11):1270. https://doi.org/10.3390/rs11111270
Chicago/Turabian StyleJoshi, Sushil Kumar, Stefan V. Baumgartner, Andre B. C. da Silva, and Gerhard Krieger. 2019. "Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection" Remote Sensing 11, no. 11: 1270. https://doi.org/10.3390/rs11111270
APA StyleJoshi, S. K., Baumgartner, S. V., da Silva, A. B. C., & Krieger, G. (2019). Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection. Remote Sensing, 11(11), 1270. https://doi.org/10.3390/rs11111270