# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 7 Conclusions  
This paper proposes a CFAR-based ship detection processing chain principally suitable for real time applications. The major component of the proposed processing chain is the automatic training data selection approach. This approach includes a novel target pre-detection module used for successfully cancelling the outliers, i.e., spiky clutter peaks and strong ship signals, from the training data. This ensures a proper fit of suitable sea clutter models, and consequently an accurate CFAR threshold computation. A proper fit of the sea clutter models and an accurate CFAR threshold computation are essential for keeping the threshold errors and false alarm rate errors at a low level.  
Various sea clutter models were thoroughly investigated in terms of threshold errors and false alarm rate errors using experimental single-channel range-compressed radar data acquired with DLR's airborne sensor F-SAR during linear and circular flight tracks. Many X-band data with HH polarization and a range bandwidth of 384 MHz were used for these investigations. It was found that as a sea clutter model the K-distribution (regardless of any parameter estimation method) is generally not a good choice, especially not for near (15\({}^{\circ}\)-30\({}^{\circ}\) incidence angle) and mid ranges (30\({}^{\circ}\)-50\({}^{\circ}\)). The chi-square and the 3 MD model lead to extremely high false alarm rate errors and threshold errors in the near and mid ranges but perform very well in the far range (>50\({}^{\circ}\) incidence angle), which is dominated by thermal noise. In contrast, the K-Rayleigh distribution results in the smallest false alarm rate errors and threshold errors in the near and mid-ranges but were found unsuitable for the far range.  
Therefore, in terms of suitable sea clutter models, we recommend using the K-Rayleigh distribution function for lower incidence angle ranges (15\({}^{\circ}\)-50\({}^{\circ}\)) and the 3 MD model or the chi-square model for higher incidence angles (>50\({}^{\circ}\)). We also want to point out that for the investigations in this paper we mainly have used X-band radar data acquired during two F-SAR flights each lasting only a few hours. During that short time frame, it can be expected that the sea state has not changed significantly. Therefore, we were not able to investigate the influence of the sea state on the sea clutter model performance. This is an open topic for the future. Further investigations using linearly and circularly acquired radar data at different sea states are recommended. However, to the authors knowledge, so far, such an extensive data set, which would allow for more sophisticated investigations, does not exist.  
Conceptualization, S.K.J. and S.V.B.; Methodology, S.K.J. and S.V.B.; Software, S.K.J. and S.V.B.; Validation, S.K.J. and S.V.B.; Formal Analysis, S.K.J. and S.V.B.; Investigation, S.K.J.; Resources, S.V.B.; Writing--Original Draft Preparation, S.K.J.; Writing--Review & Editing, S.K.J., S.V.B., A.B.C.d.S. and G.K.; Visualization, S.K.J. and S.V.B.; Supervision, S.V.B.; Funding Acquisition, S.V.B. This research was funded by the German Academic Exchange Service (DAAD) [grant number 57265855].  
The authors would like to thank the anonymous reviewers for their useful comments and suggestions that significantly improved the clarity of the paper.  
The authors declare no conflict of interest.