# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 3 Training Data Selection  
An appropriate selection of training data and sea clutter statistics estimation lead to an accurate computation of the detection threshold. In reality, training samples within a region of interest may be contaminated by target peaks and high clutter peaks. A typical example of the presence of a bright ship signal in the RC radar data in the time domain is shown in Figure 5.  
To understand the effects of ocean clutter contamination due to bright targets, a logarithmic plot of the probability density functions (PDFs) of \"ocean only\" and \"ocean with the bright ship signal\" is shown in Figure 6.  
Figure 6 shows how the presence of the target and other high peaks in the background clutter skews the histogram to extremely high intensity values (blue). Such high intensities can severely degrade the performance of clutter models. The detection threshold may be raised to a very high value which lowers the probability of detection. In the literature, such effects are termed as \"capture effects\". Other complications in the modeling of sea clutter arise due to the presence of high clutter peaks that causes sudden transition and shift in the background intensity values. These effects are known as \"clutter edge effects\" [26].  
Figure 5: Patch of HH polarization X-band F-SAR data containing a ship target (at a range of approx. 7570 m). For visualization purposes, the data were normalized to the maximum power.  
Figure 6: Logarithmic plot of the PDFs of the ocean only (red) and ocean with a ship signal (blue) are shown. For visualization purposes, the intensity axis is truncated as the maximum intensity due to the ship is around 600.  
Several versions of CFAR detectors were developed in the past to improve the target detection performance by eliminating these outliers. Order statistic CFAR, trimmed CFAR, censored mean level detector, iterative censoring are some of the state-of-the-art methods used to remove interfering targets. Despite showing relatively good performance in homogeneous scenarios, these techniques either have limited performance in heterogeneous environments, require wise choice of the parameters or are computationally ineffective [27; 28; 29; 30]. More recently, a new algorithm based on truncated statistics CFAR (TS-CFAR) was developed in [31] where the threshold is estimated from the truncated distribution functions. TS-CFAR has been shown as a very effective way to eliminate the outliers and estimate a robust threshold. Although being a powerful method, the truncation depth has first to be fixed to a certain value and the truncated statistical distribution functions have to be derived afterwards. Fixing the truncation depth is tedious and deriving the truncated version of the sophisticated sea clutter models is both complex and time-consuming.  
All these aforementioned developments and their associated problems led us to propose a simple, robust and effective method to pre-detect the potential targets with real time capability. In the following, the target pre-detection method in the time domain, the clutter normalization step and the importance of training data update are explained in detail.