# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 2 Principle of the Algorithm
### Algorithm Block Diagram  
Figure 4 gives a detailed block diagram of the proposed methodology for ship detection in range-Doppler domain using single-channel RC radar data as input. Note that, as already mentioned in Section 1, instead of single-channel RC data, the sum-channel or the clutter-suppressed channel data obtained, e.g., after STAP applied on RC multi-channel data, can principally be used as input data as well. Figure 4: Block diagram of the proposed algorithm which uses RC airborne radar data as input. The individual blocks and processing steps are numbered from 1 to 17 and are discussed in detail in the text. Figure 3: Clutter Doppler centroid map computed by using the measured aircraft Euler angles for a circular flight carried out with F-SAR (details about the flight are given in Section 6). In Figure 4, the RC radar data (top) are initially divided into \"green\" regions, where each green region is further partitioned along range into \"red\" sub-regions. Each red sub-region consists of a group of \"blue\" data blocks where each data block consists of a certain number of azimuth samples commonly known as coherent processing interval (CPI). The selection of the optimum number of azimuth samples is system dependent. For the F-SAR system, the typical acquisition geometry and the pulse repetition frequency (PRF) of 2400 Hz, we found that 128 azimuth samples per CPI are well suited. First, an average amplitude profile over azimuth is estimated for a red sub-region (labelled with (1) in Figure 4). A trend along range is derived after performing median filtering (2). Then, for each blue data block this median filtered range trend is used for performing range de-trending (3). This is important for normalizing the range varying amplitude of the data to a constant value (4). After range de-trending, each data block is transformed into range-Doppler domain by performing an azimuth FFT (5). From the \"target pre-detection\" module (6), the azimuth lines corresponding to \"potential targets\" at certain range positions are detected and cancelled in range-Doppler domain for each data block of the red sub-region (7). More details on the significance of target pre-detection and the proposed method are presented in Section 3.1. Afterwards, an average Doppler spectrum without target signals is estimated using all data blocks (8). Normalization over Doppler is performed (9) individually for each \"red\" group of data blocks using the estimated average Doppler spectrum (10). As result a normalized Doppler spectrum is obtained (11). More details on the average Doppler spectrum estimation and normalization are given in Section 3.2. The normalized, target free data are used as training data to estimate the local ocean clutter statistics (12). By setting a desired probability of false alarms (PFA) and by using the estimated statistics from the sea clutter model, a CFAR detection threshold is computed (13). The average Doppler power spectrum (10), which was estimated before, is further used to normalize the \"original\" \"blue\" range-Doppler data blocks (14) (see also Figure 1a,b). The CFAR threshold is then applied individually to each of these data blocks to detect the ship pixels (15). This procedure is repeated until all \"red\" groups of data blocks along range (i.e., within the \"green\" region at the top of Figure 4) are covered. Finally, clustering (16) is performed using all the available detections along the complete range of the scene and 128 azimuth samples (cf. black box in Figure 4 bottom right). The 2D positions of the computed cluster centroids for each \"black\" data block are then used for \"Tracking\" (17). The refined target positions obtained after tracking in range-Doppler domain are then projected back to time domain. An example of this processing step is shown in Figure 1d.