# Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection
## 1 Introduction  
Target detection and monitoring in the maritime environment is an imperative measure to ensure safety and security on the open sea. The outputs of such activities are used by various agencies for the sustainable management of the ocean and its resources. One of the most popular maritime surveillance services is the automatic identification system (AIS) for the precise positioning of moving ships [1]. However, most of the vessels and small boats are not equipped with the AIS transceivers. Moreover, some vessels turn off their receivers to execute illegal activities which make it harder to detect them. Remotely sensed images acquired over the sea surface are able to provide wide area coverage and repetitive monitoring in such situations. Among the different imaging sensors, synthetic aperture radar (SAR) is considered to be one of the most effective due to its all-weather independent and day-night acquisition capabilities. SAR sensors mounted either on spaceborne or airborne platforms illuminate the ground to record the backscattered signals. These signals are used to generate two-dimensional (2D) images with high resolution and wide swath coverage. In SAR images, ships generally appear as bright spots compared to its background, known as \"sea clutter\" due to their metallic structures and corners. Therefore, there exist a vast number of publications exploring the potential of fully focused SAR images to detectships, e.g., based on ship wakes [2], generalized likelihood ratio test (GLRT) [3], as well as sublook decomposition and analysis [4]. Algorithms based on ship wake and polarimetric ship wake detection can provide additional information about the ship velocity and the moving direction [5; 6; 7] without using along-track interferometry. One of the widely used methods for detecting ships is to model the background ocean clutter by a stochastic clutter model to derive a detection threshold based on a constant false alarm rate (CFAR) [8]. The detections may be further used to generate high resolution images using, e.g., inverse SAR (ISAR) imaging techniques [9; 10]. These images may be utilized for target recognition and identification purposes. For such applications, airborne platforms are very beneficial due to their flexibility to collect data with very high resolution and with short revisit and long observation times [11; 12; 13]. Furthermore, the use of multiple receiving channels offers additional advantages like clutter cancellation which leads to efficient target detection [14; 15]. Additionally, keeping in mind the potential real time requirements of ship monitoring, the use of fully focused SAR images is not always the desirable choice. Extra processing efforts in terms of range cell migration correction and an additional azimuth compression using reference functions are necessary to generate these SAR images. Instead, using range-compressed data (RC) is very attractive, especially taking into account that no conventional computation time-consuming SAR processing has to be carried out. In [9; 16], we have demonstrated the advantage of RC radar data to detect and image moving ships for different radar platforms. The present paper proposes a novel processing chain for ship detection using RC radar data that is suitable for real time applications. More details on the use of RC data for target detection are given in Section 2. The target detection is carried out in the range-Doppler domain. The advantage of using the range-Doppler domain is that even ships with a low radar cross section (RCS) can be detected when they are moving with a certain line-of-sight velocity and are shifted to the exo-clutter region as discussed in detail in Section 2. Sea clutter models are used to derive a CFAR threshold. To get a valid threshold, such models need a careful selection of ocean training samples which correctly describe the background ocean statistics. Therefore, an automatic ocean training data selection approach is also proposed.