Jamming of synthetic aperture radar (SAR) is more difficult than the conventional radars because of its high processing gain. The defocusing of fake or virtual scatterers is the main problem that any jamming technique needs to tackle. An algorithm based on fractional Fourier transform is proposed for the efficient jamming of SAR. The results show that the performance of proposed algorithm is better than conventional Range Doppler Algorithm.

Ship surveillance is important in maritime management. Space-borne Synthetic Aperture Radar (SAR), High Frequency Surface Wave Radar (HFSWR) and the Automatic Identification System (AIS) are three main sensors for the ship surveillance of large maritime areas. Fusion of these sensors' measurements can produce an accurate ship image distribution in a surveillance area. Data association is fundamental to data fusion. A Maximum Likelihood (ML) association algorithm with multi-feature improvements is proposed to increase detection accuracy and reduce false alarms. The tested features are position, size, heading and velocity. First, the ship measurement model is established. Then, the problem of data association for SAR, HFSWR and AIS is formulated as a multi-dimensional assignment problem. In the data assignment process, Jonker-Volgenant-Castanon (JVC) and Lagrangian relaxation algorithms are applied. Simulation results show that the algorithm proposed here can improve the association accuracy compared with the Nearest Neighbour (NN) and the position-only ML algorithms, using the additional features of length and velocity. Real data experiments illustrate that the algorithm can enhance target identification and reduce false alarms.

This paper presents a shallow water depth estimation methodology using S-band Synthetic Aperture Radar (SAR) data from the HJ-1C satellite. It is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. A two-scale Bragg scattering model is used to describe the imaging process of long waves by SAR. By computing the Fast Fourier Transformation (FFT) for the selected sub image, wavelength and direction of the long wave can be retrieved from the two-dimensional (2D) spectra with wave tracking technology. Shallow water depths are then obtained from the linear dispersion relation with the calculated angular wave frequency obtained from other sources or first guesses of initial water depths or wave periods. Applicability and effectiveness are tested in the near-shore area of the Fujian province, China. Comparison between the derived results and water depths from an Electronic Navigational Chart (ENC) indicates that HJ-1C SAR is capable of higher resolution underwater topography detection, and the methodology can be used for shallow water depth estimation with good accuracy. The average absolute error and average relative error of the estimated results is 0·86 m and 11·05%, respectively.

Many countries are making increased efforts to improve marine security and safety and develop ship surveillance techniques to satisfy the increasing demands. Space-borne Synthetic Aperture Radar (SAR) delivers high performance day/night all weather capabilities and a space-based Automatic Identification System (AIS) can give near real time and global coverage. Limited by the development of sensors and data processing techniques, the integration of space-borne SAR and AIS has much to offer ship surveillance. State-of-the-art data fusion methods have generally provided satisfactory performance. However, in high-density shipping or high sea-states, performance quality is less assured. This paper firstly investigates improved data association methods. The association methods based on the position feature are improved, and multi-feature-based association methods are proposed. Then, ship identification and tracking by the integration of space-borne SAR and AIS are researched further. Multi-source data fusion strategy is also investigated. Finally, the discussion is presented and the future works are emphasized in the conclusion.

Ship surveillance is important for maritime security and safety. It plays important roles in many applications including ocean environment monitoring, search and rescue, anti-piracy and military reconnaissance. Among various sensors used for maritime surveillance, space-borne Synthetic Aperture Radar (SAR) is valued for its high resolution over wide swaths and all-weather working capabilities. However, the state-of-the-art algorithms for ship detection and identification do not always achieve a satisfactory performance. With the rapid development of space-borne Automatic Identification System (AIS), near real-time and global surveillance has become feasible. However, not all ships are equipped with or operate AIS. Space-borne SAR and AIS are considered to be complementary, and ship surveillance using an integrated combination has attracted much attention. In order to summarize the achievements and present references for further research, this paper attempts to explicitly review the developments in previous research as the basis of a brief introduction to space-borne SAR and AIS.