A parametric inversion scheme for the retrieval of two‐dimensional (2‐D) ocean wave spectra from look cross spectra acquired by spaceborne synthetic aperture radar (SAR) is presented. The scheme uses SAR observations to adjust numerical wave model spectra. The Partition Rescaling and Shift Algorithm (PARSA) is based on a maximum a posteriori approach in which an optimal estimate of a 2‐D wave spectrum is calculated given a measured SAR look cross spectrum (SLCS) and additional prior knowledge. The method is based on explicit models for measurement errors as well as on uncertainties in the SAR imaging model and the model wave spectra used as prior information. Parameters of the SAR imaging model are estimated as part of the retrieval. Uncertainties in the prior wave spectrum are expressed in terms of transformation variables, which are defined for each wave system in the spectrum, describing rotations and rescaling of wave numbers and energy as well as changes of directional spreading. Technically, the PARSA wave spectra retrieval is based on the minimization of a cost function. A Levenberg‐Marquardt method is used to find a numerical solution. The scheme is tested using both simulated SLCS and ERS‐2 SAR data. It is demonstrated that the algorithm makes use of the phase information contained in SLCS, which is of particular importance for multimodal sea states. Statistics are presented for a global data set of 11,000 ERS‐2 SAR wave mode SLCS acquired in southern winter 1996.

A growing interest in global maritime surveillance has motivated the development of new methods and sensors to overcome the limitations of coastal-based systems coverage. Among the available state of the art tools, the Synthetic Aperture Radar (SAR) stands out as a powerful sensor for oceanographic observation and maritime surveillance, due to its capability of wide swath coverage and the ability to operate in all weather conditions. This paper examines the minimum ship size detectable by the Constant False Alarm Rate (CFAR) algorithm over different TerraSAR-X product modes, using simulation-based analyses. These analyses allow a comparison of the expected CFAR ship detection performance for different TerraSAR-X products, over different sea state conditions.

Herein, a detailed correlation index of health indicators for lithium‐ion batteries is presented. Identifying potential correlations of health indicators is of high importance with regard to the cell selection process and to minimize the occurring cell‐to‐cell spread within the lifetime. Health indicators that are taken into account are among others impedance measurements of different pulse lengths, capacity values at different discharge procedures and checkups, weight, and initial voltage. Herein, the work is based on four different aging datasets covering variations in cell chemistry (NMC, LFP, NCA), cell type (round, prismatic), as well as the size and designated application (consumer, automotive). A publicly available dataset is included to allow for an easy reproduction of the results.

Synthetic Aperture Radar (SAR) is a unique sensor able to provide two dimensional information on meteo-marine parameters of the ocean surface e.g. wind speed and wave height. We present an algorithm (XWAVE) to derived significant wave height (H_s) from TerraSAR-X (TS-X) data without the need of a priori information. The algorithm is based on spectral analyses of TS-X data, using a Geophysical model function, the coefficients are fitted to collocated in-situ buoy measurements using the least squares method. Wind speed information provided by the wind speed estimation XMOD algorithm [1] is combined in the XWAVE algorithm as well. Sea state parameters derived from TS-X data using the proposed algorithm have been compared to in-situ buoy measurements. Additionally, the one dimensional spectra are compared to the wave spectrum provided by the one given by the NOOA buoys. The SAR wave peak direction is also compared to the wave peak direction given by directional buoys. Sea state properties in coastal areas change rapidly in the spatial domain. TS-X data are especially suitable for the study of sea state variability in coastal areas due to its high resolution. Coastal wave fields have been derived from TS-X data and verified with numerical wave model results and with in-situ buoy measurements for the collated point in the area of the North Sea.