SAR-ISAR Blending Using Compressed Sensing Methods

Inverse Synthetic Aperture Radar (ISAR) target images are extracted using compressed sensing methods. The extracted images are edited and merged into measured Synthetic Aperture Radar (SAR) images. A noise free image of the target is extracted from the Radar Cross Section (RCS) measurement by using the Basis Pursuit Denoise (BPDN) method and then solving for a model consisting of point scatterers. The target signature point scatterers are then merged into a point scatterer representation of the SAR background scene. This method means that SAR images acquired in expensive airborne field trials can be used efficiently to evaluate different targets and camouflage measured separately in a ground based setup. The method is demonstrated with turntable measurements of a full scale target, with and without camouflage, signature extraction and blending into a SAR background. We find that the method provides an efficient way of evaluating measured target signatures in SAR backgrounds

Anisotropic velocity distributions in 3D dissipative optical lattices

We present a direct measurement of velocity distributions in two dimensions by using an absorption imaging technique in a 3D near resonant optical lattice. The results show a clear difference in the velocity distributions for the different directions. The experimental results are compared with a numerical 3D semi-classical Monte-Carlo simulation. The numerical simulations are in good qualitative agreement with the experimental results.Comment: Accepted for publication in Eur. Phys. J., "Special issue: Quantum fluctuations and coherence in optical and atomic structures" (2003

Non-Gaussian Velocity Distributions in Optical Lattices

We present a detailed experimental study of the velocity distribution of atoms cooled in an optical lattice. Our results are supported by full-quantum numerical simulations. Even though the Sisyphus effect, the responsible cooling mechanism, has been used extensively in many cold atom experiments, no detailed study of the velocity distribution has been reported previously. For the experimental as well as for the numerical investigation, it turns out that a Gaussian function is not the one that best reproduce the data for all parameters. We also fit the data to alternative functions, such as Lorentzians, Tsallis functions and double Gaussians. In particular, a double Gaussian provides a more precise fitting to our results.Comment: Final published version with 12 pages and 12 figure

Camouflage effectiveness of static nets in SAR images

We present a methodology to determine the camouflage effectiveness of static nets in a SAR image. There is currently no common recognized methodology within the signature management community in this research topic. One step towards establishing a common methodology is to use a standardized target to be camouflaged. We use the STANdard Decoy for CAmouflage Materials (STANDCAM) target developed by the German Army, WTD 52, Oberjettenberg. An ISAR measurement of the STANDCAM with a camouflage configuration is acquired as the first step of the method. The ISAR data is then blended with SAR data acquired in field trials. In the final SAR image a contrast metric between the target and background is extracted. The contrast measure is then the measure of the camouflage effectiveness. As an example of result we present ISAR measurements and determine the camouflage effectiveness in a SAR image using SAR blending for static nets with different electrical conductivity and design. This methodology presents a measure to determine the effectiveness of a static net on the STANDCAM target

Near-field SAR for signature and camouflage evaluation in realistic backgrounds

Outdoor ranges are commonly used for accurate RCS measurements of full scale vehicles with or without camouflage. Furthermore, ISAR processing is often used to obtain high quality imagery of the objects. However, these images do not show the vehicles in the environment where they are supposed to operate. This paper describes a ground based SAR system that is used to make accurate measurements on vehicles or other objects in realistic backgrounds. A transportable short range SAR measurement system is constructed by placing a SAR on the bed of an all terrain truck. The radar system is based on a PNA network analyzer without power amplifier and a pair of standard X-band horn antennas. The target range is typically 100 m, which with a 5.4 m SAR rail gives a minimum cross-range resolution of 0.3 m at X-band frequencies. The down-range resolution is also adjusted to 0.3 m by adjusting the bandwidth. The SAR processing gain is sufficient to provide high quality images limited only by the background clutter. By using the terrain the objects can be measured at depression angles up to at least 10°. The SAR images are near field images which are geometrically correct since back projection algorithms are used. The approach also gives very high angle reproducibility. This means that the same measurement geometry can be used to record the signature from a vehicle placed in a background environment before and after the addition of a camouflage system. It is then possible to use the two processed images to make a realistic estimate of the camouflage efficacy in this environment. Flexible software based entirely on open source components is developed for the data processing, presentation and statistical analysis of the SAR images. The advantages with this type of setup compared to flying SAR are less complex field trials and higher accuracy in the evaluation of camouflage. This paper presents results from the evaluation of two generic camouflage nets using reference targets in different backgrounds and SAR images of vehicles

Advances in SAR-ISAR blending

Radar signature measurements of targets with or without camouflage in different backgrounds using airborne SAR are complex and expensive. Measurements at many orientations as well as illumination angles have to be performed for each target for completeness. A more efficient solution is to use ground based ISAR measurements of the desired targets and then blend these images into measured SAR scenes. A SAR-ISAR blending method where the target and background are modelled by point scatterer representations is described in this paper. The point scatterer representations for the target and the SAR background are determined by solving two separate inverse problems using ℓ1 and ℓ2-minimization methods. The model for the target measured by ISAR is naturally sparse in the image domain and is therefore solved using an ℓ1-minimization method while the model for the SAR background image that is not sparse is solved using an ℓ2- minimization method. The proposed method is demonstrated and it is determined that it provides an efficient way of evaluating measured ISAR target signatures in measured SAR backgrounds