Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods

Sparse multidimensional exponential analysis with an application to radar imaging

We present a d-dimensional exponential analysis algorithm that offers a range of advantages compared to other methods. The technique does not suffer the curse of dimensionality and only needs O((d + 1)n) samples for the analysis of an n-sparse expression. It does not require a prior estimate of the sparsity n of the d-variate exponential sum. The method can work with sub-Nyquist sampled data and offers a validation step, which is very useful in low SNR conditions. A favourable computation cost results from the fact that d independent smaller systems are solved instead of one large system incorporating all measurements simultaneously. So the method also lends itself easily to a parallel execution. Our motivation to develop the technique comes from 2D and 3D radar imaging and is therefore illustrated on such examples

DVB-S based passive polarimetric ISAR – methods and experimental validation

In this work, we focus on passive polarimetric ISAR for ship target imaging using DVB-S signals of opportunity. A first goal of the research is to investigate if, within the challenging passive environment, different scattering mechanisms, belonging to distinct parts of the imaged target, can be separated in the polarimetric domain. Furthermore, a second goal is at verifying if polarimetric diversity could enable the formation of ISAR products with enhanced quality with respect to the single channel case, particularly in terms of better reconstruction of the target shape. To this purpose, a dedicated trial has been conducted along the river Rhine in Germany by means of an experimental DVB-S based system developed at Fraunhofer FHR and considering a ferry as cooperative target. To avoid inaccuracies due to data-driven motion compensation procedures and to fairly interpret the polarimetric results, we processed the data by means of a known-motion back-projection algorithm obtaining ISAR images at each polarimetric channel. Then, different approaches in the polarimetric domain have been introduced. The first one is based on the well-known Pauli Decomposition. The others can be divided in two main groups: (i) techniques aimed at separating the different backscattering mechanisms, and (ii) image domain techniques to fuse the polarimetric information in a single ISAR image with enhanced quality. The different considered techniques have been applied to several data sets with distinct bistatic geometries. The obtained results clearly demonstrate the potentialities of polarimetric diversity that could be fruitfully exploited for classification purposes