Sonia Tomei

Compressive sensing based ISAR: Performance evaluation

Compressive Sensing theory has been recently proven to be a valid tool to reconstruct ISAR images by using a limited amount of data samples. This property has gained the attention of the radar scientific community as it seems to overcome the Nyquist theorem. However, the capability of the CS to effectively reconstruct an ISAR image is still to be proven. From here, the need to provide the means to measure the CS based algorithm performance. A set of parameters to measure CS-based ISAR algorithm performance is provided in this paper and some examples are also shown by using real data.

The effect of Travelling Ionospheric Disturbances upon the performance of an HF skywave MIMO radar

HF Skywave radars rely on the refractive properties of the ionosphere to reach distances beyond the horizon. The instability of this medium on wide temporal and spatial scales is one of the most important issue to be addressed in the design of such systems, especially when considering a MIMO (Multiple Input Multiple Output) configuration. In such a configuration, multiple transmitters emit orthogonal waveforms that are properly separated and processed at the receiver to extract the target parameters. The success of this process relies on the separability of the transmitted waveforms at the receiver. During the propagation through the ionosphere the transmitted radiowaves undergo different degrading phenomena that change their property so they might not be separated at the receiver with consequences on the radar performances. These degrading phenomena depend on the ionospheric state and cannot be avoided. The simulator described in this work has the aim to provide the evaluation of the effect of both disturbed and undisturbed ionospheres. In particular, the fading caused by TIDs (Travelling Ionospheric Disturbances) is considered. MIMO radar for different geometries are considered in order to show the advantages and the disadvantages of each configuration.

OTH Skywave MIMO signal model and target detection in presence of multipath

HF Skywave radars exploit the refractive properties of the ionosphere to reach distances beyond the horizon allowing for the surveillance of very large areas. The instability of the ionospheric channel on wide temporal and spatial scales is one of the most important issue to be addressed in the design of such systems, especially when considering a MIMO configuration. In fact, large scale ionospheric disturbances and multipath can affect the propagating signals and degrade the radar performance. The main objective of this paper is to derive a suitable signal model that accounts for ionospheric multipath and fading. ROC curves are then simulated and compared to the case in which multipath is not considered.

Super resolution ISAR imaging via Compressive Sensing

Developing compressed sensing (CS) theory has been applied in radar imaging by exploiting the inherent sparsity of radar signal. In this paper, we develop a super resolution (SR) algorithm for formatting inverse synthetic aperture radar (ISAR) image with limited pulses. Assuming that the target scattering field follows an identical Laplace probability distribution, the approach converts the SR imaging into a sparsity-driven optimization in Bayesian statistics sense. We also show that improved performance is achieved by taking advantage of the meaningful spatial structure of the scattering field. To well discriminate scattering centers from noise, we use the non-identical Laplace distribution with small scale on signal components and large on noise. A local maximum likelihood estimator combining with bandwidth extrapolation technique is developed to estimate the statistical parameters. Experimental results present advantages of the proposal over conventional imaging methods.