Alessio Bacci

Compressive sensing–based algorithm for passive bistatic ISAR with DVB-T signals

Inverse synthetic aperture radar (ISAR) images can be obtained using digital video broadcasting-terrestrial (DVB-T)-based passive radars. However, television broadcast-transmitted signals offer poor range resolution for imaging purposes, because they have a narrower bandwidth with respect to those transmitted by a dedicated ISAR system. To reach finer range resolutions, signals composed of multiple DVB-T channels are required. Problems arise, however, because DVB-T channels are typically widely separated in the frequency domain. The gaps between channels produce high grating lobes in the image domain when Fourier-based algorithms are used to form the ISAR image. In this paper, compressive sensing theory is investigated to address this problem because of its ability to reconstruct sparse signals by using incomplete measures. By solving an optimization problem under the constraint of signal sparsity, passive ISAR images can be obtained with strongly reduced grating lobes. Both simulation and experimental results are shown to demonstrate the validity of the proposed approach.

Refocussing of moving targets in SAR images based on inversion mapping and ISAR processing

Moving target often appear defocussed in SAR images due to their relative motion with respect to the SAR center scene. In several applications that require target classification and/or recognition an unfocussed image of a target would likely lead to misclassification, therefore decreasing classification performances. In this paper, the authors present a new method for refocussing moving targets starting from formed Single Look Complex (SLC) SAR images. Results obtained by applying the proposed techique to Cosmo Skymed (CSK) SLC SAR images show its effectiveness.

Joint STAP-ISAR for non-cooperative target imaging in strong clutter

The aim of this work is to combine clutter suppression and ISAR processing in order to obtain well focused images of extended moving targets. A STAP ISAR based moving target imaging scheme for Multichannel SAR (M-SAR) in strong clutter environment is presented. This scheme consists of two main processing steps. The first step performs clutter mitigation and radar motion compensation by exploiting Space Time Adaptive Processing while the second step performs target motion compensation using ISAR processing. Two principal issues will be addressed. First a technique for applying ISAR processing after clutter mitigation is presented and then a suboptimal approach to clutter mitigation is proposed to overcome computational and statistical issues associated with estimation of clutter space time covariance matrix. Results of the processing applied to simulated data are provided in order to show the effectiveness of the proposed technique.

Space-Doppler processing for multichannel ISAR imaging of non-cooperative targets embedded in strong clutter

Non-cooperative moving targets appear defocussed in SAR images and the blurring effect due to the unknown motion leads to low detection capabilities. In this work, clutter suppression and ISAR processing are combined to obtain well focused images of extended non-cooperative moving targets embedded in strong clutter, by exploiting Multichannel SAR (M-SAR) systems. Clutter mitigation and radar motion compensation are performed by means of the proposed Space Doppler Adaptive Processing. Then ISAR processing is used to compensate the unknown target motion. Two principal issues will be addressed. First, a technique to apply ISAR processing after clutter mitigation is presented, and then a suboptimal approach for clutter mitigation is proposed to overcome computational and statistical issues associated with estimation of clutter cross-power spectral matrix. Results of the processing applied to simulated data are provided in order to show the effectiveness of the proposed techniques.

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.

High resolution imaging of moving ground targets via clutter mitigation and ISAR processing

Non-cooperative moving targets appear defocussed within SAR images. Moreover, in the case of ground targets, the blurring effect due to the uncompensated target motion decreases detection capabilities. In this work, clutter suppression by means of Space Doppler Adaptive Processing and ISAR imaging are combined to obtain high resolution images of non-cooperative moving targets within SAR images. Results obtained by processing a real dataset prove the effectiveness of the proposed processing chain.

A sub-optimal approach for bistatic joint STAP-ISAR

Moving targets appear defocused within SAR images and their detection is challenging especially in the case of ground targets that are embedded in strong ground clutter. STAP methods show optimal results in the clutter and interference suppression when the signal environment is stationary. This improves detection performance and allows for the application of ISAR based techniques which are then used to obtain high resolution images of moving targets. However, in bistatic system geometry, clutter echo returns are not stationary but range dependent. This situation degrades significantly the STAP performance due to the fact that data are not iid. By modeling the dynamic behavior of the beamforming weight, the losses in performance may be recovered at the expense of doubling DoFs and then significantly increasing the computational cost. The aim of this work is to combine bistatic STAP and ISAR techniques to obtain a well focused image of non-cooperative moving targets with a lower computational cost with respect to the classical bistatic STAP technique. Two principal issues will be addressed. First, a clutter model in the bistatic geometry is developed and then a sub-optimal implementation of the Extended Sample Matrix Inversion to clutter mitigation is proposed. Results of the proposed processing applied to simulated data are provided in order to show the effectiveness of the proposed technique.

Clutter Suppression and High-Resolution Imaging of Noncooperative Ground Targets for Bistatic Airborne Radar

In a recent paper, the problem of inverse synthetic aperture radar (ISAR) imaging moving ground targets was discussed and an effective solution based on a novel joint space-time adaptive processing (STAP)-ISAR formulation was proposed. In the case of bistatic geometry, ISAR imaging can be simply and effectively enabled via the bistatically equivalent monostatic theory, which allows for monostatic ISAR processing to be used to form bistatic ISAR images. However, the clutter nonstationary introduced by the bistatic geometry strongly degraded STAP performances if a monostatic STAP processing is used. In this paper, a new joint STAP-ISAR processing is introduced that works in the bistatic case. Theoretical figures and numerical simulations are used to prove the effectiveness of the proposed approach.

Cognitive multichannel ISAR imaging for maritime coastal surveillance and ground border control

In this paper an attempt to design a cognitive multiplatform SAR system for 24/7 concealed target detection has been made. The need for a cognitive system is dictated by the need to change the waveform parameters over time to maximise the system performance. How the waveform diversity is implemented in a real system is in fact an open issue in literature and cognitive system could answer this need.