Leandro Pralon

Evaluation of ICA-Based ICTD for PolSAR Data Analysis Using a Sliding Window Approach: Convergence Rate, Gaussian Sources, and Spatial Correlation

Polarimetric incoherent target decomposition aims at accessing physical parameters of illuminated scatters through the analysis of the target coherence or covariance matrix. In this framework, independent component analysis (ICA) was recently proposed as an alternative method to eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high-resolution synthetic aperture radar systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to the classical eigenvector decomposition, and the inability to be employed in scenarios under the Gaussian clutter assumption. In this paper, both drawbacks are analyzed. First, a Monte Carlo approach is performed in order to investigate the bias in estimating Touzis target-scattering-vector-model parameters when ICA is employed. Simulated data and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the bias estimation under different scenarios. Finally, the performance of the algorithm is also evaluated under the Gaussian clutter assumption and when spatial correlation is introduced in the model.

Angular estimation for phased array surveillance radars considering orthogonal beamforming

Phased array antennas have been extensively used in radar systems. Each application requires an adequate design, to comply with specific requirements that involves both the radiation pattern as well as the beams spacing. Within this context, Orthogonal Beamforming arises as an interesting solution for surveillance radar applications. Besides optimum sidelobe isolation around the center of the beams, the orthogonality provides better coverage efficiency using the minimum number of beams to fill a given angular sector. On the other hand, since a single target can be detected in multiple beams, angular estimation in such configuration poses as a challenge when no windowing is applied to the radiation pattern (e.g. in long range applications) and the system can not cope with the additional computational burden of multi channel estimation. In the present work, closed form expressions describing the targets angular position in linear phased array systems with orthogonal beam shapes are derived, allowing full modeling of the beams, including side lobes positions. A combined algorithm for angular estimation is then proposed considering surveillance phased array systems with orthogonal beam spacing. Finally, the proposed algorithm performance, in terms of accuracy and precision, is evaluated using a Monte Carlo simulation approach, and compared to the simple centroid standalone solution.

Information extraction by blind source separation from polarimetric SAR data

Cloude and Pottier H/α feature space [1] is one of the most employed methods for unsupervised PolSAR data classification based on Incoherent Target Decomposition. The association of the coherence matrix eigenvectors to the most dominant scatters in the analysed pixel introduces unfeasible regions in the H/α plane. The Independent Component Analysis provides promising new information to better interpret non-Gaussian heterogeneous clutter in the frame of polarimetric incoherent target decompositions. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the H/α plane, increasing the range of possible natural phenomenons depicted in the aforementioned feature space.

A comparison between real and complex Schott spherical symmetry test for PolSAR data analysis

Most of the tests proposed in the literature to verify if a given random multivariate dataset fits a spherical or elliptical distribution are designed for real valued data and rely on the estimation of high order moment matrices. Recently, a test that considers complex random vectors, derived based on the Schott spherical symmetry test was proposed aiming in a more proper analysis of PolSAR data. Results showed its effectiveness in discriminating data that fits or not the complex spherically invariant random vector model (product model), inherent to high resolution heterogeneous PolSAR systems. Within this context, this paper further extends the assessment of the referred test efficiency, verifying its performance under different stochastic model assumptions and comparing the results with the ones achieved when the Schott test derived for real random vectors is employed.

Near-thumbtack ambiguity function of random frequency modulated signals

Noise radar is an emerging technology which employs random signals as their transmit waveforms. Relying on its stochastic properties, with the proper choice of system s parameters, high performance with respect to the suppression of range ambiguity and low range sidelobes can be achieved. Many works have been published in the literature addressing systems that transmit carriers modulated in amplitude by a given stochastic process, generally generated by a hardware noise source. Nevertheless, the characterization of random frequency modulation (more suitable for several applications) is still an on going subject matter. Within this context, in this paper closed form expressions of the narrowband ambiguity function of random frequency modulated signals are presented, and, as a consequence, its Doppler tolerance is better addressed.

Blind source separation in polarimetric SAR interferometry

Polarimetric incoherent target decomposition aims in accessing physical parameters of illuminated scatters through the analysis of target coherence or covariance matrix. In this framework, Independent Component Analysis (ICA) was recently proposed as an alternative method to Eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high resolution SAR systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to classical Eigenvector decomposition and the inability to be employed in scenarios under Gaussian clutter assumption. First, a Monte Carlo approach is performed in order to investigate the bias in estimating the Touzi Target Scattering Vector Model (TSVM) parameters when ICA is employed. A RAMSES X-band image acquired over Brétigny, France is taken into consideration to investigate the bias estimation under different scenarios. Finally, some results in terms of POLinSAR coherence optimization [1] in the context of ICA are proposed.

On a probabilistic approach to detect Noise Radar random transmit waveforms based on a simple circularity test

Noise Radars are electromagnetic systems that use random signals for detecting and locating reflecting objects. Besides high performance with respect to the suppression of range ambiguity in the detection of targets and low range sidelobes, they also present an intrinsic property of low probability of interception by other systems, due to the stochastic nature of their transmit waveforms. Traditional methods and equipment are often ineffective to detect the presence of such kind of radars, both in time and frequency, since they generally adopt an ultra-wide bandwidth (UWB) configuration, spreading its power through a broad portion of the spectrum. Within this context, this paper proposes a probabilistic approach based on a statistical property of random vectors, the circularity, to detect the presence of pulses of this nature in the scenario under study.