Prof. Renato Machado

Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture . More precisely, we derive novel closed-form expressions for the probability of detection (PD) and the probability of false alarm (PFA), that can be readily evaluated using any mathematical software. Monte-Carlo simulations corroborate our analytical findings.

12 October 2016 in IEEE Transactions on Geoscience and Remote Sensing

Journal Article

False Alarm Reduction in Wavelength-Resolution SAR Change Detection Using Adaptive Noise Canceler

Viet Thuy Vu

Mats Ingemar Pettersson

Renato Machado

Patrik Dammert

Hans Hellsten

This paper introduces a method to reduce false alarms in wavelength-resolution synthetic aperture radar (SAR) change detection and aims at very high frequency-band systems like the Coherent All Radio Band System (CARABAS). The false alarms are usually caused by the elongated structures, such as power lines and fences, which stand out from the background. The responses of elongated structures are sensitive to flight path. The introduced method aims at minimizing the false alarms caused by the elongated structures and is based on the well-known adaptive processing mechanism, i.e., the so-called adaptive noise canceler (ANC) where a separate reference signal is required. The changes between measurements are considered by the input signal of ANC while the separate reference signal comes from the measurements without change. Hence, the method requires three SAR images associated with three measurements, with no changes between two of them. The reference data for the study are provided by CARABAS. The experimental results indicate that the method can reduce false alarms significantly and provide high probability of detection (≥98%). The experimental results also show that the method still works well even in the case where the flight tracks of the SAR system in the change detection measurements are slightly different.

1 August 2016 in IEEE Geoscience and Remote Sensing Letters

Journal Article

The Stability of UWB Low-Frequency SAR Images

Renato Machado

Viet Thuy Vu

Mats Ingemar Pettersson

Patrik Dammert

Hans Hellsten

This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system

13 June 2020 in Remote Sensing

Journal Article

Change Detection in UWB SAR Images Based on Robust Principal Component Analysis

Christofer Schwartz

Lucas Ramos

Leonardo Tomazeli Duarte

Marcelo Pinho

Mats Ingemar Pettersson

Renato Machado

This paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method.

3 April 2019 in IEEE Geoscience and Remote Sensing Letters

Journal Article

Rayleigh Regression Model for Ground Type Detection in SAR Imagery

Bruna Gregory Palm

Fabio Mariano Bayer

R J Cintra

Mats Ingemar Pettersson

Renato Machado

This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.

About Me

Dr. Renato Machado (M’08, SM’21) received a BSEE. degree from São Paulo State University (UNESP), Ilha Solteira, SP, Brazil, in 2001. He received an M.Sc. and PhD in Electrical Engineering from the Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil, in 2004 and 2008, respectively. He was a visiting Ph.D. scholar in the Department of Electrical Engineering, Arizona State University, Tempe, AZ, from Aug. 2006 to Jun. 2007. From November 2013 to February 2015, Dr. Machado was a visiting Research Fellow at Blekinge Institute of Technology (BTH) in partnership with Saab AB. He worked at the Nokia Institute of Technology, Brazil, in 2007 and 2008. From August 2009 to December 2017, he was an Assistant (2008–2016) and an Associate (2017) Professor at the Federal University of Santa Maria, RS, Brazil. Since December 2017, Dr. Machado has been with the Aeronautics Institute of Technology (ITA) as an Associate Professor. He is the research leader of the Digital and Signal Processing Laboratory, and SAR and Radar Signal Processing Laboratory, ITA. Dr. Machado is a Member of the Brazilian Telecommunication Society and a Senior Member of the Institute of Electrical and Electronics Engineers. His research interests include SAR Processing, SAR image processing, change detection, radar signal processing, and digital signal processing.

Prof. Renato Machado

Prof. Renato Machado

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