Weather radar data processing on graphic cards

Weather radar operation generates data at a high rate that requires prompt processing. The operations performed on data for weather product generation are repeated in each resolution cell and thus are naturally prone to parallelization. Parallel processing using graphic cards is an emerging technology that allows for implementation of high-throughput algorithms at a low cost. In this paper, the parallel implementation of the main product of a polarimetric weather radar using GPU is presented, focusing on its optimization. A speedup exceeding 20 ×× is obtained when compared to the serial implementation. Also processing is found to be memory bound, which results in a counter-intuitive performance improvement when the number of threads per job is reduced.Fil: Denham, Mónica Malen. Universidad Nacional de Rio Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lamperti, Enrico. Universidad Nacional de Rio Negro. Sede Andina; ArgentinaFil: Areta, Javier Alberto. Universidad Nacional de Rio Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Adaptive spectral processing algorithm for staggered signals in weather radars

A spectral algorithm for processing staggered-pulse repetition time (SPRT) signals in weather radar is introduced. It includes new approaches for ground clutter filter and hydrometeor spectral moments estimation. The algorithm uses ideas similar to GMAP but applied to non-uniform sampled signals. This work is focused on staggered sequences 2/3, but can be extended to other staggered sequences. Monte Carlo experiments were used to evaluate the performance of the spectral moments estimators for simulated weather signal, in scenarios with and without the presence of ground clutter. When clutter is present, a study using different clutter-to-signal ratios was carried out, showing that the method can deal with a wide range of situations and is appropriate for implementation in real scenarios. A comparison against GMAP-TD was performed, showing similar estimation results for both algorithms and a fivefold processing speed improvement for the proposed method. The performance was also validated using real weather data RMA-12 from a radar located in San Carlos de Bariloche, Argentina. The proposed algorithm has an easy implementation and is a good candidate for real-time implementations.Fil: Collado Rosell, Arturo. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Universidad Nacional de Cuyo; ArgentinaFil: Pascual, Juan Pablo. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Universidad Nacional de Cuyo; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Areta, Javier Alberto. Universidad Nacional de Río Negro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentin

Synthetic aperture radar signal processing in parallel using GPGPU

In this work an efficient parallel implementation of the Chirp Scaling Algorithm for Synthetic Aperture Radar processing is presented. The architecture selected for the implementation is the general purpose graphic processing unit, as it is well suited for scientific applications and real-time implementation of algorithms. The analysis of a first implementation led to several improvements which resulted in an important speed-up. Details of the issues found are explained, and the performance improvement of their correction explicitly shown.Instituto de Investigación en InformáticaComisión de Investigaciones Científicas de la provincia de Buenos Aire

Synthetic aperture radar signal processing in parallel using GPGPU

In this work an efficient parallel implementation of the Chirp Scaling Algorithm for Synthetic Aperture Radar processing is presented. The architecture selected for the implementation is the general purpose graphic processing unit, as it is well suited for scientific applications and real-time implementation of algorithms. The analysis of a first implementation led to several improvements which resulted in an important speed-up. Details of the issues found are explained, and the performance improvement of their correction explicitly shown.Fil: Denham, Mónica Malen. Universidad Nacional de Rio Negro. Sede Andina. Departamento de Ciencias Exactas, Naturales y de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Areta, Javier Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Rio Negro. Sede Andina. Departamento de Cs.exactas, Naturales y de Ingeniería. Area de Ingeniería Electronica; ArgentinaFil: Tinetti, Fernando Gustavo. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Multipulse Processing Algorithm for Improving Mean Velocity Estimation in Weather Radar

In this article, we present a novel algorithm termed multipulse processing (MPP) for improving mean Doppler velocity estimation in weather radar applications. It can be used for both staggered pulse repetition time (PRT) and uniform-PRT sequences. Essentially, MPP consists of finding a particular zero of a functional composed of data autocorrelation estimates at multiple lags. To select the proper zero, an initial Doppler velocity estimate is required. Therefore, MPP can be considered as an estimation refinement stage. Its advantage lies in the fact that it uses the complete information contained in the radar signal autocorrelation. After a theoretical analysis, we compare the performance of MPP against other well-established methods of similar complexity and the Cramér-Rao lower bound, by means of Monte Carlo simulations using synthetic data. We show that the proposed estimator offers the lowest root-mean-square error (RMSE) at low signal-to-noise ratio (SNR) situations for a wide range of spectral widths. Finally, we evaluate the MPP algorithm performance using real data measured by the RMA Argentinian weather radar. The results of tests performed are consistent with those of Monte Carlo simulations and validate the proposed method.Fil: Pascual, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; ArgentinaFil: Cogo, Jorge. Universidad Nacional de Río Negro; ArgentinaFil: Collado Rosell, Arturo. Comisión Nacional de Energía Atómica; ArgentinaFil: Areta, Javier Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Río Negro; Argentin

Polarimetric SAR Image Segmentation using CEM Algorithm

In this work we perform Synthetic Aperture Radar (SAR) polarimetric images segmentation based on the Classification-Expectation-Maximization (CEM) method, with both supervised and unsupervised initialization. In the former case, the algorithm is randomly initialized with the number of classes as the only initial information, while in the unsupervised case initialization is based on a previous classification. Real EMISAR Single-Look-Complex (SLC) data are used, with Mixing Gaussian model. Results are compared with those obtained by Wishart unsupervised classification method, which is a well-known and widely used method for radar image classification. Finally, Da-vies-Bouldin index is applied for quantitative comparison be-tween the obtained segmentations, and for studying the CEM method performance.Fil: Fernández Michelli, Juan Ignacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hurtado, Martin. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Areta, Javier Alberto. Universidad Nacional de Rio Negro. Sede Andina. Departamento de Ciencias Exactas, Naturales y de Ingenieria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muravchik, Carlos Horacio. Universidad Nacional de la Plata. Facultad de Ingeniería. Departamento de Electrotecnia; Argentin

Non-linear Kalman filters comparison for generalised autoregressive conditional heteroscedastic clutter parameter estimation

In this work, the authors analyse the estimation of the generalised autoregressive conditional heteroscedastic (GARCH) process conditional variance based on three non-linear filtering approaches: extended Kalman filter (EKF), unscented Kalman filter and cubature Kalman filter. The authors present a state model for a GARCH process and derive an EKF including second-order non-linear terms for simultaneous estimation of state and parameters. Using synthetic data, the authors evaluate the consistency and the correlation of the innovations for the three filters, by means of numerical simulations. The authors also study the performance of smoothed versions of the non-linear Kalman filters using real clutter data in comparison with a conventional quasi-maximum likelihood estimation method for the GARCH process coefficients. The authors show that with all methods the process coefficients estimates are of the same order and the resulting conditional variances are commensurable. However, the non-linear Kalman filters greatly reduce the computational load. These kind of filters could be used for the radar detector based on a GARCH clutter model that uses an adaptive threshold that demands the conditional variance at each decision instant.Fil: Pascual, Juan Pablo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Von Ellenrieder, Nicolás. McGill University; CanadáFil: Areta, Javier Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Río Negro; ArgentinaFil: Muravchik, Carlos Horacio. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentin

Image Classification by means of CEM Algorithm based on a GARCH-2D data model

The aim of synthetic aperture radar (SAR) classification is to assign each pixel to a class according to a feature of the illuminated area. In this work, a classification method suitable for SAR images is presented through the maximum a posteriori (MAP) criteria by means of the expectation-maximization (EM) algorithm based on a mixture of GARCH-2D processes data model. This model assumes that the data probability density function (pdf) is a combination of a finite number of pdf's of GARCH-2D processes, that represent the pixel classes and whose parameters are estimated iteratively by means of the EM algorithm. Once the parameter estimation is performed, the a-posteriori probability of each pixel belonging to each class is computed and the classification is performed through the MAP criteria. Based on this model, the expressions for estimation and classification procedures are derived. Finally, the method performance is verified through a numeric example for a particular case and a comparison is performed between this approach and a variant of the classification algorithm based on a Gaussian mixture model for the data pdf.Fil: Pascual, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Fernández Michelli, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Von Ellenrieder, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Hurtado, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Areta, Javier Alberto. Universidad Nacional de Río Negro; ArgentinaFil: Muravchik, Carlos Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas; Argentin