38,697 research outputs found
DEVELOPMENT OF SETUP FOR ON-WAFER PULSE-TO-PULSE STABILITY CHARACTERIZATION OF GAN HEMT TRANSISTOR IN KU-BAND
International audienceWe report on the development of a test bench to extract pulse-to-pulse (P2P) stability On-Wafer in Ku-band. The P2P stability is crucial for RADAR performances. GaN HEMT transistors are a promising candidate for RADAR application. However, they typically generate trapping effects, which can strongly affect the P2P stability. Two methods RMS and Standard Deviation based on temporal analysis are employed to extract the stability indicators. The main idea of the P2P test bench is the use of a homodyne demodulation to recover the envelop of the RF. This setup is also combined to an active load pull towards P2P stability test bench dedicated to the new generation of GaN HEMT transistors in large signal condition close to their operational mode
MIMO Radar Waveform Optimization With Prior Information of the Extended Target and Clutter
The concept of multiple-input multiple-output (MIMO) radar allows each transmitting antenna element to transmit an arbitrary waveform. This provides extra degrees of freedom compared to the traditional transmit beamforming approach. It has been shown in the recent literature that MIMO radar systems have many advantages. In this paper, we consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. Numerical results show that the proposed methods have better SINR performance than existing design methods
Doppler Spectrum Estimation by Ramanujan Fourier Transforms
The Doppler spectrum estimation of a weather radar signal in a classic way
can be made by two methods, temporal one based in the autocorrelation of the
successful signals, whereas the other one uses the estimation of the power
spectral density PSD by using Fourier transforms. We introduces a new tool of
signal processing based on Ramanujan sums cq(n), adapted to the analysis of
arithmetical sequences with several resonances p/q. These sums are almost
periodic according to time n of resonances and aperiodic according to the order
q of resonances. New results will be supplied by the use of Ramanujan Fourier
Transform (RFT) for the estimation of the Doppler spectrum for the weather
radar signal
Change detection in SAR time-series based on the coefficient of variation
This paper discusses change detection in SAR time-series. Firstly, several
statistical properties of the coefficient of variation highlight its pertinence
for change detection. Then several criteria are proposed. The coefficient of
variation is suggested to detect any kind of change.
Then other criteria based on ratios of coefficients of variations are
proposed to detect long events such as construction test sites, or point-event
such as vehicles.
These detection methods are evaluated first on theoretical statistical
simulations to determine the scenarios where they can deliver the best results.
Then detection performance is assessed on real data for different types of
scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative
evaluation is performed with a comparison of our solutions with
state-of-the-art methods
Innovative observing strategy and orbit determination for Low Earth Orbit Space Debris
We present the results of a large scale simulation, reproducing the behavior
of a data center for the build-up and maintenance of a complete catalog of
space debris in the upper part of the low Earth orbits region (LEO). The
purpose is to determine the performances of a network of advanced optical
sensors, through the use of the newest orbit determination algorithms developed
by the Department of Mathematics of Pisa (DM). Such a network has been proposed
to ESA in the Space Situational Awareness (SSA) framework by Carlo Gavazzi
Space SpA (CGS), Istituto Nazionale di Astrofisica (INAF), DM, and Istituto di
Scienza e Tecnologie dell'Informazione (ISTI-CNR). The conclusion is that it is
possible to use a network of optical sensors to build up a catalog containing
more than 98% of the objects with perigee height between 1100 and 2000 km,
which would be observable by a reference radar system selected as comparison.
It is also possible to maintain such a catalog within the accuracy requirements
motivated by collision avoidance, and to detect catastrophic fragmentation
events. However, such results depend upon specific assumptions on the sensor
and on the software technologies
Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map
This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations
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