Real Acquisition System Research Articles

Cross Sensor Simulation of Tomographic SAR Stacks

This paper presents an algorithm for simulating tomographic synthetic aperture radar (SAR) data based on another stack actually gathered by a real acquisition system. Through the procedure here proposed, the simulated system can be evaluated according to its capability to image complex natural media rather than reference point targets. This feature is particularly important whenever the biophysical properties of the target of interest must be preserved and cannot be easily modeled. The system to be simulated may be different from the original one concerning resolution, off-nadir angles, bandwidth and central frequency. The algorithm here proposed handles these differences by properly taking into account the wavenumbers of the target illuminated by the real survey and requested by the simulated one. The complex images constituting the synthetic stack are associated with the effective vertical interferometric wavenumber peculiar of the geometry to be simulated, regardless of the original data. Furthermore, the three-dimensional resolution cell of the simulated tomographic system is consistent with the simulated geometry concerning size and spatial orientation. These two latter features cannot be guaranteed by simply filtering the original stack. The simulator here proposed has been used to simulate the tomographic stack expected from the forthcoming European Space Agency (ESA) BIOMASS mission. The relationship between baseline distribution and 3D focusing capability was explored; special attention has been paid to the robustness of tomographic power at being a good proxy for the above ground biomass in tropical regions.

Performance analysis of a two-stage spectrum sensing scheme for dynamic spectrum access in TV bands

Spectrum Sensing schemes in Cognitive Radio systems are fundamental elements for a complete opportunistic communication technology. They usually reveal the presence of occupied bands without performing a measurement of the spectrum portion actually occupied. Considering that the final aim of Cognitive Radios is the exploitation, in the most efficient way, of the spectrum holes left free by primary users, the measurement of the actual occupied spectrum becomes a fundamental task. In this framework, this paper proposes a two-stage spectrum sensing method, tailored to work in TV bands, offering a good detection of the occupied bands with selectable false alarm rates (Stage 1) and an accurate measurement of the real occupied spectrum (Stage 2). Performance results, achieved in simulation and emulation environments, prove the method’s goodness and robustness to non-idealities of real acquisition systems. A comparison, with another two-stage method available in literature, has confirmed its efficacy in real contexts.

An Efficient Distributed Compressed Sensing Algorithm for Decentralized Sensor Network.

We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems. The proposed DCSMP algorithm can be divided into two independent parts, the common and innovation support set estimation processes. The goal of the common support set estimation process is to obtain an estimated common support set by fusing the candidate support set information from an individual node and its neighboring nodes. In the following innovation support set estimation process, the measurement vector is projected into a subspace that is perpendicular to the subspace spanned by the columns indexed by the estimated common support set, to remove the impact of the estimated common support set. We can then search the innovation support set using an orthogonal matching pursuit (OMP) algorithm based on the projected measurement vector and projected sensing matrix. In the proposed DCSMP algorithm, the process of estimating the common component/support set is decoupled with that of estimating the innovation component/support set. Thus, the inaccurately estimated common support set will have no impact on estimating the innovation support set. It is proven that under the condition the estimated common support set contains the true common support set, the proposed algorithm can find the true innovation set correctly. Moreover, since the innovation support set estimation process is independent of the common support set estimation process, there is no requirement for the cardinality of both sets; thus, the proposed DCSMP algorithm is capable of tackling the unknown sparsity problem successfully.

An Analysis of Block Sampling Strategies in Compressed Sensing

Compressed sensing is a theory which guarantees the exact recovery of sparse signals from a small number of linear projections. The sampling schemes suggested by current compressed sensing theories are often of little practical relevance, since they cannot be implemented on real acquisition systems. In this paper, we study a new random sampling approach that consists of projecting the signal over blocks of sensing vectors. A typical example is the case of blocks made of horizontal lines in the 2-D Fourier plane. We provide the theoretical results on the number of blocks that are sufficient for exact sparse signal reconstruction. This number depends on two properties named intra- and inter-support block coherence. We then show that our bounds coincide with the best so far results in a series of examples, including Gaussian measurements or isolated measurements. We also show that the result is sharp when used with specific blocks in time-frequency bases, in the sense that the minimum required amount of blocks to reconstruct sparse signals cannot be improved up to a multiplicative logarithmic factor. The proposed results provide a good insight on the possibilities and limits of block compressed sensing in imaging devices, such as magnetic resonance imaging, radio-interferometry, or ultra-sound imaging.

Desenvolvimento de um tomógrafo ultrassônico para simulação do monitoramento dinâmico de injeção de CO2 em reservatórios de hidrocarbonetos

O principal objetivo deste artigo é apresentar o desenvolvimento de um tomógrafo ultrassônico que simula o monitoramento dinâmico da injeção de CO2 em um reservatório de hidrocarbonetos. É proposto o uso de tomografia ultrassônica para detectar mudanças na velocidade do som causadas por um corpo de prova dentro de um meio com escala reduzida, visando simular o monitoramento dinâmico em campo, uma vez que a injeção de CO2 também causa mudanças na velocidade do som no reservatório de hidrocarbonetos. O sistema de aquisição real simulado por este tomógrafo é composto por fontes acústicas espaçadas uniformemente ao longo de um poço aproximadamente vertical em paralelo a outro poço, perfurados em um campo de exploração de hidrocarbonetos, onde os receptores são dispostos uniformemente. O sinal acústico se propaga a partir de cada fonte e percorre o meio entre os poços até atingir os receptores no outro poço. A partir dos tempos de trânsito do sinal registrado nos receptores, pode-se estimar a velocidade de propagação do meio entre fontes e receptores. A imagem do subsolo é então estimada utilizando-se algoritmos de tomografia sísmica. Esta imagem é uma representação gráfica do campo de velocidades e permite identificar os contornos da pluma do CO2 injetado em reservatórios de hidrocarbonetos. Assim, pode-se automatizar o monitoramento contínuo da injeção de CO2 de modo a preservar a segurança da operação de campo contra vazamentos de CO2 indesejados e potencialmente letais.