DOA estimation for uncorrelated and coherent signals based on Fourth-order Cumulants
This work aims to estimate the Direction-of-arrival (DOA) of uncorrelated and coherent signals based on Fourth-order Cumulant (FOC) and sparse reconstruction method. Firstly, a subspace method based on FOC matrix is proposed for estimating the DOAs of the uncorrelated signalsthen combining the FOC matrix of the coherent signals with sparse reconstruction theory, we obtain the DOAs information of the coherent signals by mapping a sparse vector. Simulation results are provided to demonstrate that our proposed method is not sensitive to the non-Gaussian signals and colored noise.
Leveraging grid computing technology, i.e. the visualization of distributed computing and data resources such as processing, network bandwidth and storage capacity to create a single system image, we present a Grid Air Quality Forecast System (G-AQFS). The modeling system consists of meteorological and dispersion models coupled in cascade. The computational workflow of the modeling system is defined by means of DAGs (direct acyclic graph). A simple system is presented to manage and schedule the computational grid resources. As case study the system has been applied over Salento area, in the Apulia region (South-eastern Italy), to simulate ground level ozone concentration. Model predictions have been compared with field measurements, with reasonable results.