2018-2019 Matlab Projects using Digital Signal Processing(DSP)
2.Alternating Iteratively Reweighted Least Squares Minimization for Low-Rank Matrix Factorization
3.Joint Channel Estimation and User Grouping for Massive MIMO Systems
4.New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images
5.Bounding Multivariate Trigonometric Polynomials
DSP PROJECTS USING MATLAB
6.Tensor Decomposition for Signal Processing and Machine Learning
7.Variational Mode Decomposition
DSP PROJECTS USING MATLAB
8.Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems
Abstract: This paper explores the problem of change detection in time series of heterogeneous multivariate synthetic aperture radar images.DSP PROJECTS USING MATLAB Classical change detection schemes have modelled the data as a realisation of Gaussian random vectors and have derived statistical tests under this assumption. However, when considering high-resolution images, the heterogeneous behaviour of the scatterers is not well described by a Gaussian model. In this paper, the data model is extended to Spherically Invariant Random Vectors where the heterogeneity of the images is accounted for through a deterministic texture parameter. MATLAB PROJECTS USING DSPThen three separate detection problems are considered and generalised likelihood ratio test technique is used to derive statistical tests for each problem. The constant false alarm rate property of the new statistics are studied both theoretically and through simulation. Finally, the performance of the new statistics are studied both in simulation and on real synthetic aperture radar data and compared to Gaussian-derived ones. The study yields promising results when the data are heterogeneous.