Scope

Statistical Signal Processing is an approach which treats signals as stochastic processes and uses their statistical properties to perform signal processing tasks. Such techniques are widely used in signal processing applications, involving detection, estimation and prediction, blind source separation, and how statistical properties of different component signals are exploited to extract each of the component signals.

Areas covered by this specialty section include, but are not limited to:

• Detection and Parameter Estimation

• Non-Linear Filtering and Tracking Algorithms

• Classification Methods, Pattern Recognition, Non-Parametric Methods

• Performance Analysis and Bounds

• Statistical Signal and System Modeling and Analysis

• Spectral Analysis and Spectral Estimation

• Nonstationary and Cyclo-Stationary Statistical Signal Processing

• Deconvolution

• Signal Separation and Restoration

• Non-Gaussian Signals and Noise, Higher-Order Statistical Methods

• Hierarchical Models and Tree- or Graph-Structured Signal Processing

• (Empirical, Variational, …) Bayesian Techniques, Message Passing, Belief Propagation

• Adaptive Systems and Machine Learning

• Data Fusion, Distributed and Networked Systems

• Sparsity Aware Processing and Compressive Sensing

• Sparse/Low-Dimensional/Low-Rank Signal, Matrix and Tensor recovery

• Dictionary Learning, Subspace and Manifold Learning

• Source Localization

Application areas include

• Data Science

• Bioinformatics and Genomics, Medical Imaging and Biomedical Signal Processing

• Communication Systems and Networks, Internet of Things and Sensor Networks

• Sensor Array Systems, Radar and Sonar

• Smart Grids, Power Systems and Industrial Applications

• Information Forensics and Security, Social Networks

• Geoscience and Seismology, Astrophysics

• Financial Signal Processing

• Quantum Signal Processing

Statistical Signal Processing is a very classical and significant area of signal processing, submissions in all formats/styles including full-transactions papers (original research/technology and code), perspective/vision papers, tutorials/surveys (reviews), magazines (mini review), letters (brief research report), as well as replies and comments are encouraged. All studies must contribute insights into the fundamental aspects and applications of Statistical Signal Processing.

Submission

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