Organization of the Special Session "Sparsity in Estimation" at the IEEE Statistical Signal Processing Workshop (SSP 2018)
Sprache des Titels:
Englisch
Original Kurzfassung:
Exploiting sparsity is an important principle in modern estimation. It enables to utilize measurement information efficiently, allowing for an excellent estimation performance even in low SNR scenarios. Sparsity-aware signal processing techniques entered fields such as digital communications, radar, acoustics or image processing. With the development of efficient algorithms for sparsity-aware estimation the application space is further growing, and complexity reducing approaches pave the way for real time implementations and industrial applications. Today, sparsity is not only relevant on an algorithmic level, but also on a system design level, e.g. for finding an optimal sparse placement of sensors to perform cooperative estimation in a sensor network.
The presented works in this session will cover topics such as:
? Adaptive Compressed Sensing and Sparsity-Aware Techniques
? Low Complexity Sparsity-Aware Estimation
? Threshold Functions for Sparse Signal Processing
? Sparsity in Sensor Networks
? Sparsity in Direction of Arrival Estimation
? Sparsity-Aware Signal Processing for Future Communication Systems
? Sparsity and Basis Functions
? Compressive Data Acquisition
Forschungs-