5th CoSeRa 2018

Following the success of the previous editions of the workshop on compressive sensing applied to radar, we are pleased to announce the 5th International Workshop on Compressed Sensing applied to Radar, Multimodal Sensing,and Imaging (CoSeRa 2018). It will be held in Siegen (Germany) on 10.-13. September 2018. The aim of CoSeRa is to bring experts of Compressive Sensing (CS) and Radar, Sensor, and Imaging processing together to explore the state-of-the-art in development of CS techniques for different areas of applications and to turn out its advantages or possible drawbacks compared to classical solutions.

Topics of interest include (but are not limited to):

  • Fundamentals, Mathematical Aspects, Concepts and Algorithms of Compressed Sensing, with applications to:
    • + Radar systems (target detection, GMTI,…)
    • + Imaging radar (SAR, ISAR),
    • + THz imaging and material analysis,
    • + Digital optics, time of flight imaging, hyperspectral imaging,
    • + Systems for medical diagnostics (CT, MRT, X-ray, ultrasonography),
    • + X-ray crystallography,
    • + Acoustic systems, microphone arrays, SONAR systems,
    • + Radio astronomy

  • Classification based on compressed sensing
    • + Sparse modeling
    • + Physical modeling, e.g. based on electromagnetic theory
    • + Dictionary learning
  • System design and hardware for compressed sensing
    • + Analog to information converters
    • + Sampling strategies, sparse array design

  • Random and optimum sensing matrices Structured compressed sensing for sensor systems
    • + Block-sparsity, group-sparsity, joint sparsity

  • Application of matrix decomposition and tensor-based techniques for sensor systems
    • + Blind deconvolution, blind calibration
    • + Separation of foreground and background, change detection

  • Quality evaluation
    • + Estimation based on compressed sensing and the CRB
    • + Evaluation of super-resolution
    • + Off-grid evaluation
    • + Detection probabilities

Radar/SAR awaits Compressed Sensing
Compressed sensing (CS) techniques offer a mathmatical framework for the detection and allocation of sparse signals with a reduced number of samples. Today, modern radar systems operate with high bandwidths - demanding high sample rates according to the Shannon-Nyquist theorem - and a huge number of single elements for phased array antennas.
Often only a small amount of target parameters is the final output, raising the question, whether CS could be a good means to reduce data size, complexity, weight, power consumption and costs of radar systems. The amount of publications addressing the application of CS to radar is still limited, leaving open a number of questions.