Program
Monday, September 10
Monday, September 10 14:00 - 15:30
Tutorial - Part I
Monday, September 10 15:30 - 16:00
Coffee Break
Monday, September 10 16:00 - 17:15
Tutorial - Part II
Monday, September 10 18:00 - 20:00
Icebreaker Welcome to CoSeRa 2018
Tuesday, September 11
Tuesday, September 11 9:00 - 9:45
Welcome and Opening of CoSeRa 2018
Tuesday, September 11 9:45 - 12:00
A.1: Synthetic Aperture Radar
- Comparison of raw data based and complex image based sparse SAR imaging methods
- Low-Rank Plus Sparse Decomposition, Multi-Chromatic Analysis and Generalized Likelihood Ratio Test for Ship Weak Detection, (L+S)-MCA-GLRT
- Expansion of Dropped-Channel PolSAR CS to include a Spatial Dictionary
- Dictionary learning for multiplicative distortions with applications to SAR autofocus
- Synthetic Aperture Radar Image filtering by Unbiased Risk Estimates for Singular Value Thresholding and Multi-Chromatic-Analysis
Tuesday, September 11 12:00 - 14:00
Lunch Break
Tuesday, September 11 14:00 - 14:45
Keynote I: Sparse Reconstruction and Compressive Sensing in Earth Observation
Sparse signals are commonly expected in remote sensing and Earth observation. E.g. radar images have much less information content than the acquired raw data samples pretend. Another example is hyperspectral unmixing where only few materials are expected in a pixel compared to the prodigious endmember library. Along with the significant development of the compressive sensing theory, exploitation of sparsity in remote sensing became a very relevant and active field. Breakthroughs are brought in different remote sensing problems covering synthetic aperture radar, multispectral and hyperspectral image analysis, LiDAR and cross cutting data fusion. This talk gives a review on recent advances in sparsity exploitation in remote sensing and Earth observation, regarding the theory, applications and future trends. In particular, synthetic aperture radar tomography, image fusion and hyperspectral unmixing will be presented as highlight examples.
Tuesday, September 11 14:45 - 16:05
A.2: Inverse Synthetic Aperture Radar
- Analysis of Initial Estimate Noise in the Sparse Randomly Sampled ISAR Signals
- Wide Angle SAR imaging based on LS-CS-Residual
- A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks
- Reconstruction of Rigid Body with Noncompensated Acceleration After Micro-Doppler Removal
Tuesday, September 11 16:05 - 16:35
Coffee Break
Tuesday, September 11 16:35 - 17:55
A.3: Radar I
- Recovery Guarantees for Slow Time Phase Coded Waveforms in MIMO radar
- Multiple Carrier Agile Radar via Compressed Sensing
- 1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing
- Experimental results of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF application
Tuesday, September 11 18:00 - 22:00
Social Event: Visit of Krombacher Brewery - "Siegerländer Dreiklang"
Wednesday, September 12
Wednesday, September 12 9:00 - 9:45
Keynote II: Sampling Time Resolved Phenomena
Can we see through diffusers? Can we remove reflections when photographing through windows? Can we perform low cost bio-imaging with with game consoles (such as Microsoft Kinect)? Can we infer the geometry of blood cells from pulses of light? In the traditional sense, any imaging system produces a two-dimensional photograph. This is done by accumulating photons over time and in the process, time information is lost. On the contrary, this talk explores the idea: How can we exploit the time dimension in imaging? By considering the speed of light to be finite, the information in time-delays or echoes of light, resulting from the interaction of light and the scene, can be harnessed in unconventional ways. For example, complex, multiple scattering is often ignored or mitigated in literature, leading to applications that work with simplified environments. However, with time-resolved information at hand, one can exploit temporal features of the scene to infer scattering information. In this way, time-resolved imaging fundamentally combines time-stamped photos with computational methods to redefine a "camera." Thus, allowing one to go beyond the conventional barriers in imaging. Of course, observing physical phenomena at the speed of light requires exorbitant sampling rates. However, by exploiting structural (scene) information and using tools from harmonic analysis and sampling theory, we present case studies where a co-design of hardware and mathematical algorithms achieves state-of-art performance in applications linked with different sub-bands of the electromagnetic spectrum. This co-design philosophy also leads to new sensing paradigms. We discuss an example, the Unlimited Sensing Framework, which allows for high-dynamic-range sensing from low-dynamic-range measurements.
Wednesday, September 12 9:45 - 12:00
A.4: Optical Sensing
- Single-pixel real-time video imaging with closed-form single-step image reconstruction
- Coffee Break
- Evaluation of using coded aperture imaging in the mid- and far-infrared region
- Compressive Nonlinear Frequency Modulated CW LIDAR
- Analysis of masks for compressed acquisitions in variational-based pansharpening
- Compressive sensing analog-to-information system based on optical speckle
Wednesday, September 12 12:00 - 14:00
Lunch Break
Wednesday, September 12 14:00 - 14:45
Keynote III: Blind deconvolution with randomness - convex geometry and algorithmic approaches
Blind deconvolution problems are ubiquitous in many areas of imaging and technology and have been the object of study for several decades. Recently, motivated by the theory of compressed sensing, a new viewpoint has been introduced, motivated by applications in wireless application, where a signal is transmitted through an unknown channel. Namely, the idea is to randomly embed the signal into a higher dimensional space before transmission. Due to the resulting redundancy, one can hope to recover both the signal and the channel parameters. In this talk we give an overview over recent progress on recovery guarantees for this problem. On the one hand, we will discuss convex approaches based on lifting as they have first been studied by Ahmed et al. (2014). We show that one encounters a fundamentally different geometric behavior as compared to generic bilinear measurements. On the other hand we will review recent progress on the study of efficient nonconvex recovery methods and present a nonconvex approach with provable local convergence guarantees under sparsity assumptions. This talk is based on joint works with Jakob Geppert (University of Göttingen), Peter Jung (TU Berlin), Kiryung Lee (Ohio State University), Justin Romberg (GeorgiaTech), and Dominik Stöger (TUM).
Wednesday, September 12 14:45 - 15:45
A.5: THz Sensing and Structural Monitoring
Wednesday, September 12 15:45 - 16:15
Coffee Break
Wednesday, September 12 16:15 - 17:55
A.6: Radar II
- Through the wall Target Detection/Monitoring from Compressively Sensed Signals via Structural Sparsity
- Relevant Vector Identification using Matrix Extension for Anisotropic SFCW Radar
- Resolution Analysis of Compressive Data Acquisition
- Reducing Radar Energy Consumption in Classification Tasks through the use of Compressed Sensing
- High Resolution Range Profiling for Stepped Radar via Sparsity Exploitation
Wednesday, September 12 18:00 - 20:00
A.7: Poster Session
- Fast Binary Compressive Sensing via ℓ0 Gradient Descent
- Focal Plane Speckle Patterns for Compressive Microscopic Imaging in Laser Spectroscopy
- A low cost non-imaging system for standoff threat detection
- Ground Clutter Processing for Airborne Radar in a Compressed Sensing Context
- Fast Multipath Estimation for PMD Sensors
- 1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing
- A Novel Inverse Synthetic Aperture Radar Imaging Method Using Convolutional Neural Networks
Thursday, September 13
Thursday, September 13 9:00 - 9:45
Keynote IV: Sub-Nyquist and Cognitive Radars
The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Consequently, conversion to digital has become a serious bottleneck. The Nyquist theorem also results in a large number of elements in antenna arrays and in wide bandwidths in applications requiring high resolution. In this talk we consider a general framework for sub-Nyquist radar in space, time and frequency which allows to dramatically reduce the number of antenna elements, sampling rates and band occupancy. Sub-Nyquist radars break the link between common radar design trade-offs such as range resolution and transmit bandwidth; dwell time and Doppler resolution; spatial resolution and number of antenna elements; continuous-wave radar sweep time and range resolution. We then show that they also pave the way for cognitive radars which share their transmit spectrum with other communication services, thereby providing a robust solution for coexistence in spectrally crowded environments. Finally, we present state-of-the-art hardware prototypes that demonstrate the real-time feasibility of sub-Nyquist radars.