Subproject B03

Subproject B03

Compressive Sensing for Empatho-Kinaesthetic Radar Sensors

In subproject B03, we employ Compressive Sensing techniques to reduce the number of required measurements for successful reconstruction of images taken with  of Empatho-Kinaesthetic Radar Sensors. This approach yields a lower measurement time for taking an image, resulting in a higher temporal resolution.
Alternatively to reducing the measurement, one can also increase the spatial resolution without requiring additional measurements. This is possible by exploiting redundancies in the signal already in the measurement process (instead of just for image compression after the measurement), which is the key idea of Compressive Sensing. To achieve the goals of this project, we create mathematical models of the radar array near-field scenario of EmpkinS. Based on this, we develop Compressive Sensing based algorithms for highly efficient signal reconstruction. We then evaluate these algorithms in terms of the required number of measurements, reconstruction time and achieved image quality. In collaboration with other subprojects, we test and confirm our algorithms on actual radar data.

 

Contacts

 

Bastian Eisele, M. Sc.

Doctoral Candidate

 

Dr.-Ing. Ingrid Ullmann, Akad. Rat

Postdoc

 

Additional Information

  • Eisele B., Bereyhi A., Müller R., Rangan S.:
    Vector Approximate Message Passing for 3D MIMO Radar. Asilomar Conference on Signals, Systems, and Computers (2024)
  • Eisele B., Bereyhi A., Müller R.:
    Multiple Target Measurements: Bayesian Framework for Moving Object Detection in Mimo Radar. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Rhodes Island, Greece, 2023). DOI: 10.1109/ICASSP49357.2023.10094649
  • Eisele B., Bereyhi A., Ullmann I., Müller R.:
    A Novel Antenna Placement Algorithm for Compressive Sensing MIMO Radar. IEEE Radar Conference (San Antonio, 2023). DOI: 10.1109/RadarConf2351548.2023.10149680

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