Multiple Target Measurements: Bayesian Framework for Moving Object Detection in Mimo Radar

Eisele B, Bereyhi A, Müller R (2023)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Pages Range: 1-5

Conference Proceedings Title: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Event location: Rhodes Island, Greece GR

URI: https://ieeexplore.ieee.org/document/10094649

DOI: 10.1109/ICASSP49357.2023.10094649

Abstract

Utilizing compressive sensing (CS), one can significantly reduce the number of required antenna elements in MIMO radar systems, while preserving a high spatial resolution. Most CS-based studies focus on individual processing of a single set of measurements collected from an stationary scene. In this paper, we propose a new scheme called multiple target measurements (MTM). This scheme uses the target movement to collect multiple sets of measurements from jointly sparse stationary scenes. Invoking approximate message passing, we develop a Bayesian-like iterative algorithm to recover the sparse scenes jointly. Our analytical and numerical investigations demonstrate that MTM can further reduce the array size required to achieve a desired spatial resolution.

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How to cite

APA:

Eisele, B., Bereyhi, A., & Müller, R. (2023). Multiple Target Measurements: Bayesian Framework for Moving Object Detection in Mimo Radar. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). Rhodes Island, Greece, GR.

MLA:

Eisele, Bastian, Ali Bereyhi, and Ralf Müller. "Multiple Target Measurements: Bayesian Framework for Moving Object Detection in Mimo Radar." Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece 2023. 1-5.

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