Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing

Author:

Panhuber Reinhard1ORCID

Affiliation:

1. Fraunhofer FHR, Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, 53343 Wachtberg, Germany

Abstract

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in practice, fast convergence, low computational complexity, and high reconstruction performance. Although a plethora of reconstruction algorithms are available in the literature, these generally do not meet all of the aforementioned desired properties together. In this paper, a set of algorithms fulfilling these conditions is presented. The desired requirements are met by a combination of turbo-message-passing algorithms and smoothed ℓ0-refinements. Their performance is evaluated by use of extensive numerical simulations and compared with popular conventional algorithms.

Funder

Hensoldt Sensor GmbH

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference70 articles.

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5. Sun, S., Mishra, K.V., and Petropulu, A.P. (2019, January 22–26). Target Estimation by Exploiting Low Rank Structure in Widely Separated MIMO Radar. Proceedings of the 2019 IEEE Radar Conference (RadarConf), Boston, MA, USA.

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