Work

Effort Considerations of Compressed Sensing for Automotive Radar

Year: 2019

Type: conference-paper

Abstract: The application of iterative compressed sensing algorithms for automotive radar is often considered as too complex for real-time evaluation. In this paper, it is shown that the number of required iterations can be chosen considerable low. To determine the necessary steps, a quality criterion is evaluated. The two examined scenarios are a reduced data rate and an interference mitigation. Measurement results are shown for different numbers of iterations to verify the sufficient reconstruction capabilities.

Source: IEEE Radio and Wireless Symposium (RWS)

DOI: 10.1109/rws.2019.8714419

Authors: Fabian Roos, Philipp Hügler, Jonathan Bechter, Muhammad Ahsan Razzaq, Christina Knill, Nils Appenrodt, Jürgen Dickmann, Christian Waldschmidt

Institutions: Universität Ulm, Daimler (Germany)

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2916734530

FWCI: 1.061

Cites: 12

Cited by: 7

Related to: 10