Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks
Modern sensor networks—those used for autonomous driving, security systems, human motion tracking, or smart city/smart factory applications—are shifting to a more centralized data processing approach to enable efficient multimodal sensor fusion for optimal environmen...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Microwaves |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9604005/ |
| _version_ | 1818942883061825536 |
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| author | Georg Korner Marcel Hoffmann Patrick Stief Mengyu Zhang Rainer Ruckert Christian Herglotz Andre Kaup Martin Vossiek |
| author_facet | Georg Korner Marcel Hoffmann Patrick Stief Mengyu Zhang Rainer Ruckert Christian Herglotz Andre Kaup Martin Vossiek |
| author_sort | Georg Korner |
| collection | DOAJ |
| description | Modern sensor networks—those used for autonomous driving, security systems, human motion tracking, or smart city/smart factory applications—are shifting to a more centralized data processing approach to enable efficient multimodal sensor fusion for optimal environment perception in complex dynamic situations. Among lidars and cameras, radars are typical for these applications, but they generate huge amounts of data, which cannot be transmitted or stored effectively in current setups. Consequently, manufacturers usually have to process the data “on sensor.” This results in transmitting only a few extracted features as point clouds or object lists to a central processing unit, which usually causes a significant loss of information. With this approach, advanced processing—such as enhancement of resolution by coherent combination of sensors or ghost target removal with advanced algorithms—is hardly possible. To overcome this, we suggest an alternative method by using signal-based compression with defined losses. The following topology will be proposed: the sensors encode raw data without prior radar-specific processing and after transmission, a central unit decodes and processes the radar data, thus benefiting from its more powerful heterogeneous processing system. We will analyze lossless compression algorithms with rate savings of about 30% to 65%, but the focus is on lossy compression algorithms that incorporate higher compression ratios by allowing negligible errors. It is shown that state-of-the-art multimedia compression algorithms can obtain rate savings of 99%, and radar specific algorithms can add a 50-fold gain on top, reaching 99.98%. To assess the distortions of compressed data, we then present different radar-specific evaluation metrics. |
| first_indexed | 2024-12-20T07:18:30Z |
| format | Article |
| id | doaj.art-a971f976d8a24ad497bcd6c13a1075fc |
| institution | Directory Open Access Journal |
| issn | 2692-8388 |
| language | English |
| last_indexed | 2024-12-20T07:18:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Microwaves |
| spelling | doaj.art-a971f976d8a24ad497bcd6c13a1075fc2022-12-21T19:48:45ZengIEEEIEEE Journal of Microwaves2692-83882022-01-0121789610.1109/JMW.2021.31197819604005Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar NetworksGeorg Korner0https://orcid.org/0000-0003-4570-1698Marcel Hoffmann1https://orcid.org/0000-0003-0131-6288Patrick Stief2https://orcid.org/0000-0001-8256-9628Mengyu Zhang3https://orcid.org/0000-0003-2287-0237Rainer Ruckert4Christian Herglotz5https://orcid.org/0000-0001-8975-0171Andre Kaup6https://orcid.org/0000-0002-0929-5074Martin Vossiek7https://orcid.org/0000-0002-8369-345XInstitute of Microwaves and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyInstitute of Microwaves and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyInstitute of Microwaves and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyInstitute of Microwaves and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyInstitute of Microwaves and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMultimedia Communications and Signal Processing, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyMultimedia Communications and Signal Processing, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyInstitute of Microwaves and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyModern sensor networks—those used for autonomous driving, security systems, human motion tracking, or smart city/smart factory applications—are shifting to a more centralized data processing approach to enable efficient multimodal sensor fusion for optimal environment perception in complex dynamic situations. Among lidars and cameras, radars are typical for these applications, but they generate huge amounts of data, which cannot be transmitted or stored effectively in current setups. Consequently, manufacturers usually have to process the data “on sensor.” This results in transmitting only a few extracted features as point clouds or object lists to a central processing unit, which usually causes a significant loss of information. With this approach, advanced processing—such as enhancement of resolution by coherent combination of sensors or ghost target removal with advanced algorithms—is hardly possible. To overcome this, we suggest an alternative method by using signal-based compression with defined losses. The following topology will be proposed: the sensors encode raw data without prior radar-specific processing and after transmission, a central unit decodes and processes the radar data, thus benefiting from its more powerful heterogeneous processing system. We will analyze lossless compression algorithms with rate savings of about 30% to 65%, but the focus is on lossy compression algorithms that incorporate higher compression ratios by allowing negligible errors. It is shown that state-of-the-art multimedia compression algorithms can obtain rate savings of 99%, and radar specific algorithms can add a 50-fold gain on top, reaching 99.98%. To assess the distortions of compressed data, we then present different radar-specific evaluation metrics.https://ieeexplore.ieee.org/document/9604005/RadarFMCW radarOFDM modulationdata compressionsource coding |
| spellingShingle | Georg Korner Marcel Hoffmann Patrick Stief Mengyu Zhang Rainer Ruckert Christian Herglotz Andre Kaup Martin Vossiek Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks IEEE Journal of Microwaves Radar FMCW radar OFDM modulation data compression source coding |
| title | Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks |
| title_full | Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks |
| title_fullStr | Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks |
| title_full_unstemmed | Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks |
| title_short | Applicability and Performance of Standard Compression Methods for Efficient Data Transmission and Storage in Radar Networks |
| title_sort | applicability and performance of standard compression methods for efficient data transmission and storage in radar networks |
| topic | Radar FMCW radar OFDM modulation data compression source coding |
| url | https://ieeexplore.ieee.org/document/9604005/ |
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