Combining Run-Length Encoding Preprocessing With Lempel-Ziv-Markov Algorithm to Enhance Compression for Automotive Radar Data

The integration of high-resolution radars in vehicles with central processing systems has significantly increased data volume in sensor networks. To address this issue, researchers have investigated radar data compression methods designed to reduce information loss, conserve resources, and improve p...

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Published in:2025 16th German Microwave Conference (GeMiC) pp. 411 - 414
Main Authors: Ruckert, Rainer, Herglotz, Christian, Sura, Oliver, Ninov, Youliy, Kaup, Andre, Vossiek, Martin
Format: Conference Proceeding
Language:English
Published: Institut fur Mikrowellen und Antennentechnik - IMA 17.03.2025
Subjects:
Buildings
Compression algorithms
data compression
data handling
data processing
Encoding
Heuristic algorithms
Huffman coding
LZMA
Microwave theory and techniques
Noise
Quantization (signal)
Radar
Radar imaging
RLE
Vehicle dynamics
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Abstract The integration of high-resolution radars in vehicles with central processing systems has significantly increased data volume in sensor networks. To address this issue, researchers have investigated radar data compression methods designed to reduce information loss, conserve resources, and improve processing efficiency. These methods include lossy preprocessing and quantization. This paper focuses on a data stream generated by Run-Length Encoding (RLE), which is adapted for complex radar data with a dynamic counter length. Following this, the data stream is further compressed using either the Lempel-Ziv-Markov Algorithm (LZMA) directly or a combination of Huffman coding before LZMA. We compare the compression ratio achieved by RLE alone, RLE with Huffman coding, RLE with LZMA, and RLE with Huffman coding followed by LZMA. Our methods achieve average compression ratios of up to 48500 on actual radar data.
AbstractList The integration of high-resolution radars in vehicles with central processing systems has significantly increased data volume in sensor networks. To address this issue, researchers have investigated radar data compression methods designed to reduce information loss, conserve resources, and improve processing efficiency. These methods include lossy preprocessing and quantization. This paper focuses on a data stream generated by Run-Length Encoding (RLE), which is adapted for complex radar data with a dynamic counter length. Following this, the data stream is further compressed using either the Lempel-Ziv-Markov Algorithm (LZMA) directly or a combination of Huffman coding before LZMA. We compare the compression ratio achieved by RLE alone, RLE with Huffman coding, RLE with LZMA, and RLE with Huffman coding followed by LZMA. Our methods achieve average compression ratios of up to 48500 on actual radar data.
Author Sura, Oliver
Vossiek, Martin
Herglotz, Christian
Ruckert, Rainer
Ninov, Youliy
Kaup, Andre
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  surname: Ruckert
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  organization: Friedrich-Alexander-Universität Erlangen-Nürnberg,Germany
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  givenname: Christian
  surname: Herglotz
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  organization: Brandenburgische Technische Universität Cottbus-Senftenberg,Germany
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  givenname: Oliver
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  givenname: Youliy
  surname: Ninov
  fullname: Ninov, Youliy
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  organization: GPP Communication GmbH & Co. KG,Germany
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  givenname: Andre
  surname: Kaup
  fullname: Kaup, Andre
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  organization: Friedrich-Alexander-Universität Erlangen-Nürnberg,Germany
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  givenname: Martin
  surname: Vossiek
  fullname: Vossiek, Martin
  email: martin.vossiek@fau.de
  organization: Friedrich-Alexander-Universität Erlangen-Nürnberg,Germany
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Snippet The integration of high-resolution radars in vehicles with central processing systems has significantly increased data volume in sensor networks. To address...
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StartPage 411
SubjectTerms Buildings
Compression algorithms
data compression
data handling
data processing
Encoding
Heuristic algorithms
Huffman coding
LZMA
Microwave theory and techniques
Noise
Quantization (signal)
Radar
Radar imaging
RLE
Vehicle dynamics
Title Combining Run-Length Encoding Preprocessing With Lempel-Ziv-Markov Algorithm to Enhance Compression for Automotive Radar Data
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