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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2025 16th German Microwave Conference (GeMiC) s. 411 - 414
Hlavní autori: Ruckert, Rainer, Herglotz, Christian, Sura, Oliver, Ninov, Youliy, Kaup, Andre, Vossiek, Martin
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Institut fur Mikrowellen und Antennentechnik - IMA 17.03.2025
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
DOI:10.23919/GeMiC64734.2025.10979136