Probabilistic SAR Processing for High-Resolution Mapping Using Millimeter-Wave Radar Sensors

In the field of autonomous driving, highly accurate representations of the environment are essential for trajectory planning as well as for estimating the vehicle's location. Today, this can be achieved with the help of chirp-sequence radar sensors or radar sensor networks. The possibilities fo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2023-10, Vol.59 (5), p.1-16
Main Authors: Grebner, Timo, Grathwohl, Alexander, Schoeder, Pirmin, Janoudi, Vinzenz, Waldschmidt, Christian
Format: Article
Language:English
Subjects:
ADAS
Algorithms
Amplitudes
Chirp
chirp-sequence radar sensors
gird-mapping
High resolution
Image resolution
Mapping
millimeter wave
Millimeter waves
Occupancy
occupancy grid-map
Probabilistic logic
probabilistic mapping
Probability theory
Radar
Radar imaging
Radar measurements
radar sensor networks
SAR
Sensors
Synthetic aperture radar
Target detection
Time-domain analysis
Trajectory planning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the field of autonomous driving, highly accurate representations of the environment are essential for trajectory planning as well as for estimating the vehicle's location. Today, this can be achieved with the help of chirp-sequence radar sensors or radar sensor networks. The possibilities for environmental mapping cover simple point clouds, target list based grid maps and raw data based high resolution synthetic aperture radar (SAR) maps. While for target list based grid maps it has already been shown that a probabilistic occupancy grid map has significant advantages over an amplitude-based grid map in terms of robustness and resolution, no probabilistic approaches to SAR processing exist up to now. This paper presents a fundamental approach of processing radar raw data to generate high-resolution SAR images based on probabilities. A probabilistic SAR processing is presented which combines high resolution environmental mapping with amplitude independent target detection. Based on measurements, a qualitative and quantitative comparison between conventional amplitude and phase-based SAR processing, the presented probabilistic SAR processing, and a probabilistic target list-based occupancy grid map is performed. Since the presented algorithm is not limited to the automotive field and chirp-sequence radar sensors, it can be extended to arbitrary SAR applications and radar architectures.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2023.3289784