Situation-aware Space-time Waveform Design for Automotive MIMO Radars

Radar is a key technology in automotive driving for target detection and perception. In this work, we leverage prior environmental information in the form of occupancy maps to design space-time codes for a fully digital MIMO radar. We transform this design problem into the optimization of spatial beamforming gains and time-domain codes. The beamforming gains are optimized to enhance the strength of returns from cells associated with a higher uncertainty of occupancy. The time-domain codes are optimized to minimize the correlation between returns of targets within the drivable space. We validate our method on the nuScenes dataset to show that the designed space-time codes achieve higher detection rates than designs that do not rely on prior information from occupancy maps.

Authors

Institutions

Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10890749
Citations
1
Primary Topic
Radar Systems and Signal Processing
Type
article
Field-Weighted Citation Impact
4.99
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Situation-aware Space-time Waveform Design for Automotive MIMO Radars

Geethu Joseph, Nitin Jonathan Myers, Ashish Pandharipande, Edoardo Focante
1 citations
Radar Systems and Signal Processing
4.99
article

Situation-aware Space-time Waveform Design for Automotive MIMO Radars

Geethu Joseph, Nitin Jonathan Myers, Ashish Pandharipande, Edoardo Focante
article en
1 citations

Abstract

Radar is a key technology in automotive driving for target detection and perception. In this work, we leverage prior environmental information in the form of occupancy maps to design space-time codes for a fully digital MIMO radar. We transform this design problem into the optimization of spatial beamforming gains and time-domain codes. The beamforming gains are optimized to enhance the strength of returns from cells associated with a higher uncertainty of occupancy. The time-domain codes are optimized to minimize the correlation between returns of targets within the drivable space. We validate our method on the nuScenes dataset to show that the designed space-time codes achieve higher detection rates than designs that do not rely on prior information from occupancy maps.

NXP (Netherlands) (NL), Delft University of Technology (NL)
Openalex Percentile: Top 7%
Radar Systems and Signal Processing
4.99
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Situation-aware Space-time Waveform Design for Automotive MIMO Radars — Geethu Joseph, Nitin Jonathan Myers, et al. · (2025) | TGRS Research Map | TGRS