Signal processing for environment-aware radar (SPEAR)

The SPEAR project studies signal processing for connected automotive radar systems. The goal is to exploit vehicular connectivity to improve perception in mixed traffic conditions where vehicles have different sensing capabilities.

Project data

Researchers: Geethu Joseph, Nitin Myers, Peiyuan Zhai, Rupam Chakraborty
Starting date: February 2023
Closing date: February 2027
Funding: 2000 kE; related to group 1000 kE
Sponsor: NXP, TKI
Partners: NXP
Contact: Geethu Joseph

Research themes

Digital mmWave radar waveform design and algorithms
Interference-aware distributed radar sensing
Multimodal sensing for automotive radar
Surface-aided radar sensing

The project develops advanced signal processing methods for next-generation automotive radar. It focuses on distributed processing over the communication backbone network, so multiple sensors can cooperate instead of operating in isolation.

SPEAR project

Summary

In future automotive radar systems, multiple mmWave radars are networked to enable high-resolution localization and obstacle detection. SPEAR addresses the gap between current radar sensing and connected sensing infrastructure by developing advanced algorithms that make better use of available vehicles, sensors, and communication links.

The project is part of TU Delft’s Signal Processing Systems research line on distributed autonomous sensing systems.

References

(Zhai et al., 2025) (Harraway et al., 2025) (Zhai et al., 2026) (Harraway et al., 2025) (Zhai et al., 2024)

2026

IEEE Sensors Journal

Camera-Aided Binary Prior Support-Informed Occupancy Grid Mapping

Peiyuan Zhai, Geethu Joseph, N. Jonathan Myers, and 1 more author

IEEE Sensors Journal, Feb 2026

DOI Bib

@article{zhai2026camera,
  title = {Camera-Aided Binary Prior Support-Informed Occupancy Grid Mapping},
  author = {Zhai, Peiyuan and Joseph, Geethu and Myers, N. Jonathan and Pandharipande, Ashish},
  journal = {IEEE Sensors Journal},
  volume = {26},
  number = {3},
  pages = {4340--4348},
  year = {2026},
  month = feb,
  doi = {10.1109/JSEN.2025.3642255},
  publisher = {IEEE},
}

2025

IEEE Sensors Journal

Spatial Sparsity-Aware Radar-LiDAR Fusion for Occupancy Grid Mapping in Automotive Driving

Peiyuan Zhai, Geethu Joseph, N. J. Myers, and 2 more authors

IEEE Sensors Journal, Sep 2025

DOI Bib

@article{zhai2025spatial,
  title = {Spatial Sparsity-Aware Radar-LiDAR Fusion for Occupancy Grid Mapping in Automotive Driving},
  author = {Zhai, Peiyuan and Joseph, Geethu and Myers, N. J. and Onen, Cemalcan and Pandharipande, Ashish},
  journal = {IEEE Sensors Journal},
  volume = {25},
  number = {17},
  pages = {33328--33338},
  year = {2025},
  month = sep,
  doi = {10.1109/JSEN.2025.3592023},
  publisher = {IEEE},
}

IEEE Sensors Journal

Accelerated Pattern-Coupled Sparse Bayesian Learning for Automotive Occupancy Mapping

Frank Harraway, Peiyuan Zhai, Geethu Joseph, and 1 more author

IEEE Sensors Journal, 2025

DOI Bib

@article{harraway2025accelerated,
  author = {Harraway, Frank and Zhai, Peiyuan and Joseph, Geethu and Pandharipande, Ashish},
  journal = {IEEE Sensors Journal},
  title = {Accelerated Pattern-Coupled Sparse Bayesian Learning for Automotive Occupancy Mapping},
  year = {2025},
  volume = {25},
  number = {22},
  pages = {41801--41810},
  keywords = {Sensors;Laser radar;Radar;Computational modeling;Vectors;Radar measurements;Bayes methods;Sparse matrices;Reflection;Octrees;Adaptive resolution;light detection and ranging (LiDAR) point clouds;nuScenes dataset;occupancy grid mapping (OGM);radar point clouds;RADIATE dataset;sparse Bayesian learning},
  doi = {10.1109/JSEN.2025.3617133}
}

IEEE SENSORS

Computationally-Efficient Sparsity-Aware Occupancy Grid Mapping for Automotive Driving

Frank Harraway, Peiyuan Zhai, Ashish Pandharipande, and 1 more author

In 2025 IEEE SENSORS, 2025

DOI Bib

@inproceedings{harraway2025computationally,
  author = {Harraway, Frank and Zhai, Peiyuan and Pandharipande, Ashish and Joseph, Geethu},
  booktitle = {2025 IEEE SENSORS},
  title = {Computationally-Efficient Sparsity-Aware Occupancy Grid Mapping for Automotive Driving},
  year = {2025},
  pages = {1--4},
  keywords = {Point cloud compression;Laser radar;Runtime;Accuracy;Benchmark testing;Spatial databases;Real-time systems;Computational efficiency;Bayes methods;Automotive engineering;Occupancy grid mapping;LiDAR point clouds;sparse Bayesian learning;automotive driving},
  doi = {10.1109/SENSORS59705.2025.11331194}
}

2024

IEEE SENSORS

Sparsity-aware occupancy grid mapping for automotive driving using radar-LiDAR fusion

Peiyuan Zhai, Geethu Joseph, N. J. Myers, and 2 more authors

In 2024 IEEE SENSORS, Oct 2024

DOI Bib

@inproceedings{zhai2024sparsity,
  title = {Sparsity-aware occupancy grid mapping for automotive driving using radar-LiDAR fusion},
  author = {Zhai, Peiyuan and Joseph, Geethu and Myers, N. J. and Onen, Cemalcan and Pandharipande, Ashish},
  booktitle = {2024 IEEE SENSORS},
  pages = {1--4},
  year = {2024},
  month = oct,
  doi = {10.1109/SENSORS60989.2024.10785054},
  organization = {IEEE},
}