Multi-Sensor Fusion of Active and Passive Measurements for Extended Object Tracking
In many real-world scenarios - like when a person carries a radio device - direct line-of-sight (LoS) signals from surrounding anchors (e.g., base stations or sensors) can get blocked by the person's own body. This makes accurate positioning difficult, especially in challenging indoor or cluttered environments.
Our recent work tackles this problem by treating the person not just as an obstacle, but as a key part of the solution. We model the person as an extended object (EO) that can scatter and attenuate signals. Instead of relying only on LoS measurements, we combine:
- Active measurements between the device and fixed anchors
- Passive measurements between pairs of anchors that reflect off the person
We developed a Bayesian estimation framework and an extended object data association algorithm that can make sense of these complex, indirect signals.
The result shows that the proposed joint estimation algorithm (AP-EOPDA) is much more reliable and accurate in positioning - even when the LoS is completely blocked [1].
This approach outperforms traditional methods that assume the point-target or rely solely on LoS signals. It opens the door to more robust and energy-efficient localization, especially in environments where unavailable LoS is common.
Feel free to reach out if you're working on related problems or want to know more about the technical details!
[1] Hong Zhu and Alexander Venus and Erik Leitinger and Klaus Witrisal, "Multi-Sensor Fusion of Active and Passive Measurements for Extended Object Tracking", in 33rd European Signal Processing Conference (EUSIPCO 2025), DOI:10.48550/arXiv.2504.18301.
Contact:
hong.zhu@tugraz.at
https://www.linkedin.com/in/hong-zhu-74173427a/
Delay-Doppler-based Direct Positioning in D-MIMO
In this work we exploit the delay-Doppler characteristics of the channel state infromation to track a moving device. Together with a group of colleagues, we worked with realistic measurement data collected in an industrial scenario consisting of a trajectory going through various types of channel characteristics, ranging from clear line-of-sight (LOS), over obstructed LOS to non line-of-sight (NLOS) for the various anchors.
Watch the video below and listen to the explanation of Benjamin Deutschmann, describing how the developed algorithm deals with this difficult scenario. Head over to IEEEXplore to read the final paper or check out the pre-print on arXiv.
Bistatic MIMO radar sensing
We recently published results about MIMO radar sensing. The results show how passive sensing of reflective surfaces can be achieved with large arrays.
More details can be found at this link in our paper.
