# Poster: Joint RF-Gas Sensing for Victim Localization using UAV Networks

Guillermo Benito-Calvino, Ahmed Boubrima, Hervé Rivano, Alessandro Renzaglia, Zhambyl Shaikhanov

► To cite this version:

Guillermo Benito-Calvino, Ahmed Boubrima, Hervé Rivano, Alessandro Renzaglia, Zhambyl Shaikhanov. Poster: Joint RF-Gas Sensing for Victim Localization using UAV Networks. Mobisys25 - 23rd ACM International Conference on Mobile Systems, Applications, and Services, Jun 2025,heim, United States. pp.1-3, 2025, 10.1145/3711875.3734566 . hal-05059897

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Submitted on 7 May 2025

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<figure><img src="image_1.png" /></figure>
Poster: Joint RF-Gas Sensing for Victim
Localization using UAV Networks

Guillermo Benito-Calvino¹, Ahmed Boubrima¹, Hervé Rivano¹, Alessandro Renzaglia¹, Zhambyl Shaikhanov²

¹Inria, INSA Lyon, CITI, UR3720, 69621 Villeurbanne, France

²University of Maryland, Maryland, USA

Abstract

Using UAVs has recently emerged as a cost-efficient solution to assist first responders in search and rescue missions. Victims are usually equipped with wireless devices, which makes RF sensing an efficient solution for their localization in disaster situations. Nevertheless, the success of existing methods is highly diminished by the noisy nature of RF measurements. While leveraging recent advancements in lightweight gas sensing, we present in this ongoing work paper a novel localization approach that efficiently combines RF measurements with victim odor information while accounting for the dynamic nature of measurements' quality. We discuss the approach design, early simulation results, and ongoing experimental evaluation.

**Keywords:** Target localization, UAV mission planning, RF sensing, gas sensing, trilateration, triangulation.

# 1 Introduction

Search and Rescue (S&R) missions aim to locate, assist, and extract people in distress during emergencies (e.g. earthquakes, avalanches, or floods). Precision and speed are critical but challenging due to the limited accessibility of typical mission environments. Unmanned Aerial Vehicles (UAVs) can be used to search and assist victims rapidly and without human risk.

Radio frequency (RF) sensing is an efficient solution for victim localization, especially in situations where victims cannot be localized using vision and acoustics (e.g. nighttime or victims covered in debris). Nevertheless, the success of existing RF methods is highly diminished by the noisy nature of radio measurements, for instance, due to multipath or NLOS effects. To cope with that, we propose in this ongoing work incorporating odor as an additional information source. Indeed, victims release volatile organic compounds (VOCs) through their skin, breath, and excretions, creating a unique scent that can be detected by sensors. Similarly to how dogs locate victims by tracking their scent.

# 2 Related work

Supporting search and rescue missions can greatly improve mission success. Several studies have explored victim localization using RF-sensing UAVs. For example, in [1, 2], UAVs measure the time-of-flight to the target and use methods similar to trilateration to determine the source location. Additionally, the growing field of odor or gas source localization with robots has produced numerous studies [3]. For instance, authors in [4] consider the use of a UAV with three gas sensors to determine the direction of the gas source. However, the integration of RF sensing with odor or gas measurements still remains unexplored.

# 3 Proposed Approach

## 3.1 Objective

Our method aims to efficiently integrate RF and odor signals to quickly and accurately determine a target's position using a mobile sensing system. The setup includes a static target emitting odor/gas and RF signals, and a UAV (or a network of UAVs) capable of sensing both. The UAV collects noisy range
measurements (distance to the target) via RF sensing, using techniques like time-of-flight or received signal strength, in addition to noisy bearing measurements (direction to the target) via odor signals, using the signal gradient and wind direction. Combining these data, the system estimates the most probable target position.

The main challenge regarding odor integration is the fact that bearing information is only available within the odor's plume. This makes incorporating odor measurements very challenging in cases where, for instance, the UAV system is located right behind the victim but not exposed to the odor's plume due to the wind direction facing the opposite way.

We assume in what follows that the shape and the size of the odor's plume are known, and that the bearing information can be correctly measured everywhere inside the plume by considering both the wind direction and the odor's gradient.

## 3.2 Background: Fisher Information Matrix

A commonly used metric to quantify how much information a set of noisy measurements provides about an unknown parameter is the Fisher Information Matrix (FIM), which is the inverse of the Cramér-Rao Lower Bound (CRLB) [5]. The CRLB sets a lower bound on the covariance of any unbiased estimator, making the FIM a valuable tool for assessing the measurement information content. When applied to the localization problem, the FIM depends on the geometry of the measurement positions. The minimization of the localization uncertainty is obtained by maximizing the determinant of the FIM (D-optimality) and consequently selecting the UAV's measurement positions.

## 3.3 Localization process

The UAV begins by selecting an arbitrary (or convenient) initial measurement position to record a range measurement and, if within the plume, a bearing measurement as well. Next, the target's position probability density function (PDF) is calculated using Bayesian multi-lateration and multi-angulation. Under white noise, a single range measurement produces an annular PDF, while one bearing measurement yields a conical PDF, both with normally distributed widths.

By combining the target's PDF with known plume shape, size, and plume presence data in measuring locations, we compute the plume probability across the region of interest. The next measurement position is then chosen to maximize the determinant of the Fisher Information Matrix (FIM). This process is repeated iteratively, with the final target estimate corresponding to the most probable point in the PDF.

Notably, our algorithm has two interdependent components: PDF estimation and measuring position selection via the FIM. Both components interact symbiotically: A sharper PDF enhances the accuracy of the FIM, which in turn guides better measurement positioning, further refining the PDF in subsequent iterations.

## 3.4 Sensing optimization

Given a set of positions where measurements were collected $\mathcal{P}$ (a subset of space points) and a potential target's position $\mathbf{q}$ (that is unknown), we derive from [5] the element-wise formulation of the total Fisher Information Matrix (combining both RF and odor information) as

$$ \mathbf{FIM}_{\text{tot}}^{lk}(\mathcal{P}, \mathbf{q}) = \sum_{\mathbf{p}_i \in \mathcal{P}} \mathbf{FIM}_{\mathbf{r}}^{lk}(\mathbf{p}_i, \mathbf{q}) + f(\mathbf{p}_i) \cdot \mathbf{FIM}_{\mathbf{b}}^{lk}(\mathbf{p}_i, \mathbf{q}), \quad (1) $$

where the terms $\mathbf{FIM}_{\mathbf{r}}$ and $\mathbf{FIM}_{\mathbf{b}}$ denote the range and bearing FIM, respectively, and $f(\mathbf{p}_i)$ represents the odor's plume presence probability at each location $\mathbf{p}_i$.

Based on that, we obtain the most informative measuring position at step $t+1$, denoted $\mathbf{p}_{t+1}^*$, by solving the optimization problem:

$$ \mathbf{p}_{t+1}^* = \arg\max_{\mathbf{p}_{t+1}} \int_{\mathbb{R}^3} \det(\mathbf{FIM}_{\text{tot}}(\mathcal{P}_t + \mathbf{p}_{t+1}, \mathbf{q})) \rho(\mathbf{q}) d\mathbf{q}, \quad (2) $$

where $\rho(\mathbf{q})$ is the probability density function of the target's position and $\mathcal{P}_t + \mathbf{p}_{t+1}$ represents the union set of all previous measuring positions up to step $t$ and the next measuring position $\mathbf{p}_{t+1}$.
# 4 Results and ongoing evaluations

We conducted preliminary simulations to assess the current algorithm, with ongoing plans for real-world experiments. A static odor plume is modeled using a Gaussian dispersion, forming an oval shape with one vertex at the source, covering roughly 5% of the simulation space. We then follow the process described in Section 3.3 simulating range and bearing (only if inside the plume) measurements by adding white noise to the ground truth.

We compare our proposed method to localization results obtained using range-only sensors (which use an efficient, evenly distributed geometry). We denote the standard deviation of the noise for range and bearing measurements $\sigma_r$ and $\sigma_b$ respectively. Results, depicted in Fig. 1, show that for small values of $\sigma_b$, the proposed method outperforms range-only sensors in both accuracy and speed. Furthermore, at very high values ($\sigma_b = \pi \text{ rad}$), where the data becomes nearly random, the proposed method is robust and manages to match the performance of the range-only approach by prioritizing optimal geometries for range measurement positions, rather than placing measurement positions inside the plume.

<figure><img src="image_2.png" /><figcaption>Figure 1: Average error of the proposed method compared to evenly distributed range-only sensors.</figcaption></figure>

The next step is to experimentally evaluate the proposed method. We are currently developing a UAV sensing system to collect real-world data, which will enable us to further analyze the performance of our approach and provide insights on how to guide the development of practical solutions. Moreover, we will work on relaxing the assumptions stated in Section 3.1.

# 5 Conclusions

We propose a novel approach for determining the position of victims in search and rescue missions by jointly considering odor emissions (emitted from the human body) and radio frequency signals (emitted from the victim's wireless devices). Early simulation results indicate that odor can serve as a viable source of information under ideal conditions. Ongoing real-world evaluations while considering experimental data will allow us to determine the performance of our proposed approach in practice.

# References

[1] Z. Shaikhanov, A. Boubrima, and E. Knightly. Falcon: A networked drone system for sensing, localizing, and approaching rf targets. *IEEE IoT Journal*, 9(12):9843–9857, 2022.

[2] A. Albanese, V. Sciancalepore, and X. Costa-Pérez. Sardo: An automated search-and-rescue drone-based solution for victims localization. *IEEE TMC*, 21(9):3312–3325, 2022.

[3] T. Jing, Q. Meng, and H. Ishida. Recent progress and trend of robot odor source localization. *IEEJ TEEE*, 16(7):938–953, 2021.

[4] B. Luo, Q. Meng, J. Wang, and M. Zeng. A flying odor compass to autonomously locate the gas source. *IEEE TIM*, 67(1):137–149, 2018.

[5] A. N. Bishop, B. Fidan, B. D.O. Anderson, K. Doğançay, and P. N. Pathirana. Optimality analysis of sensor-target localization geometries. *Automatica*, 46(3):479–492, 2010.