Radio Source Localization using Sparse Signal Measurements from Uncrewed Ground Vehicles (2312.03493v1)

Abstract: Radio source localization can benefit many fields, including wireless communications, radar, radio astronomy, wireless sensor networks, positioning systems, and surveillance systems. However, accurately estimating the position of a radio transmitter using a remote sensor is not an easy task, as many factors contribute to the highly dynamic behavior of radio signals. In this study, we investigate techniques to use a mobile robot to explore an outdoor area and localize the radio source using sparse Received Signal Strength Indicator (RSSI) measurements. We propose a novel radio source localization method with fast turnaround times and reduced complexity compared to the state-of-the-art. Our technique uses RSSI measurements collected while the robot completed a sparse trajectory using a coverage path planning map. The mean RSSI within each grid cell was used to find the most likely cell containing the source. Three techniques were analyzed with the data from eight field tests using a mobile robot. The proposed method can localize a gas source in a basketball field with a 1.2 m accuracy and within three minutes of convergence time, whereas the state-of-the-art active sensing technique took more than 30 minutes to reach a source estimation accuracy below 1 m.

• The paper demonstrates a novel UGV-based method that collects sparse RSSI data to accurately localize radio sources within 1 meter error in about 8 minutes.
• It employs coverage path planning and grid-cell analysis of mean RSSI values to determine the most probable location of the transmitter efficiently.
• Field experiments validate that this approach outperforms active sensing strategies by reducing estimation time while simplifying the localization process.

Introduction to Radio Source Localization

Radio source localization is a critical procedure for a variety of applications, including the management of wireless sensor networks, satellite positioning systems, and security-related surveillance operations. Determining the exact location of a radio transmitter proves to be complex due to multiple factors that affect radio signal transmission. The reliability of the communication hinges on the transmitter, receiver, and the environmental conditions. With dynamic variables like free space loss, signal reflections, diffraction, and scattering, receiving strong, consistent radio signals is challenging.

Addressing the Challenge with Uncrewed Ground Vehicles

To combat the complexities of radio signal reception, the exploration of novel techniques becomes necessary. This paper presents a method that uses an uncrewed ground vehicle (UGV), specifically a mobile robot, to collect sparse Received Signal Strength Indicator (RSSI) measurements outdoors and localize the radio source efficiently. The technique showcases significant improvements in speed and reduced complexity compared to conventional methods. RSSI data is gathered as the robot follows a coverage path planning map, and the mean RSSI within each grid cell is evaluated to ascertain the most probable cell containing the radio source. The proposed method has been tested through multiple field experiments using a mobile robot, demonstrating its ability to localize a radio source with high accuracy.

The Efficacy of Sparse Signal Measurements

Three different techniques utilizing RSSI data were analyzed over eight field tests involving a mobile robot, and the proposed method emerged superior with its rapid source localization ability. Compared to an active sensing strategy, which took over 30 minutes for source estimation accuracy below 1 meter, the new method required approximately 8 minutes to cover a basketball-sized field and accurately estimate the source location within 1 meter error. Such performance not only indicates a substantial improvement in turnaround time but also showcases how sparse signal data—when collected strategically—can yield precise results while keeping complexity in check.

Moving Forward with Radio Source Localization

In conclusion, this paper represents a leap in the field of radio source localization through the use of UGVs by introducing a more expedient and less intricate approach. Through utilization of coverage path planning and exploiting sparse RSSI measurements, the method opens up possibilities for quick and precise localization tasks, with potential applications from environmental monitoring to public safety. This promising avenue can be furthered by adding multiple robots to the localization tasks or incorporating machine learning to adapt to environments with higher uncertainties. The research underscores the advancing capacities in robotics and autonomous systems funded by the Australian Centre for Advanced Defence Research.