Algorithm will help locate humans, robots in GPS-denied environments, says ARL
NewsOctober 05, 2018
ADELPHI, Md. Scientists at the U.S. Army Research Laboratory (ARL) have developed a new algorithm that enables users to locate both humans and robots in GPS-denied areas.
According to the researchers -- including team members Gunjan Verma and Dr. Fikadu Dagefu -- typical approaches to localization employ a wireless signal's power or delay, which works well in outdoor scenarios having minimal obstacles but does not work well in obstacle-rich scenes.
The ARL scientists developed a novel technique for determining the direction of arrival (DoA) of a radio-frequency signal source, which is a basic enabler of localization.
"The proposed technique is robust to multiple scattering effects, unlike existing methods such as those that rely on the phase or time of arrival of the signal to estimate the DoA," Verma said. "This means even in the presence of occluders that scatter the signal in different directions before it is received by the receiver, the proposed approach can accurately estimate the direction of the source."
"For example, an adversary may destroy the infrastructure (e.g., satellites) needed for GPS; alternatively, complex environments (e.g., inside a building) are hard for the GPS signal to penetrate," Dagefu said. "This is because complex and cluttered environments impede the straight-line propagation of wireless signals." Dagefu noted that obstacles inside the building, especially when their size is much larger than the wavelength of the wireless signal, weaken the power of the signal (attenuation) and redirect its flow (called multipath), making a wireless signal very unreliable for communicating information about location.
The researchers say that the key invention is an algorithm that statistically models the RSS gradient and controls for spatial outliers and correlations; when the signal is extremely noisy, the estimator correctly outputs that no DoA is present, rather than incorrectly estimating an arbitrary direction. The output is an estimated DoA and associated uncertainty.
In addition to doing away with a requirement for fixed infrastructure, the proposed technique also does not rely on any prior training data, knowledge about the environment, multiple antennas, or prior calibration between nodes.
A journal paper documenting the research has been accepted for publication in the Institute of Electrical and Electronics Engineers Transactions on Vehicular Technology, an early version of which can be found at the IEEE website.
