Abstract
A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.
Highlights
We propose an approach to enhance time difference of arrival (TDOA) localization Systems in urban scenario binding together a ray-tracing propagation tool to extract the multipath fingerprint and a machine learning framework to improve the precision even in NLOS situation
Among several parameters that can be measured from the received signal, time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), and direction of arrival (DOA), of the emitted signal, are the most commonly used for source localization
A TDOA system is deployed in an NLOS scenario, and the multipath effects corrupt the time-differences extracted from the complex baseband signals that arrived in each sensor
Summary
We propose an approach to enhance TDOA localization Systems in urban scenario binding together a ray-tracing propagation tool to extract the multipath fingerprint and a machine learning framework to improve the precision even in NLOS situation. Contrary to similar approaches [1], where the NLOS measurements are not used in the position estimation, we build a multipath exploitation scheme that uses ray tracing and machine learning to give an extra layer of information using the buildings and obstacles in the environment. The localization of an electromagnetic source using a set of geographic spatially separated sensors is an essential problem in radar, sonar, and global positioning systems and mobile communications. The position of a target of interest can be determined by utilizing its emitted signal measured at an array of spatially separated receivers, known as “observers” or sensors, with a priori known locations.
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