Algorithmic analysis demonstrates autonomous radio frequency emitter localization in GPS-denied indoor settings, highlighting robust navigation under environmental uncertainty.
This paper presents a method for autonomously searching for a Radio Frequency (RF) emitter with unknown intensity in an indoor environment. It employs a cognitive search strategy to locate the RF emitter at discrete intervals. The searcher concurrently estimates the RF emitter parameters and self-localizes within a known map. The searcher utilizes the received signal strength sensor to estimate the location and signal intensity of a stationary RF emitter. The search algorithm employs a partially known path loss model to account for realistic RF signal propagation, addressing the uncertainties regarding the structure and materials of the indoor environment. RF emitter localization and self-localization are carried out simultaneously using a sequential Bayesian estimation framework (implemented by using particle filter), and dynamic routing strategies are planned using a partially observable Markov decision process (POMDP).
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Ly et al. (2024) studied this question.
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