Adaptive Aerial Localization Using Lissajous Search Patterns
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Authors
Steckenrider, J. Josiah
Issue Date
2022
Type
journal-article
Language
Keywords
Estimation , Location awareness , Probability density function , Adaptation models , Mathematical model , Search problems , Uncertainty
Alternative Title
Abstract
This work presents an adaptive approach to cooperative aerial search and localization (SAL) which implements Lissajous search patterns and non-Gaussian observation likelihoods to preserve high target information. The adaptive component of the framework utilizes a simultaneous estimation and modeling technique to both estimate agent states and correct their motion models. In order to maximize the information available about a target even when it is not observed by a search agent, multi-Gaussian observation likelihoods are continuously generated for each agent and then fused across the search team. Monte Carlo simulation studies show that the proposed adaptive localization framework outperforms standard filtering techniques by significant margins, for a wide range of parameter values. The differential entropies of fused target likelihoods are studied for various multiagent Lissajous pattern configurations, leading to the derivation of optimal Lissajous parameters for cooperative SAL. This work has relevance for SAL applications in rescue, safety, and defense sectors, offering a robust solution to target localization when a priori target motion information is unavailable.
Description
Citation
J. J. Steckenrider, "Adaptive Aerial Localization Using Lissajous Search Patterns," in IEEE Transactions on Robotics, vol. 38, no. 4, pp. 2094-2113, Aug. 2022, doi: 10.1109/TRO.2021.3126225.
Publisher
IEEE
License
Journal
Volume
Issue
PubMed ID
ISSN
1552-3098
1941-0468
1941-0468