Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

Autor: Ismail Guvenc, Farshad Koohifar, Mihail L. Sichitiu
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
intermittent transmitter
General Computer Science
Heuristic (computer science)
Computer science
Systems and Control (eess.SY)
02 engineering and technology
drone
Upper and lower bounds
jammer
localization
Extended Kalman filter
0203 mechanical engineering
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
Computer Science - Multiagent Systems
Motion planning
Cramer Rao lower bound
Electrical Engineering and Systems Science - Signal Processing
020301 aerospace & aeronautics
Noise measurement
Fisher information
Stochastic process
General Engineering
Swarm behaviour
020206 networking & telecommunications
Trajectory
Computer Science - Systems and Control
lcsh:Electrical engineering. Electronics. Nuclear engineering
Gradient descent
Algorithm
lcsh:TK1-9971
Multiagent Systems (cs.MA)
Zdroj: IEEE Access, Vol 6, Pp 15884-15897 (2018)
Popis: Localization of a radio frequency (RF) transmitter with intermittent transmissions is considered via a group of unmanned aerial vehicles (UAVs) equipped with omnidirectional received signal strength (RSS) sensors. This group embarks on an autonomous patrol to localize and track the target with a specified accuracy, as quickly as possible. The challenge can be decomposed into two stages: 1) estimation of the target position given previous measurements (localization), and 2) planning the future trajectory of the tracking UAVs to get lower expected localization error given current estimation (path planning). For each stage we compare two algorithms in terms of performance and computational load. For the localization stage, we compare a detection based extended Kalman filter (EKF) and a recursive Bayesian estimator. For the path planning stage, we compare steepest descent posterior Cramer-Rao lower bound (CRLB) path planning and a bio-inspired heuristic path planning. Our results show that the steepest descent path planning outperforms the bio-inspired path planning by an order of magnitude, and recursive Bayesian estimator narrowly outperforms detection based EKF.
Databáze: OpenAIRE
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