Spatio-temporal characterization of airborne radio frequency environments

Wagle, Neeti; Frew, Eric W.

doi:10.1109/glocomw.2011.6162387

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…Significant progress has been made in solving the offline estimation problem of localizing an RF emitter. Wagle and Frew [29] propose a Gaussian process-based method and show experimental results from data collected by a fixed-wing UAV. Korner et al [16] report on offline estimation experiments using a directional antenna and a deterministic range-bearing sensor model.…”

Section: Related Workmentioning

confidence: 99%

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Cliff

Fitch

Sukkarieh

et al. 2015

Robotics: Science and Systems XI

112

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Abstract-The application of autonomous robots to efficiently locate small wildlife species has the potential to provide significant ecological insights not previously possible using traditional landbased survey techniques, and a basis for improved conservation policy and management. We present an approach for autonomously localizing radio-tagged wildlife using a small aerial robot. We present a novel two-point phased array antenna system that yields unambiguous bearing measurements and an associated uncertainty measure. Our estimation and informationbased planning algorithms incorporate this bearing uncertainty to choose observation points that improve confidence in the location estimate. These algorithms run online in real time and we report experimental results that show successful autonomous localization of stationary radio tags and live radio-tagged birds.

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Section: Related Workmentioning

confidence: 99%

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Cliff

Fitch

Sukkarieh

et al. 2015

Robotics: Science and Systems XI

112

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“…(1) and (2) depend only on u m . Since channel signal strength generally depends on position and time [14], the buffer dynamics depend on both u m and u c . The full problem objective [Eq.…”

Section: Data-ferry Systemmentioning

confidence: 99%

Integrating Nonparametric Learning with Path Planning for Data-Ferry Communications

Carfang

Wagle

Frew

2015

Journal of Aerospace Information Systems

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In a data-ferrying unmanned aircraft system, ferrying performance requires knowledge of the communication environment through which the aircraft moves. This work integrates ferry planning with opportunistically learning the radio environment through the use of a Gaussian process. The unmanned aircraft's trajectory is initially optimized with an a priori model. After flying one circuit of the closed trajectory, radio-frequency variations observed by the ferry are used to train a Gaussian process and improve the model of the environment. This iterative ferry-andlearn system is analyzed through a simulation study, showing ferry performance improves rapidly. The ferry achieves 80% of optimal within four iterations and 93% after nine iterations, as the Gaussian process is able to converge quickly to the true radio-frequency environment. This work further compares the Gaussian process to common parameter-based estimation methods through two extremes of radio-frequency environments. The nonparametric nature of a Gaussian process allows for a higher-resolution model, resulting in the ferry's performance converging to a significantly higher upper bound than parameter-based methods. Nomenclature= set of all buffers over all points b 1 ; : :node buffers at time t i b f i = state of ferry buffer at time t i C N = covariance matrix for Gaussian process training samples c = Shannon-Hartley channel capacity e= radio-frequency variation, i.e., a priori model residual f m , f t , f c = functionals for vehicle, time, and buffer dynamics GP j;n = Gaussian process predictor for node j after n iterations J = objective function for the ferrying problem, i.e., the system's effective throughput J c = objective function for bandwidth control policy K = kernel matrix k = vector of correlations L = length scale matrix l s = Gaussian process hyperparameter representing spatial length scale M j;n = radio-frequency environment model learned for node j after n iterations N = number of segments in the discretized ferry path n = iteration of the ferry-and-learn process p = ferry's path; the set of ferry poses p 1 ; : : : ; p N p i = ferrying vehicle pose at time t i s = mean received signal strength t i = time corresponding to when the ferry is at location instance i u c = control variables for the ferry's communication u m = control variables for the ferry's motion v i = location-dependent noise X = set of input variables in Gaussian process training set Y = set of response or output variables in Gaussian process training set z = measured wireless signal strength β = bandwidth of the wireless channel δ = Kronecker delta θ = set of Gaussian process hyperparameters l s ; σ 2 f ; σ 2 n μ e;j = mean radio-frequency variation prediction from the Gaussian process of node j ν = additive white Gaussian noise Ξ j = a priori model for node j π = optimal bandwidth control policy

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“…The fundamental parameter in wireless channel capacity and BER performance is the Signal to Noise Ratio (SNR [46]. Previous efforts have attempted to sample the communication environment a priori to construct a radio-map indicating areas of high network performance.…”

Section: Planning With Uncertaintymentioning

confidence: 99%

“…However, actively studied concepts of mini/micro-UAV teams deployed below the tree line, inside buildings, or in urban settings will be significantly affected by these dynamics [45]. Similarly, noise and interference from other emitting sources in the environment can be difficult to predict and can change both in time and by location [46]. In some contested environments, another source may actively seek to interfere or "jam" communications to degrade the system per- Given the ability to control the network topology, the problem of efficiently routing information between nodes is a challenge in itself [10].…”

Section: Network Control Challengesmentioning

confidence: 99%

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Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams

et al. 2013

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A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution.

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Spatio-temporal characterization of airborne radio frequency environments

Cited by 16 publications

References 12 publications

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Integrating Nonparametric Learning with Path Planning for Data-Ferry Communications

Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams

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