Magnetic signature control strategies for an unmanned aircraft system

Forrester, Robert

doi:10.22215/etd/2011-07308

Cited by 14 publications

(22 citation statements)

References 16 publications

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“…Research on the magnetic signature of the GeoSurv II was conducted in parallel (Forrester 2011;Forrester et al 2011). The transient and static sources of magnetic noise produced by the GeoSurv II were measured using two Geometrics G822-A cesium magnetometers located in the pods at each wingtip, in a magnetically quiet environment.…”

Section: Figurementioning

confidence: 99%

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Aeromagnetic surveying using a simulated unmanned aircraft system

Caron

Samson

Straznicky

et al. 2013

Geophysical Prospecting

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Carleton University and Sander Geophysics are developing an unmanned aircraft system (UAS) for aeromagnetic surveying. As an early indication of the expected performance of the unmanned aircraft system, a simulated unmanned aircraft system (sUAS) was built. The simulated unmanned aircraft system is a T‐shaped structure configured as a horizontal gradiometer with two cesium magnetometers spaced 4.67 m apart, which is the same sensor geometry as planned for the unmanned aircraft system. The simulated unmanned aircraft system is flown suspended beneath a helicopter. An 8.5 km2 area in the Central Metasedimentary Belt of the Grenville Province, near Plevna, Ontario, Canada, was surveyed with the simulated unmanned aircraft system suspended 50 m above ground. The survey site was chosen on the basis of its complex geological structure. The total magnetic intensity (TMI) data recorded were compared to that obtained during a conventional fixed‐wing survey and a ground survey. Transverse magneto‐gradiometric data were also recorded by the simulated unmanned aircraft system. The simulated unmanned aircraft system total magnetic intensity data have a higher resolution than the conventional fixed‐wing data and were found to have a similar resolution to that of the ground survey data. The advantages of surveying with the simulated unmanned aircraft system were: (1) the acquisition of a detailed data set free of gaps in coverage at a low altitude above the terrain and (2) substantial saving of time and effort. In the survey site, the 4.67 m simulated unmanned aircraft system gradiometer measured the transverse magnetic gradient reliably up to an altitude of 150 m above ground.

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Section: Figurementioning

confidence: 99%

“…Research on the magnetic signature of the GeoSurv II was conducted in parallel (Forrester ; Forrester et al . 2011).…”

Section: Introductionmentioning

confidence: 99%

Aeromagnetic surveying using a simulated unmanned aircraft system

Caron

Samson

Straznicky

et al. 2013

Geophysical Prospecting

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“…, ). For this type of installation, methods for locating, modelling and reducing interference have been developed (Forrester ; Tuck et al . ).…”

Section: Introductionmentioning

confidence: 99%

Real‐time compensation of magnetic data acquired by a single‐rotor unmanned aircraft system

Tuck

Samson

Polowick

et al. 2019

Geophysical Prospecting

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Two methods for low‐altitude calibration of a single‐rotor unmanned aircraft system using a real‐time compensator are tested: (1) a stationary calibration where the unmanned aircraft system executes manoeuvres while hovering in order to minimize ambient field changes due to the local geology; and (2) an adapted box calibration flown in four orthogonal directions. Both methods use two compensator‐specific limits derived from established methods for manned airborne calibration: the lowest frequency used by the compensator for the calibration algorithm and the maximum variation of the ambient magnetic intensity experienced by the unmanned aircraft system during calibration. Prior to flying, the unmanned aircraft system was magnetically characterized using the heading error and fourth difference. Magnetic interference was mitigated by extending the magnetometer‐unmanned aircraft system separation distance to 1.7 m, shielding, and demagnetization. The stationary calibration yielded an improvement ratio of 8.595 and a standard deviation of the compensated total magnetic intensity of 0.075 nT (estimated Figure‐of‐Merit of 3.8 nT). The box calibration also yielded an improvement ratio of 3.989 and a standard deviation of the compensated total magnetic intensity of 0.083 nT (estimated Figure‐of‐Merit of 4.2 nT). The stationary and box calibration solutions were robust with low cross‐correlation indexes (1.090 and 1.048, respectively) when applied to a non‐native data set.

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“…2008], [Lares, A. 2012], obstacle detection [Owlia, 2013], obstacle avoidance [Verbickas, 2012], hardware-in-the-loop simulations [Hojeij, 2013], and magnetic interference of ferromagnetic sources on the aircraft [Caron M., 2013], [Forrester, 2011], are all part of the GeoSurv Project.…”

Section: Unmanned Aircraft System Technologies Projectmentioning

confidence: 99%

Development and Testing of a Simulation Environment for an Unmanned Aerial Vehicle

Sunil¹

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This thesis describes the development of a simulation environment for the GeoSurv II Unmanned Aerial Vehicle (UAV) developed by the Unmanned Aircraft System Technologies Project which is a collaborative research program between Carleton University and Sander Geophysics Ltd (SGL). Unmanned Aircraft System Technologies Project continues to evolve and therefore, many of the avionics sub systems has been carried out by the previous students. The problem considered in this thesis is that of integrating the existing systems and two new subsystems developed in this research, into one integrated autonomous simulation environment.The solution for this problem included developing a concept for an autonomous system but more importantly to develop a simulation environment in which the sub-systems can be tested individually or as whole system when required. The new simulation environment developed in this research includes all aspects of the UAV system, from external environmental factors to the flight control responses caused by the avionics and the aerodynamics of the UAV. The framework is also able to produce stereoscopic data that can be used to test various image processing algorithms. Lastly, a communication protocol was developed which bridged the gap between the simulation framework and the real world aircraft. II

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Magnetic signature control strategies for an unmanned aircraft system

Cited by 14 publications

References 16 publications

Aeromagnetic surveying using a simulated unmanned aircraft system

Aeromagnetic surveying using a simulated unmanned aircraft system

Real‐time compensation of magnetic data acquired by a single‐rotor unmanned aircraft system

Development and Testing of a Simulation Environment for an Unmanned Aerial Vehicle

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