“…The main landing gear retracts into the wings and has partial doors that cover the struts, but the sides of the tires are exposed to the airstream. In order to focus on airframe noise sources, engine power was set at idle (N 1 = 30%) and the aircraft passed over the microphone array while maintaining level flight for approximately 3 s. A JAXA-developed “Tunnel-in-the-sky” 13,14 guidance display (Figure 4) presented the required flight path to the pilots to enable the aircraft to be flown over the precise center of the microphone array at the required speed and altitude. Figure 4.“Tunnel-in-the-sky” guidance display.…”
In 2015, the Japan Aerospace Exploration Agency launched the Flight demonstration of QUiet technology to Reduce nOise from High-lift configurations project to verify by flight demonstration the feasibility of practical noise-reducing aircraft modification concepts. In order to serve as a baseline for comparison before modification, airframe noise sources of the JAXA Jet Flying Test Bed ''Hisho'' were measured with a 30 m diameter array of 195 microphones mounted on a wooden platform built temporary beside the runway of Noto Satoyama Airport in Japan. A classical Delay and Sum in the time domain beamforming algorithm was adapted for the present study, with weight factors introduced to improve the low-frequency resolution and autocorrelations eliminated to suppress wind noise at high frequencies. In the landing configuration at idle thrust, the main landing gear, nose landing gear, and side edges of the six extended flap panels were found to be the dominant ''Hisho'' airframe noise sources. Deconvolution by the DAMAS and CLEAN-SC algorithms provided clearer positions of these sound sources at low frequencies. Integration of acoustical maps agreed well with the sound pressure level measured by a microphone placed at the center of the microphone array and gave detailed information about the contribution of each noise source.
“…The main landing gear retracts into the wings and has partial doors that cover the struts, but the sides of the tires are exposed to the airstream. In order to focus on airframe noise sources, engine power was set at idle (N 1 = 30%) and the aircraft passed over the microphone array while maintaining level flight for approximately 3 s. A JAXA-developed “Tunnel-in-the-sky” 13,14 guidance display (Figure 4) presented the required flight path to the pilots to enable the aircraft to be flown over the precise center of the microphone array at the required speed and altitude. Figure 4.“Tunnel-in-the-sky” guidance display.…”
In 2015, the Japan Aerospace Exploration Agency launched the Flight demonstration of QUiet technology to Reduce nOise from High-lift configurations project to verify by flight demonstration the feasibility of practical noise-reducing aircraft modification concepts. In order to serve as a baseline for comparison before modification, airframe noise sources of the JAXA Jet Flying Test Bed ''Hisho'' were measured with a 30 m diameter array of 195 microphones mounted on a wooden platform built temporary beside the runway of Noto Satoyama Airport in Japan. A classical Delay and Sum in the time domain beamforming algorithm was adapted for the present study, with weight factors introduced to improve the low-frequency resolution and autocorrelations eliminated to suppress wind noise at high frequencies. In the landing configuration at idle thrust, the main landing gear, nose landing gear, and side edges of the six extended flap panels were found to be the dominant ''Hisho'' airframe noise sources. Deconvolution by the DAMAS and CLEAN-SC algorithms provided clearer positions of these sound sources at low frequencies. Integration of acoustical maps agreed well with the sound pressure level measured by a microphone placed at the center of the microphone array and gave detailed information about the contribution of each noise source.
“…Many such interfaces exist for the control of UAVs which are prevalent in both military and hobbyist communities. Fixed wing displays are commonly designed to mimic a cockpit experience using video feedback with overlaid pitch, roll, heading, altitude and other indicators including projected path estimates [16][17][18][19][20]. However, these interfaces are not necessarily optimal for a guided airdrop system which is incapable of thrust generation which restricts the system to a constant descent rate with respect to the atmospheric winds.…”
The direct inclusion of human pilots into airdrop operations has a strong potential to increase the landing accuracy of conventionally autonomous guided airdrop systems. Human pilots have significant mental flexibility and an innate ability to prioritize requirements to aid safe and accurate landings. Autonomous algorithms on the other hand, are generally rigid in nature and cannot handle situations not directly programmed into the software. This paper outlines the work done to develop an integrated human-machine interface that combines the flexibility of human control decisions with the powerful ability of autonomous systems to measure data on the aircraft and generate key estimates. Two interfaces are presented melding a first person view camera mounted to the payload of the airdrop system and a bird's eye GPS based map of the drop zone with relevant system estimates overlaid. Flight testing of these digital feedback methods to the pilot are studied to identify the ability of human operators to successfully and accurately control an airdrop system to the ground. Results indicated that a trained human operator has the ability to improve landing accuracy over a conventionally autonomous system by 36%.
“…An initial study into the benefits of human control of guided airdrop systems was tested by Mayer et al [23,24] in the 1980's. Since the implementation of GPS, human-in-the-loop control strategies in aerospace have focused on fixed-wing vehicles [25][26][27][28][29] or the control of multiple platforms by a single operator [30,31] due to a document issued by the Office of the Secretary of Defense outlining future goals of unmanned aerial vehicles [32]. The visual feedback methods studied here combine elements common to remote piloting of fixed wing UAVs which are prevalent in both military and hobbyist communities.…”
Section: Introductionmentioning
confidence: 99%
“…The visual feedback methods studied here combine elements common to remote piloting of fixed wing UAVs which are prevalent in both military and hobbyist communities. Fixed wing displays are commonly designed to mimic a cockpit experience using video feedback with overlaid pitch, roll, heading, altitude and other indicators including projected path estimates [25][26][27][28][29]. However, there are significant differences between the dynamics and control of fixed wing aircraft and guided parafoil systems that limit the transition of previously studied displays to this work.…”
Advances in guided airdrop technology including guidance, navigation, and control algorithms, novel control mechanisms and wind sensing algorithms have led to significant improvements over unguided airdrop systems. Guided systems are autonomously controlled with an embedded microprocessor using position and velocity feedback. While capable of highly accurate landing, these systems struggle to overcome deviations from expected flight dynamics due to canopy damage or cargo imbalance, complex terrain at the drop zone, and loss of sensor feedback. Human operators are intelligent, highly adaptive, and can innately judge the flight vehicle and environment to steer the vehicle to the desired impact point provided sufficient information. This work experimentally explores operators' abilities to accurately land an airdrop system using different sensing modalities. Human operator landing results are compared with a state of the art fully autonomous airdrop system. Across the methods analyzed, human operators attained up to a 40% increase in landing accuracy over the fully autonomous control algorithm.
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