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Nico van Oosten
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2 records found
1
Since 2020, all commercial aircraft have been mandated to be equipped with ADS-B Out
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated. ...
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated. ...
Since 2020, all commercial aircraft have been mandated to be equipped with ADS-B Out
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated.
transponders. Despite the many advantages of locating an aircraft with openly available and
accessible data, it also has some limitations. Firstly, not all aircraft, such as general aviation, are required to transmit their locations; secondly, due to obstacles such as buildings,
a location is not always transmitted at lower altitudes (75-130 m); thirdly, it is vulnerable to cyberattacks. Therefore, while it is convenient to have ADS-B Out data, creating a
computationally efficient alternative methodology for determining the aircraft location is
advisable. This paper investigates the accuracy, efficiency, and computational cost of two
methods of source localization using data taken by an array of microphones: a global optimization (GO) method called the differential evolution (DE) and the conventional beam-
forming approach (CBF). The real-world data required as input for both methods is obtained
with a 64-microphone phased array placed at a distance of 1.14 km from Rotterdam The
Hague Airport (RTHA). The 2-dimensional flight trajectories, i.e., azimuth, and elevation
relative to the array, obtained from the GO and CBF methods, are compared with the ADS-
B Out data for approaching and departing flyovers. Furthermore, the smallest size of an
array required for satisfactory localization accuracy is investigated.
The use of acoustics for drone localisation has gained more interest in recent years. Traditionally, acoustic localisation is done using microphone arrays. The data is processed with methods such as a time-difference-of-arrival approach or beamforming. It has however not been investigated yet if alternative noise monitoring systems can be used for this purpose. The aim of this research is therefore to investigate if noise monitoring systems which output noise directivity can be used for drone localisation. Four SV200A noise monitoring systems were used during a real-life experiment. These systems output the directivity in the XY-plane from 0◦ to 360◦ and in the Z-plane from 0◦ to 180◦ in bins with a width of 11.25◦. During the experiment, a DJI Phantom quadcopter flew a variety of manoeuvres. For performance evaluation, two flight patterns are distinguished: horizontal and vertical flight. Initial results showed limited performance. In the XY-plane, the percentage of time where the correct bin was estimated varied between 20% to 49% depending on the manoeuvre and microphone, whereas in the Z-plane this was only 0.3% to 14%. However, when including the angular bins adjacent to the correct bin, the performance increased significantly. In the XY-plane, the percentages increased to 63% to 98% and in the Z-plane to 29% to 49%. A reason for this increase can be that the GPS receiver of the drone has limited accuracy, which leads to a mismatch between the true flown position and the logged position. Furthermore, due to an offset in internal clocks of the drone and microphones, some microphone estimations were slightly ahead or behind the actual drone movement. In conclusion, these results show that the noise monitoring systems can estimate the location of the drone if some margin in the estimation is taken into account.
...
The use of acoustics for drone localisation has gained more interest in recent years. Traditionally, acoustic localisation is done using microphone arrays. The data is processed with methods such as a time-difference-of-arrival approach or beamforming. It has however not been investigated yet if alternative noise monitoring systems can be used for this purpose. The aim of this research is therefore to investigate if noise monitoring systems which output noise directivity can be used for drone localisation. Four SV200A noise monitoring systems were used during a real-life experiment. These systems output the directivity in the XY-plane from 0◦ to 360◦ and in the Z-plane from 0◦ to 180◦ in bins with a width of 11.25◦. During the experiment, a DJI Phantom quadcopter flew a variety of manoeuvres. For performance evaluation, two flight patterns are distinguished: horizontal and vertical flight. Initial results showed limited performance. In the XY-plane, the percentage of time where the correct bin was estimated varied between 20% to 49% depending on the manoeuvre and microphone, whereas in the Z-plane this was only 0.3% to 14%. However, when including the angular bins adjacent to the correct bin, the performance increased significantly. In the XY-plane, the percentages increased to 63% to 98% and in the Z-plane to 29% to 49%. A reason for this increase can be that the GPS receiver of the drone has limited accuracy, which leads to a mismatch between the true flown position and the logged position. Furthermore, due to an offset in internal clocks of the drone and microphones, some microphone estimations were slightly ahead or behind the actual drone movement. In conclusion, these results show that the noise monitoring systems can estimate the location of the drone if some margin in the estimation is taken into account.