Speckle noise attenuation in orbital laser vibrometer seismology

Courville, S. W.; Sava, Paul

doi:10.1016/j.actaastro.2020.03.016

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…In the case of LDVs, which are equally sensitive to instrument vibration as to the target vibration, recent advances have shown that by using the data obtained from correctly positioned measurement transducers on the LDV or steering optics, the target vibration can be fully recovered [ 12 , 13 ]. While the mobile deployment of LDVs on autonomous vehicles is yet to be realised, their successful integration would augment capability in many in-field applications that have already received interest, for example, buried landmine detection from mobile platforms [ 14 ], orbital seismology [ 15 , 16 , 17 ], terrestrial seismology [ 18 ] and structural health monitoring from drones [ 19 ], thereby, positioning LDVs as powerful yet underutilised tools for mobile deployment on autonomous systems.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks

Darwish

Halkon²,

Oberst³

2022

Sensors

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Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.

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Section: Introductionmentioning

confidence: 99%

Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks

Darwish

Halkon²,

Oberst³

2022

Sensors

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“…We note that Dräbenstedt et al (2016) alludes to extending LDV-based seismic measurements to airborne platforms, but they cite a need for better clock synchronization, sensor vibration isolation, and methods for removing residual motion measured by a reference sensor before doing so. The effect a moving platform has on LDV measurements including the so-called "speckle noise" may be addressed by multiple independent LDV sensors and signal processing algorithms, such as maximal ratio combining (Dräbenstedt 2014;Rembe and Dräbenstedt 2015;Courville and Sava 2019;Courville 2019). Maximal ratio combining leverages an assumption of independence between the LDV signals and the physics of speckle noise to enable measuring signals hidden by noise induced from a moving platform and the speckle pattern.…”

Section: Introductionmentioning

confidence: 99%

Removing residual airborne sensor motion for measuring seismic signals from a drone

Rapstine

Sava

2021

J. Unmanned Veh. Sys.

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Acquiring seismic data using drones requires excellent knowledge of the drone’s motion since positional measurements made from an airborne sensor represent a combination of sensor and ground motion. Recent advancements in laser Doppler vibrometry and repeat lidar surveys show that the frequency and resolution of non-contact motion measurements is increasing to the point necessary for measuring seismic signals. We explore the conditions under which separation of sensor motion from ground motion can be accomplished in practice. We assume (i) that the translation and rotation of a stabilized airborne sensor follows an analytic form in time that is either known or can be estimated from the sensor’s measurements, (ii) that the seismic signal we observe has compact support contained within the measurement window, and (iii) that the ground motion can be described by a rigid translation. We analyze the effectiveness of our signal separation problem as a function of peak signal, sensor noise level, sensor rotation angle, and sensor point sampling density by defining a boundary where SNR = 0 dB for various combinations of these parameters. We find that under the set of assumptions, lower rotation angles, lower sensor noise, and denser point samplings on the ground provide better signal separation using our method.

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Production of BN nanostructures by pulsed laser ablation in liquids: Influence of the applied Nd:YAG harmonics on the structural, optical and photoluminescence properties

Domínguez‐Crespo

Rodríguez

Torres‐Huerta

et al. 2020

Ceramics International

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Speckle noise attenuation in orbital laser vibrometer seismology

Cited by 7 publications

References 41 publications

Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks

Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks

Removing residual airborne sensor motion for measuring seismic signals from a drone

Production of BN nanostructures by pulsed laser ablation in liquids: Influence of the applied Nd:YAG harmonics on the structural, optical and photoluminescence properties

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