This paper describes the signal processing chain for source localization using an acoustic vector... more This paper describes the signal processing chain for source localization using an acoustic vector sensor hosted on a buoyancy Slocum glider. The sensor used is a 3D directional hydrophone capable of acquiring both the acoustic pressure and the components of the particle velocity vector. The paper presents experimental results with data collected at sea and describes the signal processing chain, including detection, direction of arrival and clustering.
Journal of the Acoustical Society of America, May 1, 2007
Polynomial chaos (PC) expansions have recently been derived to model the statistical behavior of ... more Polynomial chaos (PC) expansions have recently been derived to model the statistical behavior of complex modal amplitudes in uncertain waveguides. Here we explore the statistical trajectories of complex modal amplitudes in phase space for one and two degrees freedom (DOF) of waveguide uncertainty. It is shown that for one DOF of uncertainty, the modal amplitudes fall along a trajectory in phase space, while for higher DOFs the phase space of the amplitudes is space filling. Results are compared to phase space characteristics of the adiabatic and saturated limits of waveguide uncertainty, and a discussion of the range progression of the scintillation index of modal amplitude is undertaken. Comparisons between PC estimates of the probability density function of the modal amplitudes and Monte Carlo histograms are also made. [Work supported by ONR.]
Journal of the Acoustical Society of America, May 1, 2007
The polynomial chaos (PC) expansion method has been recently applied to estimating the statistica... more The polynomial chaos (PC) expansion method has been recently applied to estimating the statistical properties of underwater acoustic propagation in the presence of environmental uncertainty. Here we use PC estimates of the field covariance structure to design uncertainty robust match field processing (MFP) weights using Krolik’s minimum variance beamformer with sound-speed perturbation constraints (MV-SPC) [J. Acoust. Soc. Am. 92 (3), 1408–1419 (1992)]. The idea behind the MV-SPC beamformer is to realize much of the high sidelobe rejection of MV processors in environments with environmental variability by opening up the signal model to include uncertainty effects. Here we compare the performance of MV-SPC designed with an adiabatic signal uncertainty model to the same beamformer designed with the PC signal model for realizations of the signal vector obtained with fully coupled propagation models, with results showing the superiority of the PC approach. [Work supported by ONR.]
Journal of the Acoustical Society of America, Oct 25, 2002
In a previous paper the author derived closed form expressions for the average intensity of broad... more In a previous paper the author derived closed form expressions for the average intensity of broadband time series averaged over oceanographic variability in range independent wave guides [LePage, J. Comput. Acoust. 4 (2001)]. Here, equivalent expressions are derived for the expected value of reverberation intensity. Examples are computed which show that for time series, oceanographic variability most strongly affects the earliest arrivals, while bottom variability most strongly affects the late arrivals. For reverberation, the relative sensitivity to bottom and oceanographic variability were explored using the new model. [Work supported under the ONR capturing uncertainty DRI.]
Cooperative autonomy and data sharing can largely improve the mission performance of robotic netw... more Cooperative autonomy and data sharing can largely improve the mission performance of robotic networks in underwater surveillance applications. In this paper, we describe the cooperative autonomy used to control the Autonomous Underwater Vehicles (AUVs) acting as sonar receiver nodes in the CMRE Anti-Submarine Warfare (ASW) network. The paper focuses on a track management module that was integrated in the robot autonomy software for enabling the share of information. Track to track (T2T) associations are used for improving track classification and for creating a common tactical picture, necessary for AUV cooperative strategies. We also present a new cooperative data-driven AUV behaviour that exploits the spatial diversity of multiple robots for improving target tracking and for facilitating T2T associations. We report results with real data collected at sea that validate the approach. The reported results are one of the first examples that show the potential of cooperative autonomy and data fusion in realistic underwater surveillance scenarios characterised by limited communications.
Global Oceans 2020: Singapore – U.S. Gulf Coast, Oct 5, 2020
We propose a cooperative adaptive behaviour to control multiple underwater robots for localising ... more We propose a cooperative adaptive behaviour to control multiple underwater robots for localising and tracking targets using bearing-only measurements. The behaviour uses as perception layer an Occupancy Grid (OG) Mapping-based framework presented in our recent work. The produced maps show the probability of target presence at different locations. This information is exchanged and fused between the robots to produce maps that allow to estimate the x - y target position. Using these OG maps, the robots make non-myopic coordinated decisions for their heading angles to create favourable geometric network configurations. The reached configurations increase target probability of detection and improve target localisation. The developed control framework is generic, distributed in nature and is suited to control the underwater vehicles of the passive sonar network under development at CMRE. We report results of nontrivial simulations of the developed method that demonstrate its effectiveness in controlling two underwater robots equipped with passive sonars in a realistic underwater surveillance scenario.
We address the problem of building the perception layer for controlling a network of autonomous, ... more We address the problem of building the perception layer for controlling a network of autonomous, sensorised robots in an underwater surveillance application. To this purpose, we propose a novel Occupancy Grid (OG) mapping framework, based on an adaptation to the standard OG method. The method iteratively builds an OG map from successive acoustic measurements. The resulting maps, produced in real-time by each robot, show the probability of each grid cell containing a target. The algorithm is designed to handle the presence of multiple targets and takes into account a dynamic world (moving targets). Albeit generic, the proposed method was adapted to work with passive sonar sensors that produce bearing-only measurements. Results from simulations demonstrate the capability of the method to create maps showing regions likely containing targets. They also show how data fusion between maps produced with sensors spatially separated can achieve target localisation and tracking. The produced OG maps provide valuable guidance for robot cooperative decision making. They identify interesting areas to survey (higher target presence probability), regions likely empty and areas not adequately monitored.
Journal of the Acoustical Society of America, Apr 1, 2022
One of John Preston's achievements during his career was the hosting of multinational sonar e... more One of John Preston's achievements during his career was the hosting of multinational sonar experimentation efforts whilst a scientist at SACLANTCEN. The Littoral Continuous Active Sonar Multi-National Joint Research Project is the most recent international collaborative experimentation activity focused on sonar hosted by NATO STO Centre for Maritime Research and Experimentation, SACLANTCEN's sucessor. Between 2014 and 2020 LCAS brought scientists and engineers from 7 NATO and Partner Nations together with the CMRE to evaluate the effectiveness of continuous active sonar in shallow littoral environments. In this talk, the objectives of the project are laid out, the scientific issues and experimental approach reviewed, details about the four sea trials conducted under LCAS are presented, and a summary of the major results of the project is provided.
We present the integration of an embedded autonomy engine into a Liquid Robotics Wave Glider surf... more We present the integration of an embedded autonomy engine into a Liquid Robotics Wave Glider surface vehicle, following the “backseat-driver” control paradigm. We describe the Wave Glider Autonomy Adapter software module, designed for handling the communication between a Backseat payload computer, installed by the vehicle end-user, with the manufacturer Frontseat Command and Control Unit. The module runs in the MOOS middleware environment and enables the intelligent Cooperative Autonomous Decision Making Engine (iCADME), running on the Backseat, to execute a mission involving autonomous decision-making and to issue the produced commands to the Wave Glider Frontseat autopilots, hence controlling the vehicle navigation. This makes the CMRE Wave Glider fully autonomous and capable to make decisions onboard, with no need of communication with the Command and Control (C2) centre. This reduces the need of data exchange with the C2 centre through the traditionally used satellite link. In this way, the vehicle planning and control are significantly simplified. This paves the way to the deployment of highly scalable networks, composed of cooperative Wave Gliders, capable to operate effectively in complex missions with only a limited supervision by the C2 centre. We report successfully tests of the fully autonomous Wave Glider conducted during the DANS20 trial, in December 2020. The onboard autonomy was able to successfully control the vehicle both by issuing direct waypoints commands and by using the MOOS-IvP Helm behaviour-based autonomy for producing heading reference for the Wave Glider autopilots.
This paper describes the signal processing chain for source localization using an acoustic vector... more This paper describes the signal processing chain for source localization using an acoustic vector sensor hosted on a buoyancy Slocum glider. The sensor used is a 3D directional hydrophone capable of acquiring both the acoustic pressure and the components of the particle velocity vector. The paper presents experimental results with data collected at sea and describes the signal processing chain, including detection, direction of arrival and clustering.
Journal of the Acoustical Society of America, May 1, 2007
Polynomial chaos (PC) expansions have recently been derived to model the statistical behavior of ... more Polynomial chaos (PC) expansions have recently been derived to model the statistical behavior of complex modal amplitudes in uncertain waveguides. Here we explore the statistical trajectories of complex modal amplitudes in phase space for one and two degrees freedom (DOF) of waveguide uncertainty. It is shown that for one DOF of uncertainty, the modal amplitudes fall along a trajectory in phase space, while for higher DOFs the phase space of the amplitudes is space filling. Results are compared to phase space characteristics of the adiabatic and saturated limits of waveguide uncertainty, and a discussion of the range progression of the scintillation index of modal amplitude is undertaken. Comparisons between PC estimates of the probability density function of the modal amplitudes and Monte Carlo histograms are also made. [Work supported by ONR.]
Journal of the Acoustical Society of America, May 1, 2007
The polynomial chaos (PC) expansion method has been recently applied to estimating the statistica... more The polynomial chaos (PC) expansion method has been recently applied to estimating the statistical properties of underwater acoustic propagation in the presence of environmental uncertainty. Here we use PC estimates of the field covariance structure to design uncertainty robust match field processing (MFP) weights using Krolik’s minimum variance beamformer with sound-speed perturbation constraints (MV-SPC) [J. Acoust. Soc. Am. 92 (3), 1408–1419 (1992)]. The idea behind the MV-SPC beamformer is to realize much of the high sidelobe rejection of MV processors in environments with environmental variability by opening up the signal model to include uncertainty effects. Here we compare the performance of MV-SPC designed with an adiabatic signal uncertainty model to the same beamformer designed with the PC signal model for realizations of the signal vector obtained with fully coupled propagation models, with results showing the superiority of the PC approach. [Work supported by ONR.]
Journal of the Acoustical Society of America, Oct 25, 2002
In a previous paper the author derived closed form expressions for the average intensity of broad... more In a previous paper the author derived closed form expressions for the average intensity of broadband time series averaged over oceanographic variability in range independent wave guides [LePage, J. Comput. Acoust. 4 (2001)]. Here, equivalent expressions are derived for the expected value of reverberation intensity. Examples are computed which show that for time series, oceanographic variability most strongly affects the earliest arrivals, while bottom variability most strongly affects the late arrivals. For reverberation, the relative sensitivity to bottom and oceanographic variability were explored using the new model. [Work supported under the ONR capturing uncertainty DRI.]
Cooperative autonomy and data sharing can largely improve the mission performance of robotic netw... more Cooperative autonomy and data sharing can largely improve the mission performance of robotic networks in underwater surveillance applications. In this paper, we describe the cooperative autonomy used to control the Autonomous Underwater Vehicles (AUVs) acting as sonar receiver nodes in the CMRE Anti-Submarine Warfare (ASW) network. The paper focuses on a track management module that was integrated in the robot autonomy software for enabling the share of information. Track to track (T2T) associations are used for improving track classification and for creating a common tactical picture, necessary for AUV cooperative strategies. We also present a new cooperative data-driven AUV behaviour that exploits the spatial diversity of multiple robots for improving target tracking and for facilitating T2T associations. We report results with real data collected at sea that validate the approach. The reported results are one of the first examples that show the potential of cooperative autonomy and data fusion in realistic underwater surveillance scenarios characterised by limited communications.
Global Oceans 2020: Singapore – U.S. Gulf Coast, Oct 5, 2020
We propose a cooperative adaptive behaviour to control multiple underwater robots for localising ... more We propose a cooperative adaptive behaviour to control multiple underwater robots for localising and tracking targets using bearing-only measurements. The behaviour uses as perception layer an Occupancy Grid (OG) Mapping-based framework presented in our recent work. The produced maps show the probability of target presence at different locations. This information is exchanged and fused between the robots to produce maps that allow to estimate the x - y target position. Using these OG maps, the robots make non-myopic coordinated decisions for their heading angles to create favourable geometric network configurations. The reached configurations increase target probability of detection and improve target localisation. The developed control framework is generic, distributed in nature and is suited to control the underwater vehicles of the passive sonar network under development at CMRE. We report results of nontrivial simulations of the developed method that demonstrate its effectiveness in controlling two underwater robots equipped with passive sonars in a realistic underwater surveillance scenario.
We address the problem of building the perception layer for controlling a network of autonomous, ... more We address the problem of building the perception layer for controlling a network of autonomous, sensorised robots in an underwater surveillance application. To this purpose, we propose a novel Occupancy Grid (OG) mapping framework, based on an adaptation to the standard OG method. The method iteratively builds an OG map from successive acoustic measurements. The resulting maps, produced in real-time by each robot, show the probability of each grid cell containing a target. The algorithm is designed to handle the presence of multiple targets and takes into account a dynamic world (moving targets). Albeit generic, the proposed method was adapted to work with passive sonar sensors that produce bearing-only measurements. Results from simulations demonstrate the capability of the method to create maps showing regions likely containing targets. They also show how data fusion between maps produced with sensors spatially separated can achieve target localisation and tracking. The produced OG maps provide valuable guidance for robot cooperative decision making. They identify interesting areas to survey (higher target presence probability), regions likely empty and areas not adequately monitored.
Journal of the Acoustical Society of America, Apr 1, 2022
One of John Preston's achievements during his career was the hosting of multinational sonar e... more One of John Preston's achievements during his career was the hosting of multinational sonar experimentation efforts whilst a scientist at SACLANTCEN. The Littoral Continuous Active Sonar Multi-National Joint Research Project is the most recent international collaborative experimentation activity focused on sonar hosted by NATO STO Centre for Maritime Research and Experimentation, SACLANTCEN's sucessor. Between 2014 and 2020 LCAS brought scientists and engineers from 7 NATO and Partner Nations together with the CMRE to evaluate the effectiveness of continuous active sonar in shallow littoral environments. In this talk, the objectives of the project are laid out, the scientific issues and experimental approach reviewed, details about the four sea trials conducted under LCAS are presented, and a summary of the major results of the project is provided.
We present the integration of an embedded autonomy engine into a Liquid Robotics Wave Glider surf... more We present the integration of an embedded autonomy engine into a Liquid Robotics Wave Glider surface vehicle, following the “backseat-driver” control paradigm. We describe the Wave Glider Autonomy Adapter software module, designed for handling the communication between a Backseat payload computer, installed by the vehicle end-user, with the manufacturer Frontseat Command and Control Unit. The module runs in the MOOS middleware environment and enables the intelligent Cooperative Autonomous Decision Making Engine (iCADME), running on the Backseat, to execute a mission involving autonomous decision-making and to issue the produced commands to the Wave Glider Frontseat autopilots, hence controlling the vehicle navigation. This makes the CMRE Wave Glider fully autonomous and capable to make decisions onboard, with no need of communication with the Command and Control (C2) centre. This reduces the need of data exchange with the C2 centre through the traditionally used satellite link. In this way, the vehicle planning and control are significantly simplified. This paves the way to the deployment of highly scalable networks, composed of cooperative Wave Gliders, capable to operate effectively in complex missions with only a limited supervision by the C2 centre. We report successfully tests of the fully autonomous Wave Glider conducted during the DANS20 trial, in December 2020. The onboard autonomy was able to successfully control the vehicle both by issuing direct waypoints commands and by using the MOOS-IvP Helm behaviour-based autonomy for producing heading reference for the Wave Glider autopilots.
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Papers by Kevin LePage