3,636 research outputs found
Signal Processing and Propagation for Aeroacoustic Sensor Networking,” Ch
Passive sensing of acoustic sources is attractive in many respects, including the relatively low signal bandwidth of sound waves, the loudness of most sources of interest, and the inherent difficulty of disguising or concealing emitted acoustic signals. The availability of inexpensive, low-power sensing and signal-processing hardware enables application of sophisticated real-time signal processing. Among th
Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications
The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version
Self-Localization of Ad-Hoc Arrays Using Time Difference of Arrivals
This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1
Adaptive sampling in autonomous marine sensor networks
Submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy at the Massachusetts Institute of Technology and the
Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control
in complex environments with multiple constraints, and an approach to cooperative
robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches.
The mobility of the sensor platforms is a key advantage of this strategy, allowing
dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time.
Experimental results are presented for a 2-D target tracking application in which fully
autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification
platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate.
In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the
behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based
control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research
assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network
VIGICOP: autonomous surveillance robots with Sodar detection and autonomous navigator
The main goal of the project described in this paper
is to create a security system using autonomous surveillance
robots that use SODAR-like detection system sensors,
working with acoustic signals in air environment and
navigation base on Geographic Information System and
Markov's models. The surveillance system based on SONAR
provides great information from the environment, even lets you
see behind objects (rebounds effects) whose manipulation
offers a great added value to surveillance The guide system
will implement in one hand a local navigation module directed
to avoid obstacles based on classical techniques and using
the new SODAR sensor. On the other hand a global
navigation module will be implemented using preset
trajectories and gradient techniques and an auto-location
system. One of the greatest challenges obtained is the
definition of the VIGICOPVar variable that defines, depending
on the environment and safety parameters, the probability of
intrusion. Surveillance experts of GRUPO NORTE
(multinational company with security expertise of more than 38
years) have worked In the definition and validation of the
model. The monitoring robots will be controlled in a centralized
way from an alarm center from where you can manage all
information relating to intrusion detected. VIGICOP is the low
cost surveillance robot which provides new/full information
interactive surveillance informatio
EXPERIMENTAL EVALUATION OF MODIFIED PHASE TRANSFORM FOR SOUND SOURCE DETECTION
The detection of sound sources with microphone arrays can be enhanced through processing individual microphone signals prior to the delay and sum operation. One method in particular, the Phase Transform (PHAT) has demonstrated improvement in sound source location images, especially in reverberant and noisy environments. Recent work proposed a modification to the PHAT transform that allows varying degrees of spectral whitening through a single parameter, andamp;acirc;, which has shown positive improvement in target detection in simulation results. This work focuses on experimental evaluation of the modified SRP-PHAT algorithm. Performance results are computed from actual experimental setup of an 8-element perimeter array with a receiver operating characteristic (ROC) analysis for detecting sound sources. The results verified simulation results of PHAT- andamp;acirc; in improving target detection probabilities. The ROC analysis demonstrated the relationships between various target types (narrowband and broadband), room reverberation levels (high and low) and noise levels (different SNR) with respect to optimal andamp;acirc;. Results from experiment strongly agree with those of simulations on the effect of PHAT in significantly improving detection performance for narrowband and broadband signals especially at low SNR and in the presence of high levels of reverberation
Passive Multi-Target Tracking Using the Adaptive Birth Intensity PHD Filter
Passive multi-target tracking applications require the integration of
multiple spatially distributed sensor measurements to distinguish true tracks
from ghost tracks. A popular multi-target tracking approach for these
applications is the particle filter implementation of Mahler's probability
hypothesis density (PHD) filter, which jointly updates the union of all target
state space estimates without requiring computationally complex
measurement-to-track data association. Although this technique is attractive
for implementation in computationally limited platforms, the performance
benefits can be significantly overshadowed by inefficient sampling of the
target birth particles over the region of interest. We propose a multi-sensor
extension of the adaptive birth intensity PHD filter described in (Ristic,
2012) to achieve efficient birth particle sampling driven by online sensor
measurements from multiple sensors. The proposed approach is demonstrated using
distributed time-difference-of-arrival (TDOA) and
frequency-difference-of-arrival (FDOA) measurements, in which we describe exact
techniques for sampling from the target state space conditioned on the
observations. Numerical results are presented that demonstrate the increased
particle density efficiency of the proposed approach over a uniform birth
particle sampler.Comment: 21st International Conference on Information Fusio
Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization
© 2019 IEEE. Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising
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