Distributed Acoustic Sensing
From CSL Wiki
NOTE!!! this page is out of date - you should go to http://www.lecs.cs.ucla.edu/wiki/index.php/Acoustic_Platforms
Contents |
Introduction
Distributed acoustic sensing is one of the early and persistent challenges for distributed sensing. The ability to detect, classify and localize acoustic events of interest over a given area is extremely useful for bioacoustics research.
This page describes the hardware and software platforms we have created to support distributed acoustic sensing.
Acoustic ENSBox: A System of Self-Calibrating Distributed Acoustic Arrays
Acoustic ENSBox is an ad-hoc deployable wireless system designed to support distributed acoustic sensing applications. In addition to the hardware, the Acoustic ENSBox includes a complete stack of system software designed to support distributed acoustic sensing. The system autonomously forms an ad-hoc wireless network that supports inter-node coordination, hosts routing services and reports diagnostics to a user with a laptop. It supports accurate time synchronized sampling, enabling application programmers to trivially compare time series data taken at the same time at two or more nodes. An acoustic localization system (described below) autonomously and accurately estimates relative position and orientation for all nodes in the system.
With this stack of system software, this platform is ideal for many types of collaborative sensing, especially target localization algorithms based on “beam-crossing”, where multiple states estimate bearing to a target and combine their estimates to compute a location. We hope to see the Acoustic ENSBox platform taken up by several groups at UCLA who are involved in acoustic localization projects. We are currently working with Prof. Kung Yao's group to compare their bearing estimate algorithms to those developed for the position estimation application. We are also working with a student from Prof. Charles Taylor's group who is developing software on the Acoustic ENSBox platform to detect acorn woodpecker calls.
History
We performed early work in acoustic ranging using wideband audible acoustics (2000) and further developed this work as part of the GALORE project (2001-2002) and the SHM system at Sensoria (2001-2003). In 2005 we started developing a new acoustic ranging system designed for the Acoustic ENSBox. This capability is a critical part of its usefulness as a distributed acoustic sensing system, because the beam-crossing localization applications we want to develop require precise estimation of the 3-D location and orientation of the sensor arrays.
Capabilities
This work has been highly successful, resulting in a highly accurate, self--configuring localization system. This system estimates the 3-D position and orientation of a collection of nodes, with no prior knowledge or anchor points. The resulting relative coordinate system is then fit to anchor points if absolute coordinates are required. The system works outdoors and, unlike many competing systems, is highly resilient to environmental noise and obstructing foliage. In tests localizing 10 nodes in a forested, hilly region 70m x 50m, the system achieved an average of 20 cm position error.
Our system also leverages the 8cm baseline microphone array supported by the Acoustic ENSBox to estimate 3--D direction of arrival (DOA). In controlled tests, we achieved an error distribution with a standard deviation of 0.96 degrees, and a maximum range of +/- 2 degrees. These results represent an improvement upon similar published work.
The images to the right show the conditions of our James Reserve test. Firstly, there is an approximate deployment laydown plotted on an aerial photograph of the James Reserve location (a UC Reserve located in Idyllwild, CA and managed by UC Riverside). Secondly, an image shows the conditions present between nodes 108 and 104. The red box contains a magnification of the corresponding box in the image. As you can see the environment has some dense foliage and line of sight was partially obstructed.
Acoustic ENSBox Platforms
The original acoustic ENSBox was version 1. This is the subject of the PhD thesis "A Self-Calibrating System of Distributed Acoustic Arrays", and and in the powerpoint presentation, which describe in detail the acoustic ENSBox.
Version 1
The platform is a self-contained unit containing an ARM-based CPU module, a wireless network interface, a 4-channel acoustic sampling interface, and a battery. The system connects to a “head unit” that hosts an array of 4 microphones and 4 piezo tweeters in a Lucite and aluminum chassis. The microphones are condenser microphones with a custom pre-amplifier board. The photo at right shows an Acoustic ENSBox node deployed in the James Reserve. The Acoustic ENSBox is not intended for long-term unattended deployments. The system will run for about 24 hours continuously on a single 12V 7.2AH gel cell. Longer term deployments could be achieved by duty-cycling the system.
More detailed information can be found in the aforementioned thesis and presentation.
The data collected to test this system is available from our data repository at this link.
Version 2
This is the latest, ready-to-deploy box, which is far more integrated.
Laptop
We have also created an 'Acoustic Laptop' as a platform for exploratory research and prototyping. It mirrors the features of the Acoustic ENSBox but on an x86 architecture. A paper describing the goals of the Acoustic Laptop has been accepted and will be published at the Emnets 2007 Workshop.
Software
All the software used to build this system is located in the CENS CVS repository - it is based around the Emstar framework.
There is also some helpful information on using the system located in the CSL wiki.
- Drivers for the VXP440 sound card are in emstar/sensors/audiod/vxpc_server
- Time synchronization support is in emstar/timesync
- The ranging system software is in emstar/devel/loc/ar
- The multilateration software is in emstar/devel/loc/multilat
- The StateSync multihop routing and coordination primitive is in emstar/devel/state
- The diagnostic reporting and control software is in emstar/emproxy (emrun, emview, emproxy and rbsh)
Performance
The experimentation in this section refers to work carried out on the Version 1 ENSBox. Version 2's performance is expected to be comparable, but has not been as extensively verified at present. Two sets of experiments were carried out - ranging/localization and direction of arrival. These are described in more detail in the PhD thesis "A Self-Calibrating System of Distributed Acoustic Arrays".
Ranging and Self-Localization
The two graphs below show some of the results from our tests in the James Reserve. The first two graphs show the accuracy of our Azimuth estimator under controlled conditions. This component estimates the incoming direction of a ranging signal. We see that the standard deviation of the estimates is 0.96 degrees while the total range of errors is 4 degrees. We suspect that these errors may result from the fact that we do not calibrate the array geometry (i.e. each array’s microphones will be positioned slightly differently.
The next two graphs (below) show the observed error in tests of zenith angle estimation (degrees above or below the horizon). Negative angles perform poorly because the signal arrives from below the array and is significantly blocked by the base of the array.
The next two graphs show the performance of the ranging system. We see that on the whole the ranges are within 5 cm and are not significantly affected as a function of distance. Note that this data was scaled to compensate for temperature, but only a single temperature value was used even though the test was conducted in a semi-outdoor environment over the course of several hours. We expect that some of the error is due to environmental changes. Note that when we run the multilateration system we avoid this issue by taking all the measurements in a brief span of time during which we can assume that environmental changes were small. In the distribution graph we show two curves. The narrower distribution is formed by dropping all “outliers”: points with more than 10 cm of error.
Finally, the next graph shows the error observed in X/Y position. For this graph, we ran 6 trials and computed position estimates for each trial. Then, we compared the X and Y position estimates for each node over the 6 trials. In the graph below, each node is represented by a pair of X/Y error bars. The error bars represent the distribution of X and Y coordinate estimates, considered separately for each node. Then, to represent them all on a single graph, we subtracted out the “ground truth” positions, so that we only see the error or bias in the results.
Thus, if this system worked perfectly, all of the error bars would be very small and centered at (0,0). Because the system is not perfect, the nodes are offset from the origin, and that offset represents the average bias in the position for that node. From the graph, we can see that except for one node, they are all within about 10 cm of their ground truth locations (in fact, the average is 7 cm). If we also include the Z axis, the average position error is 20 cm. This is because Z is less well constrained, since the nodes are mostly in a plane.
DoA estimation
To test the performance of the direction of arrival estimation using the acoustic array system, we ran two tests using the acoustic self-localization software and a special test setup. The details of the test setup are described in [], but the basic setup was to have a receiver array on a tripod in the center of a square area, with an emitter in one corner. A laser was used to point at the edge of the square area, to accurately measure the ground truth array angle. We then performed two experiments.
The data from these experiments is contained in these two files:
In the first experiment (file entries 7076-7585), we rotated the array through 360 deg of azimuth, with the source at 0 degrees zenith.
In the second (file entries 7586-8082), we set the array on its side and rotated it again through 360 degrees. Since the array was on its side, this made the signals appear to come from a variety of elevations. The source arrived from several ranges of 3D angle:
1. 270 deg azi, from 0 to -90 deg zen
2. 90 deg azi, from -90 to 90 deg zen
3. 270 deg azi, from 90 to 0 deg zen
To use gnuplot to see correspondence for azimuth and zenith angles using these files:
plot "asendlog.htm" using 2:3, "filtered.htm" using 2:4 plot "asendlog.htm" using 2:3, "filtered.htm" using 2:13
The errors in these results are likely the result of a non-optimal array configuration. We plan to improve the array configuration in the next version.
Application-based experimentation
Bird Localization
Data from UCLA Biology recorded during testing at James Reserve. The source is a birdsong playing from a speaker at either "center" (marked) or "far" (which is about 20m outside the deployment). There is also a relatively loud and quiet source (antbird vs owl calls).
Directory of videos of bird localization. (mov files are quicktime, avi are msmpeg encoded)
A graphic showing the array deployment geometry and the source positions.
RMBL 2006
The IPSN 2007 paper is about the experimentation at RMBL.
This work was about the online event detector and offline analysis of marmot localization using the AML algorithm.
IPSN 2007 demo
We did a demo at IPSN as well, shown in the YouTube video link below. The demo was an online version of the work presented in the IPSN paper, where we used a dog whistle instead of a pre-recorded marmot chirp. In the video, you can see a person walking in a grassy area. When he blows the whistle, the camera pans to the video display. Blue dots appear on the screen, representing the nodes that detected the whistle. Then,after the data is fused together a new log-likelihood map will appear and a red dot indicates the most likely location of the source as determined by the algorithm.
Mexico
Coming soon...
Publications
M. Allen, L. Girod, and D. Estrin, "Acoustic Laptops as a research enabler". In the Fourth Workshop on Embedded Networked Sensors (EmNets 2007), Cork, Ireland, June 2007. .pdf
A. Ali, T. Collier, L. Girod, K. Yao, C.E. Taylor, D.T. Blumstein, "An Empirical Study of Collaborative Acoustic Source Localization". In Information Processing in Sensor Networks (IPSN07). .pdf
V. Trifa, L. Girod, T. Collier, D.T. Blumstein, C.E. Taylor, "Automated wildlife monitoring using self-configuring sensor networks deployed in natural habitats". In International Symposium on Artificial Life and Robotics (AROB07), Beppu, Japan, January 2007. Best Paper. .pdf
L. Girod, M. Lukac, V. Trifa, and D. Estrin, "The Design and Implementation of a Self-calibrating Acoustic Sensing Platform". In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys 2006), Boulder, CO. November, 2006. .pdf
L. Girod, N. Ramanathan, J. Elson, T. Stathopoulos, M. Lukac, A. Parker, and D. Estrin, "EmStar: a Software Environment for Developing and Deploying Heterogeneous Sensor-Actuator Networks". In press, ACM Transactions on Sensor Networks. .pdf
L. Girod, "A Self-Calibrating System of Distributed Acoustic Arrays", Ph.D. Thesis, UCLA, 2005. .pdf (Note that the results of the test in the forest have been improved since the thesis was published. We found that the largest source of error in our results were inaccuracies in our initial survey of ground truth. After re--surveying, we achieved the results quoted above.)
L. Girod, M. Lukac, A. Parker, T. Stathopoulos, J. Tseng, H. Wang, D. Estrin, R. Guy, and E. Kohler, “A Reliable Multicast Mechanism for Sensor Network Applications”. Center for Embedded Networked Sensing Technical Report #48, April 25, 2005. .pdf
Hanbiao Wang, Lewis Girod, Nithya Ramanathan, Deborah Estrin and Kung Yao, "A Platform for Collaborate Acoustic Signal Processing" CENS Technical Report 00XX, November 28, 2004. .pdf .ps
W. Merrill, L. Girod, J. Elson, K. Sohrabi, F. Newberg, W. Kaiser, "Autonomous Position Location in Distributed, Embedded, Wireless Systems", In Proceedings of the IEEE CAS Workshop on Wireless Communications and Networking, Pasadena, CA, Sept. 2002. .pdf
Lewis Girod, Vladimir Bychkovskiy, Jeremy Elson, and Deborah Estrin, "Locating tiny sensors in time and space: A case study", .ps, .pdf In Proceedings of the International Conference on Computer Design (ICCD 2002), Freiburg, Germany. September 16-18 2002. Invited paper.
Jeremy Elson, Lewis Girod, and Deborah Estrin, "Short Paper: A Wireless Time-Synchronized COTS Sensor Platform, Part I: System Architecture" .ps, .pdf In Proceedings of the IEEE CAS Workshop on Wireless Communications and Networking, Pasadena, California. September 5-6 2002.
L. Girod, D. Estrin, "Robust Range Estimation Using Acoustic and Multimodal Sensing" .ps .pdf IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2001) , Maui, Hawaii, October 2001.
L. Girod, "Development and Characterization of an Acoustic Rangefinder" .ps Technical Report USC-CS-00-728, April 2000.
Talks/Presentations
- "The Design and Implementation of a Self-Calibrating Distributed Acoustic Sensing Platform (revised)", Talk given at UC Boulder, July 2006. .ppt
- "The Design and Implementation of a Self-Calibrating Distributed Acoustic Sensing Platform", Talk given at UMASS Amherst, April 2006. .ppt
- "A Self-Calibrating System of Distributed Acoustic Arrays", Brief presentation to Prof. Kung Yao's group, December 13, 2005. .ppt
- "A Self-Calibrating System of Distributed Acoustic Arrays", Final Ph.D. Defense, November 14, 2005. .ppt
- "Acoustic ENSBox: A Deployable Platform for System Calibration and Collaborative Acoustic Sensing". Presentation to the 2005 CENS Research Review. .ppt
- "A Deployable Platform for Collaborative Acoustic Sensing". Presentation to the MIT CSAIL (Prof. Seth Teller), Cambridge, MA, June 1, 2005. .ppt
- "Design Lessons from an Unattended Ground Sensor System", lecture given in Berkeley CS294-1 (Deeply Embedded Network Systems/Prof. David Culler), September 23, 2003. .PPT
- "Locating Tiny Sensors in Space and Time", Poster and demo presented at Mobicom 2002, Atlanta, GA. .PPT
- "GALORE Localization Project Demo", demo and slides, given with Jeremy Elson at the DARPA/NEST PI Meeting, July 2002, Bar Harbor, ME. .ppt
- "Robust Range Estimation Using Acoustic and Multimodal Sensing", presented at IROS-2001, Maui, HA, October 2001. .PPT
Links
Acoustic_Monitoring_System - Legacy page
Documentation
AcousticLaptop - Acoustic Laptop Installation Docs
GumstixEmstar - compile and install gumstix r1161 with emstar
How to run the system standalone - Running a single node
How to record data - Recording data on one/more nodes
Troubleshooting a failed node - When things go wrong
How to reflash a Slauson - For ENSBox V1 and V2
Rebuild the software distribution - For ENSBox V1 and V2
Use in deployment - Using the nodes in the field
How to replicate the AENSBox - Making a V1 Box
AcousticLaptop - Acoustic Laptop Installation Docs
[1] ENSBox files
