acoustic target tracking using tiny wireless sensor devices qixin wang, wei-peng chen, rong zheng,...
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Acoustic Target Tracking Using Tiny Wireless Sensor Devices
Qixin Wang, Wei-Peng Chen, Rong Zheng, Kihwal Lee, and Lui Sha
Dept. of CS, UIUC
Introduction
Context–Delay based sound source locating algorithm, requires large number of redundant sensors for accuracy.
-Tiny wireless sensors to real-world acoustic tracking applications.
–Tracking only impulsive acoustic signals, such as foot steps, sniper shots etc. No concept of tracking motion.
Challenges:
– Partial info at one sensor site
– Inaccuracy and unreliability of sensors
– Effective use of scarce wireless bandwidth
Solutions:
– Sensor clustering and coordination
– Redundancy for robustness
– Quality-driven (QDR) networking. Info. flow oriented v.s. raw data flow oriented.
Introduction
Introduction
Sink/Pursuer
Cluster Head
ScenarioSensor
Router
Cluster Head
Sink/Pursuer
System Overview• System Architecture
– Acoustic target tracking subsystem
Sensor (mica motes)
Cluster Head (mono-board computer)
Sensors belong to clusters with singular cluster head.
Cluster head knows the locations of its slave sensors. Raw data gathered from sensors are processed in cluster head to generate localization results
– Communication Subsystem: route back the reports generated by cluster heads to sink
Sink
cluster covered area
router (mica motes)
cluster head
System Overview
• Use RBS Time Synch (error 30s).• Onset Detection (on sensors)
–Small sliding window to compute moving average of acoustic signal magnitude.–Use threshold to detect onset time t0.
–Record one buffer load of data, then post-process.
Acoustic Target Tracking Subsystem
• Cross Correlation (to find out delays)
Detected intersted sound
ClusterHead:
Broadcast sound signature
Cross-correlation to detect local arrival time
SlaveSensor:
Report local arrival time
Locate sound src loc.
Acoustic Target Tracking Subsystem
• Sound Source Locating & Evaluation of Quality Rank (main idea)
– Throw away apparently erroneous sensor readings.
– Let A = cluster’s monitored area,
sound src location = argpAmin{|d(p) - ds|},
where d(p) is the hypothetical sensors’ sound arrival time vector, while ds is the actual one. |·| is an error measurement function.
Acoustic Target Tracking Subsystem
– In practice, we cannot check every location in A, instead, we apply a grid with 33inch2 granularity onto A, and only check those grid points.
– Quality Rank = percentage of d(p)’s elements that falls outside boundary of ds .
Acoustic Target Tracking Subsystem
Communication Subsystem
• Quality-driven(QDR) Redundancy Suppression and Contention Resolution
– Redundant clusters may report same event’s location. Good for reliability reasons.
– Quality Rank is used to suppress inferior reports and only report high quality rank localization reports to data sink
– Quality Rank is also used for contention resolution along the routes (with CSMA as MAC) to let higher quality reports get to data sink earlier:
Tbackoff = QualityRank interval + random
Acoustic Target Tracking Subsystem
Experiment• Locations of
sensors and sound sources in a single cluster
• Examples of localization results for different sound source locations
Experiment
• Average error vs. sound source locations. Note sound source is a 4inch speaker
Experiment
Experiment
• % of reports within 3-inch error range: higher quality rank, higher creditabi-lity
Experiment
• Quality-driven (QDR): Effect of various interval on the percen-tage of suppressed reports
Experiment
• Effect of Quality-driven(QDR)
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Suppose info/bit is fixed; the smaller Quality Rank, the better the quality.
Experiment
Conclusion
• Acoustic target tracking using tiny wireless devices with satisfying accuracy is possible.
• Quality Rank can be used to decide the quality of tracking result
• Quality-driven redundancy suppression and contention resolution is effective in improving the information throughput.