Introduction to algorithms
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Energy-efficient surveillance system using wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Slip surface localization in wireless sensor networks for landslide prediction
Proceedings of the 5th international conference on Information processing in sensor networks
Tracking Probabilistic Correlation of Monitoring Data for Fault Detection in Complex Systems
DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
Identification of low-level point radioactive sources using a sensor network
ACM Transactions on Sensor Networks (TOSN)
Time-bounded essential localization for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Information quality-aware tracking in uncertain sensor network
International Journal of Sensor Networks
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In this paper, we present a Gaussian mixture model based approach to capture the spatial characteristics of any target signal in a sensor network, and further propose a temporally-adaptive variant of the approach for dynamic multiple target tracking under changing environments, with the presence of both significant background event noises and a large portion of outlying sensor readings. The target position is estimated by adopting the mean-shift optimization to discriminate the target signals from the background noises. Our mixture model based algorithm is capable of fusing multivariate real-valued sensor measurements and its probability nature shows fault tolerance and robustness in noisy sensing environments. This consideration is practical as in real world applications, sensor readings are multi-modal and may contain errors. The simulation study validates our design and the results indicate that our mixture model based algorithm is an effective and capable approach for the two most typical target signal models under consideration. Desirable quantitative target tracking results are also achieved through extensive evaluations under challenging background conditions.