Tracking and data association
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Wireless integrated network sensors
Communications of the ACM
Target tracking with bearings — only measurements
Signal Processing
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed state representation for tracking problems in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Collaborative Data Fusion Tracking in Sensor Networks using Monte Carlo Methods
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
IEEE Transactions on Parallel and Distributed Systems
A probabilistic framework for combining tracking algorithms
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Tracking the direction-of-arrival of multiple moving targets bypassive arrays: algorithm
IEEE Transactions on Signal Processing
General direction-of-arrival tracking with acoustic nodes
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
An efficient PASTd-algorithm implementation for multiple directionof arrival tracking
IEEE Transactions on Signal Processing
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Decentralized processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in smart sensor networks since they provide the ability to scale, reduce vulnerability, reduce communication, and share processing responsibilities among individual nodes. Sharing the processing responsibilities allows parallel processing of raw data at the individual nodes. However, this introduces other difficulties in multi-modal smart sensor networks, such as non-observability of the targets' states at any individual node and various delays such as varying processing delays, communication delays and signal propagation delays for the different modalities. In this paper, we provide a novel algorithm to determine the initial probability distribution of multiple targets' states in a decentralized manner. The targets' state vectors consist of the targets' positions and velocities on the 2D plane. Our approach can determine the state vector distribution even if the individual sensors alone are not capable of observing it. Our approach can also compensate for varying delays among the assorted modalities. The resulting distribution can be used to initialize various tracking algorithms. Our approach is based on Monte Carlo methods, where the state distributions are represented by a weighted set of discrete state realizations. A robust weighting strategy is formulated to account for missed detections, clutter and estimation delays. To demonstrate the effectiveness of the algorithm, we simulate a network with direction-of-arrival nodes and range-Doppler nodes.