Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
The bits and flops of the n-hop multilateration primitive for node localization problems
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Distributed online localization in sensor networks using a moving target
Proceedings of the 3rd international symposium on Information processing in sensor networks
Localization in sensor networks
Wireless sensor networks
Node Localization Using Mobile Robots in Delay-Tolerant Sensor Networks
IEEE Transactions on Mobile Computing
Probabilistic self-localization for sensor networks
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
Accurate sequential self-localization of sensor nodes in closed-form
Signal Processing
Acoustic sensor network node self-localization based on adaptive particle swarm optimization
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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We propose algorithms for distributed sensor self-localization using beacon nodes. These beacon nodes broadcast some information which describes their positions. The sensor nodes with unknown location information utilize these descriptions along with the characteristics of received signals to obtain estimates of their positions. Sensors with resolved positions, in the successive stages of the algorithm also broadcast their location information to other sensors so that they can resolve their own positions. Conditional upon the availability of probabilistic distributions of noise processes, we propose iterative and Monte Carlo sampling-based methods for obtaining sensor location descriptions. We also provide approximate hybrid Cramer-Rao bounds for distributed sensor self-localization and compare them with the proposed algorithms. We demonstrate the performance of the proposed algorithms through extensive computer simulations.