Topological mapping for mobile robots using a combination of sonar and vision sensing
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Target tracking based on a distributed particle filter in underwater sensor networks
Wireless Communications & Mobile Computing - Underwater Sensor Networks: Architectures and Protocols
Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach
Robotics and Autonomous Systems
An Interlaced Extended Kalman Filter for sensor networks localisation
International Journal of Sensor Networks
Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Target tracking in wireless sensor networks using compressed Kalman filter
International Journal of Sensor Networks
Computer Vision and Image Understanding
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Mobile Sensor Network Navigation Using Gaussian Processes With Truncated Observations
IEEE Transactions on Robotics
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A new approach is developed to localise and navigate Wireless Sensor Network WSN mobile node with Received Signal Strength Indicator RSSI signal, the approach is economical, convenient and reliable in noisy environment. An efficient Metropolis-Hasting MH particle filter algorithm was proposed to process the signal for ensures the monotonic increasing relationship between RSSI value and the distance between nodes. The coordinate space quantised with RSSI value was selected to describe the state and position of robots to avoid model error. The navigation system consists of several beacon nodes, and each of them is a distributed measurement and control unites. The outputs from every beacon nodes are collected by the navigation control centre, and the final outputs for mobile robots are calculated based on data fusion. Therefore, the real-time performance of this navigation system is enhanced. Furthermore, this system could adapt dynamic or unknown scenarios due to the coordinate of beacon node is not required before navigation. The simulation and experimental results show the effectiveness of this navigation algorithm.