Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Inference in sensor networks: graphical models and particle methods
Inference in sensor networks: graphical models and particle methods
Monte Carlo localization for mobile wireless sensor networks
Ad Hoc Networks
Marginalized population Monte Carlo
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Ad Hoc Networks
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Indoor positioning using nonparametric belief propagation based on spanning trees
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
Minimum cost localization problem in wireless sensor networks
Ad Hoc Networks
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
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Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSNs), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it offline. Therefore, in this article, we propose more flexible and efficient variants of NBP for cooperative localization in mobile networks. In particular, we provide: (i) an optional 1-lag smoothing done almost in real-time, (ii) a novel low-cost communication protocol based on package approximation and censoring, (iii) higher robustness of the standard mixture importance sampling (MIS) technique, and (iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.