Anzeige
Erschienen in:
01.10.2016
A Sensor Self-aware Distributed Consensus Filter for Simultaneous Localization and Tracking
Erschienen in: Cognitive Computation | Ausgabe 5/2016
EinloggenAktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Background/Introduction
Simultaneous localization and tracking (SLAT) has become a very hot topic in both academia and industry for its potential wide applications in robotic equipment, sensor networks and smart devices. In order to exploit the advantages supported by state filtering and parameter estimation, researchers have proposed adaptive structures for solving SLAT problems. Existing solutions for SLAT problems that rely on belief propagation often have limited accuracy or high complexity. To adapt the brain decision mechanism for solving SLAT problems, we introduce a specific framework that is suitable for wireless sensor networks.
Methods
Motivated by the high efficiency and performance of brain decision making built upon partial information and information updating, we propose a cognitively distributed SLAT algorithm based on an adaptive distributed filter, which is composed of two stages for target tracking and sensor localization. The first stage is consensus filtering that updates the target state with respect to each sensor. The second stage employs a recursive parameter estimation that exploits an on-line optimization method for refining the sensor localization. As an integrated framework, each consensus filter is specific to a separate sensor subsystem and gets feedback information from its parameter estimation.
Results
The performance comparison in terms of positioning accuracy with respect to RMSE is shown and the simulation results demonstrate that the proposed ICF-RML performs better than the BPF-RML. This is expected since the distributed estimation with sufficient communication mechanism often achieves higher accuracy than that of less sufficient cases. Furthermore, the performance of the ICF-RML is comparable with that of the BPF-RML even if the latter assumes known prior network topology. We also observe from the results of tracking errors that ICF-RML accomplishes a remarkable improvement in the precision of target tracking and achieves more stable convergence than BPF-RML, in the scenario that all sensors are used to calculate the effect from data association errors.
Conclusion
We apply this approach to formulate the SLAT problem and propose an effective solution, summarized in the paper. For small-size sensor networks with Gaussian distribution, our algorithm can be implemented through a distributed version of weighted information filter and a consensus protocol. Comparing the existing method, our solution shows a higher accuracy in estimation but with less complexity.