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**Neighborhood Kalman estimation for distributed localization in wireless sensor networks.**
*(English)*
Zbl 1400.94068

Summary: Accurate location information plays an important role in the performance of wireless sensor networks since many mission applications depend on it. This paper proposes a fully distributed localization algorithm based on the concept of data fusion, allowing the full intranodes information including the correlations among estimates to take part in the algorithm. By properly constructing and updating the estimates as well as the corresponding covariance matrices, the algorithm can fuse intranodes information to generate more accurate estimates on the sensor locations with a fast speed. Finally, numerical simulations are given as examples to demonstrate the effectiveness of the algorithm.

### MSC:

94A12 | Signal theory (characterization, reconstruction, filtering, etc.) |

62M20 | Inference from stochastic processes and prediction |

### Software:

DILAND
PDFBibTeX
XMLCite

\textit{X. Wang} et al., Math. Probl. Eng. 2016, Article ID 2510903, 8 p. (2016; Zbl 1400.94068)

Full Text:
DOI

### References:

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