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From the perspective of location estimation accuracy, centralized algorithms are likely to provide more accurate location estimates than distributed algorithms.
Now, each node has the Euclidean distances, namely the estimated distances, to all the neighbors through location estimates.
Fig. 10 The RMSE of the location estimates vs. SNR.
13: Fuse all beliefs into one and compute location estimates.
In the process, location estimates are refined iteratively.
Fig. 11 Sensor distribution, location estimates and the field reconstructions.
However, the high false positive rate of flip prediction incorrectly rejects many reliable location estimates.
The results have shown that the developed algorithm can achieve highly accurate fault location estimates.
We remark that when we incorporate wall roughness, location estimates are biased.
The location estimates with smaller residuals have larger chances of corresponding to the correct target position.
The solid lines correspond to the pairwise distance estimates computed from the MDS location estimates.
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