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Fig. 7 Costs and benefits of multiple distance estimates.
When there are few beacons (10%%), multiple distance estimates improves significantly the target tracking accuracy.
To do that, multiple distance estimates are fused by a Kalman filter, during the localization process.
With RPE instead, the usage of multiple distance estimates is important for any number of nodes (Fig. 10a, c).
In this work, we combine the same algorithms, but we use data fusion of multiple distance estimates during the localization process to improve the target-tracking accuracy.
Our results show that by filtering multiple distance estimates in the localization algorithms we can improve the tracking accuracy, but the associate communication cost must not be neglected.
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As noted above, our charts may overlap, meaning that multiple distance values d (i ) are estimated whenever a center appears in multiple charts.
To obtain the best distance estimates, the photometric time series (apparent light curves) of SN Ia at multiple wavelengths must be properly modeled.
Most phylogeny [20], [21], community distance estimates [11], [22], and abundance distributions [23] [25] ultimately rely on input from a multiple alignment to compute sequence distances within a consistent alignment template.
In addition, polymorphism and distance estimates are then substantially increased.
We explored the multiple buffer distance zones to explore the sensitivity of spatial access coverage to multiple distance search boundaries.
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multiple imputation estimates
multiple mask estimates
multiple rate estimates
multiple regression estimates
multiple distance constraints
multiple distance bands
multiple risk estimates
multiple distance scales
multiple position estimates
multiple connectivity estimates
multiple mutation estimates
multiple distance functions
multiple channel estimates
multiple distance matrices
multiple distance values
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