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In the paper [1], the following errors are present on pages 4, 5, 6 and 7.
After one corrects the first error, compilation is required again to see whether additional errors are present.
The latter errors are present since the calculated circles from Figure 4 will overlap, that is, a lack of distinct intersection points invalidates the centroid calculation.
It is clear that the traditional ℓ 1-LS method for CS fails at estimating the sparse signal when gross errors are present in the compressed measurements.
The positioning performance when sensor orientation errors are present is evaluated using 25 Monte Carlo simulations for each orientation error using different trajectories.
Consequently even when sensing errors are present, the learning process can lead to capture average patterns and thus appropriately mitigate their impact.
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The errors are presented in Table 1.
Working conditions and absolute errors are presented in Table 4.
The RMS errors are presented in Table 3.
For transformed data, back-transformed predicted means and approximate standard errors are presented.
The absolute values of the errors are presented in Table 2 and Figure 2.
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