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In the FLs GMM-ITS algorithm, the concept of fixed lag smoothing is extended for a more general case of smoothing for any size of lag N, and it utilizes the benefits of multi-scan data association.
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Also, as shown can be seen in Table 1, for both models the estimates of βinf increase with the size of the lag and peak at 3 weeks lag, indicating that there is a delay which must be taken into account.
The fixed lag smoothing GMM-ITS (FLs GMM-ITS) algorithm for any size of smoothing lag N is proposed.
It considers only two scans in the smoothing interval and does not provide any generalization to smooth the target state at any size of fixed lag.
On the one hand, the KF needs a lot of storage for all measurements within a time window and extra time for reprocessing depending on the size of the lag of an OoS measurement.
Fig. 3 Effect of flow rate, glucose concentration and beads size on lag phase.
Notably, it appears that the validity of causal perspective depends on the size of the time lag.
We can measure the FWHM of the Gaussians as an approximation to the size of the time-lag kernels.
It is worthwhile noting that most of the effect sizes of individual lags within the two hour time frame (lag0-lag4) are smaller than the cumulative effects.
Both smoothing algorithms consider the lag size of 2. The confirmed true track rate for a single target in Fig. 3 presents a comparison between FLs GMM-ITS and other algorithms simulated in this work for single-target tracking.
K function can be used to describe how the expected value of a point process changes over different spatial and/or temporal range of sizes (lag).
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