Sentence examples for fixed lag from inspiring English sources

The idea of fixed lag smoothing is proposed in [7].

Simulation results indicate that the performance of fixed lag smoothing GMM-ITS significantly improves false track discrimination and root mean square errors (RMSEs).

The augmented state is also calculated for the EMM algorithm, and then fixed lag smoothing is used to obtain the smoothed target hybrid state at any scan.

The GRand PaRIS algorithm outperforms the existing fixed lag smoother for POD processes of [27], as it does not introduce any intrinsic and non-vanishing bias.

[8] consider the multi-scan data association for tracking a target and applies fixed lag smoothing; however, it does not consider the track quality measure.

The first approach relies on fixed lag smoothing [3] and was further simplified in [4] by allowing pruning and decision feedback techniques.

Again, the GRand PaRIS algorithm outperforms the fixed lag smoother as it shows a similar (vanishing) bias as the fixed lag for the largest lag and a smaller variance than the fixed lags estimates with negligible bias.

The proposed work uses an arbitrary size of the smoothing interval [k,N], where the first scan of each smoothing interval faces fixed-lag smoothing, such that the fixed-lag of the proposed algorithm is N−k.

Using computer simulations, the proposed fixed-lag smoother is shown to be comparable to the fixed-lag Kalman smoother for the nominal system and superior to that for the temporarily uncertain system.

The proposed fixed-lag smoother is shown to be much less computationally complex than the existing fixed-lag Kalman smoother with infinite memory structure.

The current fixed-lag smoothing algorithm has a characteristic, as shown in Theorem 1, that the fixed-lag smoothing estimate of the state vector is calculated in the reverse direction of time.