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Subsequently, the problem of using one, fixed disturbance distribution matrix is eliminated by using the interacting multiple models algorithm to select an appropriate unknown input distribution matrix from a predefined set of matrices.
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The authors in [3] present a multiple model algorithm to eliminate the range ambiguity problem in an HPRF radar.
The multiple model algorithm is also used to update the prior information for each bias magnitude sample.
The Gaussian mixture measurement likelihood-integrated track splitting algorithm (GMM-ITS) [4] and the enhanced multiple model algorithm (EMM) (it incorporates the track quality measure in a multiple model algorithm (MM) [3]) are investigated in [5] for single-target tracking in clutter using an HPRF radar; both algorithms are capable of trajectory estimation and false track discrimination.
Every sample time, by using localization method, only a few of models which are closer to the "true" model of the plant are selected to form a multiple model controller, so the big computation burden of the multiple model algorithm is greatly reduced.
Contrarily to other multiple model algorithms, the LMM is more suitable for current driving conditions because the model probabilities can be calculated according to the operating state of the vehicle by using the LSSVM decision model.
This idea leads to the IMM (interacting multiple-model) algorithm, which has good performance and relatively low computational complexity.
In this case, a modified interacting-multiple-model algorithm is proposed to estimate the real-time distribution of hybrid state, and evaluate multicopter's health.
Interacting Multiple Model (IMM) algorithm is proved to be useful in tracking maneuvering targets.
Further, a novel LSSVM Multiple Model (LMM) algorithm is designed to fuse the data obtained from RFID and in-vehicle sensors.
In the first step, the wheel forces' actual dynamic is evaluated by the interacting multiple model (IMM) algorithm, then a suitable model is chosen to match the evaluated dynamic of the wheel force for data denoising in the second step.
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