&lt;title&gt;Model-set sequence-conditioned estimation for variable-structure MM estimation&lt;/title&gt;

Li, X. Rong

doi:10.1117/12.324650

Cited by 5 publications

(2 citation statements)

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“…Thus, we use the extended Kalman filter (EKF) approach and approximate the nonlinear transformation with the appropriate linear approximation g,. (x(k)) z(k)=g(x(k))+v(k)= g9(x(k)) +v(k) (8) g (x(k))…”

Section: Linear and Nonlinear Kalman Filtering Reviewmentioning

confidence: 99%

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<title>Improved state estimation through use of roads in ground tracking</title>

et al. 2000

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The ability to provide an accurate view of a region of interest is standard among existing tracking systems. The extension of this capability to include accurate projections of the targets to future times increases the complexity of the problem. However, this ability to predict future locations is an important problem due to the inherent time latency that is present from "sensor to shooter." Because of this, a major requirement of the tracking system is that it must be able to use available information to accurately predict future target locations. The focus of this paper is to describe an improved state estimation technique that incorporates road information by using a Variable Structure 1MM. Furthermore, this approach will identify and account for stopped or stationary moving targets. Finally, some preliminary performance results will be presented.

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Section: Linear and Nonlinear Kalman Filtering Reviewmentioning

confidence: 99%

“…With a VS-IMM, the number of types of models that are carried by the 1MM changes adaptively over time. This technique is developed and described in [8,9,10]. …”

Section: Interacting Multiple Models Estimationmentioning

confidence: 99%

<title>Improved state estimation through use of roads in ground tracking</title>

et al. 2000

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Multiple model estimation by hybrid grid

2010

Proceedings of the 2010 American Control Conference

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This paper considers the problem of state estimation for a hybrid system with Markovian switching parameters which belong to a continuous space. A hybrid grid multiple model (HGMM) estimator is presented. The total model set for HGMM is the combination of a fixed coarse grid and an adaptive fine grid. Three practical algorithms in this scheme are developed. These algorithms are used for state estimation in maneuvering target tracking. Simulation results demonstrate that the HGMM estimator outperforms the corresponding fixed structure multiple model (FSMM) at a negligible extra computational cost.

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Survey of typical Variable Structure Multi-models estimation algorithms

Liu

Huo-ping

2010

2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering

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<title>Model-set sequence-conditioned estimation for variable-structure MM estimation</title>

Cited by 5 publications

References 0 publications

<title>Improved state estimation through use of roads in ground tracking</title>

<title>Improved state estimation through use of roads in ground tracking</title>

Multiple model estimation by hybrid grid

Survey of typical Variable Structure Multi-models estimation algorithms

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