Particle Filtering for Multisensor Data Fusion With Switching Observation Models: Application to Land Vehicle Positioning

Caron, François; Davy, Manuel; Duflos, Emmanuel; Vanheeghe, Philippe

doi:10.1109/tsp.2007.893914

Cited by 140 publications

(102 citation statements)

References 38 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…Until 1993, Gordon overcomes the problem of degradation of the algorithm, and puts forward the concept of resampling, then the first operation of the Carlo Monte filter appears, that is called the resampling particle filter algorithm [7][8][9][10][11][12]. As for particle filter algorithm, when the number of particles approaches infinity, the calculation accuracy of the particle filter algorithm is the highest, but the computation is also increased.…”

Section: Particle Filter Algorithmmentioning

confidence: 99%

Research on the method of information system risk state estimation based on clustering particle filter

Cui¹,

Hong²,

Jiang³

et al. 2017

Open Physics

View full text Add to dashboard Cite

With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.

show abstract

Section: Particle Filter Algorithmmentioning

confidence: 99%

Research on the method of information system risk state estimation based on clustering particle filter

Cui¹,

Hong²,

Jiang³

et al. 2017

Open Physics

View full text Add to dashboard Cite

show abstract

“…Whenever the new measurement arrives, the particle filter will start the distribution derivation and resampling. Such an example is available in [5], where a land vehicle positioning application is examined. This approach inherits the disadvantages of a particle filter, for example, the problems about dimensionality and variance.…”

Section: A Switching Schemementioning

confidence: 99%

“…Based on the numerical values of the weights shown in Fig. 2, we can see that (5) and (7) are kind of exponential moving average filter, which produces artificial measurements at a certain frequency up to the user.…”

Section: Proposed Solutionmentioning

confidence: 99%

Filling the gap between low frequency measurements with their estimates

Wang

Kostic

Jansen³

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

Abstract-The use of redundant sensors brings a rich diversity of information, nevertheless fusing different sensors that run at vastly different frequencies into a proper estimate is still a challenging sensor fusion problem. Instead of using the size-varying measurements and thereby the size-varying filters during each sampling period, we propose to find a substitute of the unavailable low frequency measurements such that we can avoid using different sampling frequencies in one filter. In the gap between the sampling of two low frequency measurements, the use of these substitutes produces smoother estimates. In both the proof of concept simulation and the localization experiment performed on an indoor soccer robot, our proposed approach exhibits an improved performance compared to the size-varying Kalman filter methods.

show abstract

“…As a consequence, the estimation algorithms, which are based on the exact knowledge of the model parameters, can be no longer accurate in this context. Thus the joint state and parameter estimation (i.e., state estimation in the presence of model uncertainty) for linear/nonlinear dynamical systems is a challenging problem in many practical areas, such as target tracking [2], satellite positioning [3] and communication systems [4].…”

Section: Introductionmentioning

confidence: 99%

A kernel density-based particle filter for state and time-varying parameter estimation in nonlinear state-space models

Cheng

Tourneret

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-In linear/nonlinear dynamical systems, there are many situations where model parameters cannot be obtained a priori or vary with time. As a consequence, the estimation algorithms that are based on the exact knowledge of these model parameters cannot be accurate in this context. In this work, a kernel density-based particle filter is investigated to jointly estimate the states and unknown time-varying parameters of a dynamical system described by nonlinear state and measurement equations. The approach combines an auxiliary particle filter with the kernel smoothing method so as to obtain a stationary kernel density for the unknown parameters. The performance of the proposed approach is investigated for positioning using measurements from a global navigation satellite system that are possibly contaminated by multipath interferences.

show abstract

Particle Filtering for Multisensor Data Fusion With Switching Observation Models: Application to Land Vehicle Positioning

Cited by 140 publications

References 38 publications

Research on the method of information system risk state estimation based on clustering particle filter

Research on the method of information system risk state estimation based on clustering particle filter

Filling the gap between low frequency measurements with their estimates

A kernel density-based particle filter for state and time-varying parameter estimation in nonlinear state-space models

Contact Info

Product

Resources

About