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A poor choice of importance density can have detrimental effect on the efficiency of a particle filter. While a specific choice of proposal distribution might be close to optimal for certain models, it might fail miserably for other models, possibly even leading to infinite variance. In this paper we show how mixture sampling techniques can be used to derive robust and efficient particle filters,...
In this paper we propose to use regularized Monte Carlo-Joint probabilistic data association filter (RMC-JPDAF) to the classical problem of multiple target tracking in a cluttered area. We have used the Monte Carlo methods in order to the fact that they have the ability to model any state-space with nonlinear and non- Gaussian models for target dynamics and measurement likelihood. To encounter with...
This paper shows how the random sampling, M-estimators, random walk can be combined to create a consistent sampler for generic models in problems that are difficult due to outliers and multimodality of the solution. Our method contains three major steps: (1) finding the local peaks of the selected robust cost function (M-estimator) using seeds from random sampling of minimal configurations, (2) constructing...
This work addresses range-only SLAM (RO-SLAM) as the Bayesian inference problem of sequentially tracking a vehicle while estimating the location of a set of beacons without any prior information. The only assumptions are the availability of odometry and a range sensor able of identifying the different beacons. We propose exploiting the conditional independence between the position distributions of...
Accurate visual object tracking through long sequences is a challenging task since object's appearance changes and complex motion happens. We present mixture motion model and incorporate observation model within the Monte Carlo framework to achieve robust visual tracking. The mixture motion model which employs important history motion information of the target is built according to a motion measurement...
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