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This paper presents a numerical approach to the pedestrian map-matching problem using building plans. The proposed solution is based on a sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time pedestrian navigation systems using low-cost MEMS gyroscopes and accelerometers as dead-reckoning sensors. The algorithm reliability and accuracy...
A new sequential Monte Carlo procedure for approximating the probability hypothesis density is proposed. The algorithm, based on the replacement of numerical approximation with exact computation, is applicable to the class of conditionally linear/Gaussian models. The proposed algorithm is applied with an efficient, measurement-directed importance density to multiple target tracking using range-bearings...
In this paper, we present a novel multiple-model probability hypothesis density (MMPHD) filter for multiple maneuvering targets tracking. In the proposed MMPHD filter, the multiple models are composed of two models, namely a constant velocity (CV) model and a ??current?? statistical (CS) model, and the PHD is approximated by a set of weighted random samples propagated over time using sequential Monte...
In this study, a statistical estimation algorithm is developed to solve the SLAM (simultaneous localization and map building) problem, by using a robot equipped with only simple and cheap sensors. During map building and simultaneous localization, the robot can sense its environment with infrared sensors and can decide the path to follow by using the developed SLAM algorithm. The most frequent problems...
The Stiefel manifold comprises sets of orthonormal vectors in Euclidean space, and as such arises in a variety of contemporary statistical signal processing contexts. Here we consider the problem of estimating the state of a hidden Markov process evolving on this manifold, given noisy observations in the embedding Euclidean space. We describe an approach using sequential Monte Carlo methods, and provide...
Snake has found a number of applications in recent years in computer vision. A snake is an elastic curve, which dynamically adjusts its initial position to the object shape. Snake is sensitive to parameters values and initialization, and moreover, it is a popular method for object contour localization while not suitable for state estimation in time series. This paper presents a method to extend snake...
This paper presents a method for real time object tracking. The method tracks 3D objects in image sequences and jointly estimates their 3D pose and motion parameters. The solution relies on a state modeling of this estimation problem. We develop a resolution method based on a sequential Monte Carlo method and more particularly on a hybrid particle filter. This approach combines the benefits of the...
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