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Kalman Filters are utilised for filtering and estimation in a large set of application. Here, this methodology is utilised for trajectory estimation of an underwater robot. In this work, three Kalman Filter methods are proposed for trajectory estimation. There are: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Central Difference Kalman Filter (CDKF). Simulation results are presented...
This work presents a study to determine the improvement of the simultaneous position estimations of several Mobile Robots (MRs) adding the relative distances between them, regarding independent position estimations for each MR. To evaluate the performance of the proposal, it is supposed that the localization scenario is composed of an Ultrasonic Local Positioning System (ULPS) and two MRs with odometry...
Modeling and filtering of a mobile object stochastic trajectory on the basis of fractal Wiener process taking into account the Hurst indicator are offered. For numerical realization of this processes the wavelet based decomposition is used. The peculiarities of trajectory parameters estimation by using Kalman filter and the wavelet algorithm are investigated. The illustrating examples are given.
Data fusion for parameter estimation with multi-structure and unequal-precision is considered in this paper. Matrix tools e.g., congruent transformation, trace function and matrix differential, are used to analyze the estimation performance. Theoretical results reveal that: the single equipment estimate, the optimal fusion estimate, and the joint estimate are some special cases of the fusion estimate...
In this paper, in order to estimate the operational parameters of synchronous generator model suitable for unbalanced conditions, an approach which uses Trajectory Sensitivity Functions (TSF) and is based on Unscented Kalman Filter (UKF) is developed. In this framework, iniatilly, TSF are used to identify the parameters which most affect the behavior of the system. After the ordination of the parameters...
In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results.
Discrete-time estimation and control techniques play a crucial role in digital control architectures. These methods rely on accurate approximations of continuous-time system behavior. For mechanical systems, this includes not only the system state, but also mechanical properties such as symplecticity or the long-term energy behavior. Additionally, we aim to preserve the Hamiltonian structure of optimally...
This paper presents an approach intended for tracking of biological non-stationary signals. The proposed approach utilizes a Kalman filter autoregressive model together with a method for estimation of covariance matrices of the uncorrelated process noise and measurement noise. The method was tested in simulations, where the ability of tracking of a class of time varying autoregressive processes was...
The first method that was developed to deal with the SLAM problem is based on the extended Kalman filter, EKF SLAM. However this approach cannot be applied to a large environments because of the quadratic complexity and data association problem. The second approach to address the SLAM problem is based on the Rao-Blackwellized Particle filter FastSLAM, which follows a large number of hypotheses that...
This paper considers the problem of planning a trajectory for a sensing robot to best estimate a time-changing scalar field in its environment. We model the field as a linear combination of basis functions with time-varying weights. The robot uses a Kalman-like filter to maintain an estimate of the field, and to compute the error covariance of the estimate. The goal is to find a trajectory for the...
In many localization systems, the Mobile Node (MN) takes distance measurements with reference nodes called Anchors (ANs) in order to estimate its position. In general, the MN can obtain a better estimation when it takes measurements with multiple ANs. Unfortunately, this can lead to consume more energy and generate more traffic in the network. In this paper, we present an adaptive mechanism for the...
This paper studies the infinite-horizon sensor scheduling problem for linear Gaussian processes with linear measurement functions. Several important properties of the optimal infinite-horizon schedules are derived. In particular, it is proved that under some mild conditions, both the optimal infinite-horizon average-per-stage cost and the corresponding optimal sensor schedules are independent of the...
In previous work by the authors a novel estimator was introduced, with asymptotic stability guarantees, for the problems of source localization and navigation based on single range measurements. The aim of this paper is to further study these problems in terms of the performance of the proposed estimators and the trajectories that yield best results. To that purpose, the achievable performance with...
This paper studies the infinite-horizon sensor scheduling problem for linear Gaussian processes with linear measurement functions. Several important properties of the optimal infinite-horizon schedules are derived. In particular, it is proved that under some mild conditions, both the optimal infinite-horizon average-per-stage cost and the corresponding optimal sensor schedules are independent of the...
The paper deals with the question of robot precision and how to characterise repeatability. Hence ISO and ANSI repeatability indexes advantages and drawbacks are analysed. A new intrinsic repeatability index is proposed that can estimate the robot endpoint position variability satisfying the non-bias and convergence conditions. Computation of this index is performed using simulated straight and drifting...
In this paper, we propose a robust novel approach with closed-form estimator for object tracking based on a non-linear measurement model over time from a single sensor with arbitrary noise degradation. Relying on the widely-used dynamic motion model for arbitrary moving targets, tracking of moving objects can be formulated using received signal strength (RSS) measurements. We provide a closed-form...
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