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In this paper an approach for integration between GPS and inertial navigation systems (INS) is described. The continuous-time navigation and error equations for an earth-centered earth-fixed INS system are presented. Using zero order hold sampling, the set of equations is discretized. An extended Kalman filter for closed loop integration between the GPS and INS is derived. The filter propagates and...
In this paper we propose a novel motion filtering approach that takes into consideration the existence of certain system constraints with respect to the amount of the corrective rotational and translational motions that can be applied on each video frame for stabilization. The interdependence between rotational and translational constraints is considered, and a modified Kalman filtering algorithm...
Decentralized Kalman filters are often used in multi-sensor target tracking as such a distributed fusion architecture has several advantages compared with centralized ones. On the other hand, distributed fusion is not only conceptually more complex but the required bandwidth is also likely to be a lot higher. However, a tradeoff between bandwidth and performance is possible. In this work, the worst...
The difficulties and possibilities connected to indoor positioning suggest using several sources of navigational information. Apart from the signal processing of the individual sources this gives rise to the need for information fusion. This article aims at presenting and describing the signal processing methods and issues faced when constructing a navigation system based on a local ultra wide band...
When a Kalman filter is applied to an image for restoration purposes, the model of the original image affects the accuracy of the restoration. The model for effective restoration depends on the correlation of the original image and the variance of the noise. If these parameters are unknown, they have to be estimated from the observed image. In this paper, a method to estimate the unknown parameters...
This paper1 presents an iterative learning control (ILC) procedure based on an inverse model of the plant under control. Our first contribution is that we formulate the inversion procedure as a Kalman smoothing problem: based on a compact state space model of a possibly non-minimum phase system, identified by the MOESP class of subspace identification methods, we determine the inverse model. The inverse...
In this paper, an optimization-based iterative learning control approach is presented. Given a desired trajectory to be followed, the proposed learning algorithm improves the system performance from trial to trial by exploiting the experience gained from previous repetitions. Taking advantage of the a-priori knowledge about the systems dominating dynamics, a data-based update rule is derived which...
This paper introduces the attitude estimation method of humanoid robot using an extended Kalman filter with a fuzzy logic based tuning algorithm. A humanoid robot which uses inertial sensors such as gyros and accelerometers to calculate its attitude is considered. It is known that the attitude update using gyros are prone to diverge and hence the attitude error needs to be compensated using accelerometers...
Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic...
This paper proposes an alternative approach to deal with the problem of mobile robot navigation, which is called fusion of control signals. The proposed technique has presented good results when the robot has to execute relatively complex tasks, as it is shown in the illustrative example here presented. The technique is developed with basis on the decentralized information filter, whose equations...
In the present article we propose a nonlinear observer that merges the behaviors 1) of an extended Kalman filter, mainly designed to smooth off noise, and 2) of high-gain observers devoted to handle large perturbations in the state estimation. We specifically aim at continuous-discrete systems.
In the paper the role of anti-aliasing filters is revised based on control quality assessment of PID controllers when using average sampling versus instantaneous sampling without any analog filter. Numerical results show that contrary to common belief possible benefits gained from anti-aliasing devices are very restricted.
In this paper, after reviewing the traditional Kalman filter formulation, a development of a fuzzy logic-based adaptive Kalman filter is outlined. The adaptation is in the sense of adaptively tuning, on-line, the measurement noise covariance matrix R or the process noise covariance matrix Q. This improves the Kalman filter performance and prevents filter divergence when R or Q are uncertain. Based...
The paper proposed filtering algorithms for wireless indoor localization based on Extended Kalman filtering (EKF) and Uncented Kalman filtering (UKF). When we use EKF linearization to deal with nonlinear problems, it may cause precision decrease and a series of problems. Thus, the rigorous mathematical analysis simulations and comparative results were carried out in this paper to compare EKF and UKF...
Localization is one of the challenges in achieving reliable communication in Wireless Sensor Networks (WSN). Estimating a sensor's node's position is known as Localization. Nonlinear version of Kalman filtering is known as the Extended Kalman Filter which deals with the case governed by the nonlinear stochastic differential equations, Extended kalman filter is nonlinear filter having their own problem...
This contribution is part of analysis and research in field of unmanned vehicles navigation developed at Faculty of Aeronautics, Technical University in Kosice. The content of this contribution is an issue of the INS and GNSS integration. Loosely coupled integration architecture was in this case chosen as the basic for simple explanation of the integration issue. Within this contribution the emphasis...
In strap down inertial navigation system (SINS), an interferometric fiber optic gyroscope (IFOG) is sensitive device to measure the rotation rate of an object. The IFOG output sustains with noise and random drift errors, which are influenced by the uncertainties of the external environment (like temperature, pressure, vibration) and sensor itself. Random drift is the main error source and it degrades...
The use of wireless sensor networks for indoor localization application has emerged as a significant area of interest over the last decade, primarily motivated by its low cost and convenient deployment. The weighted centroid localization algorithm is a suitable positioning technique in a wireless sensor network due to its easy implementation. However, the performance of this method is easily affected...
For the multisensor linear discrete time-invariant systems with correlated noises, a reduced dimension weighted measurement fusion algorithm is presented. Furthermore, an optimal reduced dimension weighted measurement fusion white noise deconvolution estimator is given. Its computational burden is greatly reduced. A simulation example for a 3-sensor system with Bernoulli-Gaussian input white noise...
In this paper, it focuses on the PVT decoding process of COMPASS satellite navigation system against the operator background of COMPASS satellite navigation system. On the basis of conventional solver methods, Kalman fitering is proposed and analyzed in the solver application of the COMPASS satellite navigation to improve positioning accuracy.
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