Entanglement of spatially separated quantum states is usually defined with respect to a reference frame provided by some external observer. Thus, if one wishes localize the information within entangled state, must enact an entanglement extraction protocol also that frame. for Gaussian ground in such construction has been shown require minimum energy and hence interesting process gravitational p...

The focus of study is to review the FIM statistical behavior in each EKF update and determine its potential in providing sufficient information about Robotic Localization and Mapping problem with intermittent measurements. We provide theoretical analysis and prove that the FIM can successfully describe both upper and lower bounds for the state covariance matrix whenever measurement data is not ...

The focus of study is to discuss a statistical behavior of FIM in each EKF update and determine its potential in providing sufficient information about Robotic Localization and Mapping problem with intermittent measurements. We provide a theoretical analysis result and prove that the FIM can successfully describe both upper and lower bounds for the state covariance matrix whenever measurement d...

This paper proposed a solution to the Finite Escape Time problem in H∞ Filter based Simultaneous Localization and Mapping problem. Finite escape time has been one of the obstacle that holding the realization of H∞ Filter in many application. For this reason, a method of decorrelating some of the updated state covariance of the filter is suggested to avoid the finite escape time from occurred du...

Abstract In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation. Our combines information brought by model realizations, that based on our prior knowledge about dynamical system interest. We perform combination both sources optimal factors. The method exploits rank-deficiency matrices to provide analysis step ...

In this paper, the Weiss-Weinstein bound is analyzed in the context of sources localization with a planar array of sensors. Both conditional and unconditional source signal models are studied. First, some results are given in the multiple sources context without specifying the structure of the steering matrix and of the noise covariance matrix. Moreover, the case of an uniform or Gaussian prior...

Over ten years ago, Lu and Milios presented a probabilistic scan matching algorithm for solving the simultaneous localization and mapping (SLAM) problem with 2D laser range scans, a standard in robotics. This paper presents an extension to this GraphSLAM method. Our iterative algorithm uses a sparse network to represent the relations between several overlapping 3D scans, computes in every step ...

This paper is devoted to the localization of magnetoacoustic sources moving in a straight line at a constant speed. Our technique is based on the association of narrow band acoustic signals and magnetostatic measurements. First of all, we describe features that make possible the association of magnetic and acoustic data, secondly, we show that positioning accuracy is much improved by this assoc...

This paper introduces an accurate and high speed pose tracking method for mobile robots based on matching of extracted features from consecutive scans. The feature extraction algorithm proposed in this paper uses a global information of the whole scan data and local information around feature points. Uncertainty of each feature is represented using covariance matrices determined due to observat...

In recent years, wireless sensor networks have drawn great attention. This type of network is composed of a large number of sensor nodes which are able to sense, process and communicate. Besides, they are used in various fields such as emergency relief in disasters, monitoring the environment, military affairs and etc. Sensor nodes collect environmental data by using their sensors and send them...