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11
Indoor Location Tracking in NonlineofSight Environments Using a
 IEEE 802.15.4a Wireless Network
, 2009
"... Abstract—Indoor location tracking of mobile robots or transport vehicles using wireless technology is attractive for many applications. IEEE 802.15.4a wireless networks offer an inexpensive facility for localizing mobile devices by timebased range measurements. The main problems of timebased range ..."
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Abstract—Indoor location tracking of mobile robots or transport vehicles using wireless technology is attractive for many applications. IEEE 802.15.4a wireless networks offer an inexpensive facility for localizing mobile devices by timebased range measurements. The main problems of timebased range measurements in indoor environments are errors by multipath and nonlineofsight (NLOS) signal propagation. This paper describes indoor tracking using range measurements and an Extended Kalman Filter with NLOS mitigation. The commercially available nanoLOC wireless network is utilized for range measurements. The paper presents experimental results of tracking a forklift truck in an industrial environment. I.
Diffusion mechanisms for fixedpoint distributed kalman smoothing
 In EUSIPCO
, 2008
"... We consider the problem of fixedpoint distributed Kalman smoothing, where a set of nodes are required to estimate the initial condition of a certain process based on their measurements of the evolution of the process. Specifically, we consider linear statespace models where the Kalman smoother g ..."
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We consider the problem of fixedpoint distributed Kalman smoothing, where a set of nodes are required to estimate the initial condition of a certain process based on their measurements of the evolution of the process. Specifically, we consider linear statespace models where the Kalman smoother gives us the MMSE estimate of the initial state of the system. We propose distributed diffusion solutions where nodes communicate with their neighbors and information is propagated through the network via a diffusion process. Hierarchical cooperation schemes are also described. 1.
Approximate Distributed Kalman Filtering for Cooperative Multiagent Localization
"... Target tracking typically refers to the problem of determining the position of a mobile agent based on a stream of noisy measurements. Here, we are interested in the problem of estimating the trajectory of a mobile agent based on noisy measurements collected by a team of autonomous vehicles — a prob ..."
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Target tracking typically refers to the problem of determining the position of a mobile agent based on a stream of noisy measurements. Here, we are interested in the problem of estimating the trajectory of a mobile agent based on noisy measurements collected by a team of autonomous vehicles — a problem that is relevant to applications such as surveillance, disaster relief, and scientific exploration.
WLAN based Pose Estimation for Mobile Robots
 in Proceedings of the 17th IFAC World Congress, Seoul, Korea
, 2008
"... Abstract: Nowadays, many buildings are equipped with a WLAN infrastructure, as an inexpensive communication technology. In this paper a method to estimate position and heading (pose) of a mobile robot using WLAN technology is described. The proposed technique for localizing a mobile robot is based o ..."
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Abstract: Nowadays, many buildings are equipped with a WLAN infrastructure, as an inexpensive communication technology. In this paper a method to estimate position and heading (pose) of a mobile robot using WLAN technology is described. The proposed technique for localizing a mobile robot is based on the use of received signal strength values of WLAN access points in range. A radio map based method and Euclidean distance in combination with Delaunay triangulation and interpolation is proposed. Measured signal strength values of an omnidirectional antenna and a beam antenna are compared with the values of a radio map, in order to estimate the pose of a mobile robot, whereby the directionality of the beam antenna is used to estimate the heading of the robot. The paper presents the experimental results of measurements in an office building. 1.
Distributed boundederror state estimation
"... This paper presents a distributed boundederror state estimation algorithm suited, e.g., to measurement processing by a network of sensors. Contrary to centralized estimation, where all data are collected to a central processing unit, here, each data is processed locally by the sensor, the results a ..."
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This paper presents a distributed boundederror state estimation algorithm suited, e.g., to measurement processing by a network of sensors. Contrary to centralized estimation, where all data are collected to a central processing unit, here, each data is processed locally by the sensor, the results are broadcasted to the network and taken into account by the other sensors. A first analysis of the conditions under which distributed and centralized estimators provide the same results is presented. An application to the tracking of a moving source using a network of sensors measuring the strength of the signal emitted by the source is considered.
International Journal of Computing, SPECAL ISSUE: Intelligent Data Acquisition and Advanced Computing Systems, 7(2):pp.7383 Mobile Robot Localization using WLAN Signal Strengths
"... Abstract—Many buildings are already equipped with a WLAN infrastructure, as an inexpensive communication technology. In this paper two methods that estimate the position and the heading (pose) of a mobile robot using WLAN technology are described. The proposed techniques for localizing a mobile robo ..."
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Abstract—Many buildings are already equipped with a WLAN infrastructure, as an inexpensive communication technology. In this paper two methods that estimate the position and the heading (pose) of a mobile robot using WLAN technology are described. The proposed techniques for localizing a mobile robot are based on the use of received signal strength values of WLAN access points in range. Both use a radio map based method. For interpolation of the radio map weigthed Euclidean distance and Euclidean distance in combination with Delaunay triangulation is proposed. Measured signal strength values of an omnidirectional antenna and a beam antenna are compared with the values of a radio map, in order to estimate the pose of a mobile robot, whereby the directionality of the beam antenna is used to estimate the heading of the robot. The paper presents the experimental results of measurements in an office building. Index Terms—Mobile robots, global localization, pose estimation, WLAN, received signal strength. I.
Distributed Minimax Filter for Tracking and Flocking
"... Abstract — In this paper, we investigate a moving target tracking problem with mobile sensor networks. The moving target is assumed to be an intelligent agent, which is ‘smart’ enough to escape from the detection. We formulate this target estimation problem as a zerosum game in this paper and use a ..."
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Abstract — In this paper, we investigate a moving target tracking problem with mobile sensor networks. The moving target is assumed to be an intelligent agent, which is ‘smart’ enough to escape from the detection. We formulate this target estimation problem as a zerosum game in this paper and use a socalled minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via a consensus filter. Finally, the mobile sensor nodes need to control their motions to move towards the estimated target position and avoid collisions with neighbors. A flocking algorithm is developed for this purpose. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result. I.
Distributed Estimation over Wireless Sensor Networks with Packet Losses
, 2008
"... A distributed adaptive algorithm to estimate a timevarying signal, measured by a wireless sensor network, is designed and analyzed. One of the major features of the algorithm is that no central coordination among the nodes needs to be assumed. The measurements taken by the nodes of the network are ..."
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A distributed adaptive algorithm to estimate a timevarying signal, measured by a wireless sensor network, is designed and analyzed. One of the major features of the algorithm is that no central coordination among the nodes needs to be assumed. The measurements taken by the nodes of the network are affected by noise, and the communication among the nodes is subject to packet losses. Nodes exchange local estimates and measurements with neighboring nodes. Each node of the network locally computes adaptive weights that minimize the estimation error variance. Decentralized conditions on the weights, needed for the convergence of the estimation error throughout the overall network, are presented. A Lipschitz optimization problem is posed to guarantee stability and the minimization of the variance. An efficient strategy to distribute the computation of the optimal solution is investigated. A theoretical performance analysis of the distributed algorithm is carried out both in the presence of perfect and lossy links. Numerical simulations illustrate performance for various network topologies and packet loss probabilities.
DISTRIBUTED OPTIMIZATION IN MULTIAGENT SYSTEMS: APPLICATIONS TO DISTRIBUTED REGRESSION
, 2010
"... The context for this work is cooperative multiagent systems (MAS). An agent is an intelligent entity that can measure some aspect of its environment, process information and possibly influence the environment through its action. A cooperative MAS can be defined as a loosely coupled network of agent ..."
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The context for this work is cooperative multiagent systems (MAS). An agent is an intelligent entity that can measure some aspect of its environment, process information and possibly influence the environment through its action. A cooperative MAS can be defined as a loosely coupled network of agents that interact and cooperate to solve problems that are beyond the individual capabilities or knowledge of each agent. The focus of this thesis is distributed stochastic optimization in multiagent systems. In distributed optimization, the complete optimization problem is not available at a single location but is distributed among different agents. The distributed optimization problem is additionally stochastic when the information available to each agent is with stochastic errors. Communication constraints, lack of global information about the network topology and the absence of coordinating agents make it infeasible to collect all the information at a single location and then treat it as a centralized optimization problem. Thus, the problem has to be solved using algorithms that are distributed, i.e., different