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Visual and inertial multi-rate data fusion for motion estimation via Pareto-optimization Visual and inertial multi-rate data fusion for motion estimation via Pareto-optimization

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Date added: 02/11/2015
Date modified: 02/11/2015
Filesize: 286 Bytes
Downloads: 996

Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical model-based techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

Efficient recalibration via Dynamic Matrix Completion Efficient recalibration via Dynamic Matrix Completion

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Date added: 02/21/2014
Date modified: 02/21/2014
Filesize: 286 Bytes
Downloads: 1338

Fingerprint-based localization techniques have witnessed significant progress as they provide highly accurate location estimation with minimal hardware interventions. However, the required calibration phase is time and labour consuming. In this work, we propose a reduced effort recalibration technique for fingerprint-based indoor positioning systems. Particularly, we reduce the number of received fingerprints by performing spatial sub-sampling. The recovery of the full map from partial measurements is formulated as an instance of a Dynamic Matrix Completion problem where we exploit the spatio-temporal correlations among the fingerprints. Analytical studies and simulations are provided to evaluate the performance of the proposed technique in terms of reconstruction and location error.

WLAN-based Indoor Path Tracking Using Compressive RSS Measurements WLAN-based Indoor Path Tracking Using Compressive RSS Measurements

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Date added: 02/21/2014
Date modified: 02/21/2014
Filesize: 382.89 kB
Downloads: 803

In this paper, a hybrid path-tracking system is introduced, which exploits the power of compressive sensing (CS) to recover accurately sparse signals, in conjunction with the efficiency of a Kalman filter to update the states of a dynamical system. The proposed method first employs a hierarchical region-based approach to constrain the area of interest, by modeling the signal-strength values received from a set of wireless access points using the statistics of multivariate Gaussian models. Then, based on the inherent spatial sparsity of indoor localization, CS is applied as a refinement of the estimated position by recovering an appropriate sparse position-indicator vector. The experimental evaluation with real data reveals that the proposed approach achieves increased localization accuracy when compared with previous methods, while maintaining a low computational complexity, thus, satisfying the constraints of mobile devices with limited resources.

Localization in Wireless Networks via Laser Scanning and Bayesian Compressed Sensing Localization in Wireless Networks via Laser Scanning and Bayesian Compressed Sensing

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Date added: 02/21/2014
Date modified: 02/21/2014
Filesize: 286 Bytes
Downloads: 1211

WiFi indoor localization has seen a renaissance with the introduction of RSSI-based approaches. However, manual fingerprinting techniques that split the indoor environment into predefined grids are implicitly bounding the maximum achievable localization accuracy. WoLF, our proposed Wireless localization and Laser-scanner assisted Fingerprinting system, solves this problem by automating the way indoor fingerprint maps are generated. We furthermore show that WiFi localization on the generated high resolution maps can be performed by sparse reconstruction which exploits the peculiarities imposed by the physical characteristics of indoor environments. Particularly, we propose a Bayesian Compressed Sensing (BCS) approach in order to find the position of the mobile user and dynamically determine the sufficient number of APs required for accurate positioning. BCS employs a Bayesian formalism in order to reconstruct a sparse signal using an undetermined system of equations. Experimental results with data collected in a university building validate WoLF in terms of localization accuracy under actual environmental conditions.

Communication Infrastructures in Industrial Automation: The case of 60 GHz MillimeterWave Communicat Communication Infrastructures in Industrial Automation: The case of 60 GHz MillimeterWave Communicat

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 286 Bytes
Downloads: 1255

Wireless sensor networks for industrial automation applications must offer timely, reliable, and energy efficient communications at both low and high data rate. While traditional communication technologies between 2.4 GHz and 5 GHz are sometimes incapable to efficiently achieve the aforementioned goals, new communication strategies are emerging, such as millimeterWave communications. In this overview paper, the general requirements that factory and process automation impose on the network design are reviewed. Moreover, this paper presents and qualitatively evaluates the 60 GHz millimeterWave communication technology for automation. It is argued that the upcoming 60 GHz millimeterWave technology brings an enormous potential and can influence the design of the future communication infrastructures in factory and process automation.

Adaptive Quantization for Multihop Progressive Estimation in Wireless Sensor Networks Adaptive Quantization for Multihop Progressive Estimation in Wireless Sensor Networks

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 833.22 kB
Downloads: 1089

We consider the problem of parameter estimation in a wireless sensor network, where because of the bandwidth and power constraints, each sensor transmits quantized information to its parent on a multihop path. Our approach jointly optimizes: i) sensor selection, ii) routing structure, and iii) number of bits per sample for each sensor. First, we express our problem as an optimization problem, and then we design an algorithm that is based on an adaptive quantization and an estimate-and-forward scheme that allows performing sequentially this joint optimization in an efficient way. We show that our algorithm provides a routing structure that trades-off the aforementioned three metrics better than the traditional shortest path tree routing structure, which is commonly used in practice. Numerical results depicting the performance and advantages of our approach over previous state-of-the-art approaches are presented.

 

Quasi-Nash Equilibria for Non-Convex Distributed Power Allocation Games in Cognitive Radios Quasi-Nash Equilibria for Non-Convex Distributed Power Allocation Games in Cognitive Radios

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 286 Bytes
Downloads: 1246

In this paper, we consider a sensing-based spectrum sharing scenario in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The resulting optimization problem for each cognitive user is non-convex, thus leading to a non-convex game, which presents a new challenge when analyzing the equilibria of this game where each cognitive user represents a player. In order to deal with the non-convexity of the game, we use a new relaxed equilibria concept, namely, quasi-Nash equilibrium (QNE). A QNE is a solution of a variational inequality obtained under the first-order optimality conditions of the player's problems, while retaining the convex constraints in the variational inequality problem. In this work, we state the sufficient conditions for the existence of the QNE for the proposed game. Specifically, under the so-called linear independent constraint qualification, we prove that the achieved QNE coincides with the NE. Moreover, a distributed primal-dual interior point optimization algorithm that converges to a QNE of the proposed game is provided in the paper, which is shown from the simulations to yield a considerable performance improvement with respect to an alternating direction optimization algorithm and a deterministic game.

Efficient OOK/DS-CDMA Detection Threshold Selection Efficient OOK/DS-CDMA Detection Threshold Selection

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 286 Bytes
Downloads: 1312

A major constraint in deployments of wireless sensor networks (WSNs) is the energy consumption related to the battery lifetime of the network nodes. To this end, power efficient digital modulation techniques such as On-Off keying (OOK) are highly attractive. However, the OOK detection thresholds, namely the thresholds against which the received signals are compared to detect which bit is transmitted, must be carefully selected to minimize the bit error rate. This is challenging to accomplish in resource-limited nodes with constrained computational capabilities. In this paper, the system scenario considers simultaneously transmitting nodes in Rayleigh fading conditions. Various iterative algorithms to numerically select the detection thresholds are established. Convergence analysis accompanies these algorithms. Numerical simulations are provided to support the derived results and to compare the proposed algorithms. It is concluded that OOK modulation is beneficial in resource constrained WSNs provided that efficient optimization algorithms are employed for the threshold selection.

Distributed Fault Detection using Sensor Networks and Pareto Estimation Distributed Fault Detection using Sensor Networks and Pareto Estimation

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 286 Bytes
Downloads: 1217

In this paper, a preliminary novel distributed fault detection architecture for dynamic systems using sensor networks and a distributed estimation method based on Pareto optimization is proposed. The goal is to monitor large-scale or distributed systems by using a sensor network where each node acts as a local estimation agent without centralized coordination. Probabilistic detection thresholds related to a given rate of false alarms are derived in several different scenarios as far as the measurement pattern and the nominal dynamics is concerned. Preliminary simulation results show the effectiveness of the proposed fault detection methodology.

Non-convex Power Allocation Game in MIMO Cognitive Radio Networks Non-convex Power Allocation Game in MIMO Cognitive Radio Networks

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 286 Bytes
Downloads: 1227

We consider a sensing-based spectrum sharing scenario in a MIMO cognitive radio network where the overall objective is to maximize the total throughput of each cognitive radio user by jointly optimizing both the detection operation and the power allocation over all the channels, under a interference constraint bound to primary users. The resulting optimization problems lead to a non-convex game, which presents a new challenge when analyzing the equilibria of this game. In order to deal with the non-convexity of the game, we use a new relaxed equilibria concept, namely, quasi-Nash equilibrium (QNE). We show the sufficient conditions for the existence and the uniqueness of a QNE. A primal-dual interior point optimization method that converges to a QNE is also discussed in this paper. Simulation results show that the proposed game can achieve a considerable performance improvement with respect to a deterministic game.