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210
Adapting the Sample Size in Particle Filters Through KLDSampling
 International Journal of Robotics Research
, 2003
"... Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process. ..."
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Cited by 144 (8 self)
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Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process.
Accuracy characterization for metropolitanscale wifi localization
 In Proceedings of Mobisys 2005
, 2005
"... Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, locationaware web search) become interesting only when th ..."
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Cited by 139 (6 self)
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Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, locationaware web search) become interesting only when the underlying location system is available ubiquitously and is not limited to a single office environment. Unfortunately, the installation and calibration overhead involved for most of the existing research systems is too prohibitive to imagine deploying them across, say, an entire city. In this work, we evaluate the feasibility of building a widearea 802.11 WiFibased positioning system. We compare a suite of wirelessradiobased positioning algorithms to understand how they can be adapted for such ubiquitous deployment with minimal calibration. In particular, we study the impact of this limited calibration on the accuracy of the positioning algorithms. Our experiments show that we can estimate a user’s position with a median positioning error of 13–40 meters (depending upon the characteristics of the environment). Although this accuracy is lower than existing positioning systems, it requires substantially lower calibration overhead and provides easy deployment and coverage across large metropolitan areas. 1
Marginalized particle filters for mixed linear/nonlinear statespace models
 IEEE Transactions on Signal Processing
, 2005
"... Abstract—The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and nonGaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with th ..."
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Cited by 112 (33 self)
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Abstract—The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and nonGaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear statespace model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete highdimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported. Index Terms—Kalman filter, marginalization, navigation systems, nonlinear systems, particle filter, state estimation. I.
Current mapmatching algorithms for transport applications: Stateofthe art and future research directions
, 2007
"... ..."
Distributed compressionestimation using wireless sensor networks  The design goals of performance, bandwidth efficiency, scalability, and robustness
 IEEE SIGNAL PROCESSING MAG
, 2006
"... A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advance ..."
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Cited by 78 (1 self)
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A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advanced signal processing tasks with unprecedented robustness and versatility, thus making WSN an attractive lowcost technology for a wide range of remote sensing and environmental monitoring applications [1], [32].
Bartendr: A Practical Approach to Energyaware Cellular Data Scheduling
"... Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate ..."
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Cited by 58 (4 self)
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Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring nonurgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energyefficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost. In this paper, we make several contributions towards a practical system for energyaware cellular data scheduling called Bartendr. First, we establish, via measurements, the relationship between signal strength and power consumption. Second, we show that location alone is not sufficient to predict signal strength and motivate the use of tracks to enable effective prediction. Finally, we develop energyaware scheduling algorithms for different workloads—syncing and streaming—and evaluate these via simulation driven by traces obtained during actual drives, demonstrating energy savings of up to 60%. Our experiments have been performed on four cellular networks across two large metropolitan areas, one in India and the other in the U.S.
SOIKF: Distributed Kalman Filtering With LowCost Communications Using the Sign of Innovations
 IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2006
"... When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations—a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic proc ..."
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Cited by 57 (13 self)
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When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations—a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic processes, whereby the low communication cost is effected by requiring the transmission of a single bit per observation. Following a Kalman filtering (KF) approach, we develop recursive algorithms for distributed state estimation based on the sign of innovations (SOI). Even though SOIKF can afford minimal communication overhead, we prove that in terms of performance and complexity it comes very close to the clairvoyant KF which is based on the analogamplitude observations. Reinforcing our conclusions, we show that the SOIKF applied to distributed target tracking based on distanceonly observations yields accurate estimates at low communication cost.
Mapbased multiple model tracking of a moving object
 Proceedings of eight RoboCup International Symposium
, 2004
"... Abstract. In this paper we propose an approach for tracking a moving target using RaoBlackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete ..."
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Cited by 54 (3 self)
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Abstract. In this paper we propose an approach for tracking a moving target using RaoBlackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete states represent the nonlinear parts of the state estimation problem. In the context of target tracking, these are the nonlinear motion of the observing platform and the different motion models for the target. Using this representation, we show how to reason about physical interactions between the observing platform and the tracked object, as well as between the tracked object and the environment. The approach is implemented on a fourlegged AIBO robot and tested in the context of ball tracking in the RoboCup domain. 1
Mobile Sensor Network Control using Mutual Information Methods and Particle Filters
"... This paper develops a set of methods enabling an informationtheoretic distributed control architecture to facilitate search by a mobile sensor network. Given a particular configuration of sensors, this technique exploits the structure of the probability distributions of the target state and of the ..."
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Cited by 54 (2 self)
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This paper develops a set of methods enabling an informationtheoretic distributed control architecture to facilitate search by a mobile sensor network. Given a particular configuration of sensors, this technique exploits the structure of the probability distributions of the target state and of the sensor measurements to control the mobile sensors such that future observations minimize the expected future uncertainty of the target state. The mutual information between the sensors and the target state is computed using a particle filter representation of the posterior probability distribution, making it possible to directly use nonlinear and nonGaussian target state and sensor models. To make the approach scalable to increasing network sizes, singlenode and pairwisenode approximations to the mutual information are derived, with analytically bounded error. The pairwisenode approximation is proven to be a more accurate objective function than the singlenode approximation. The mobile sensors are cooperatively controlled using a distributed optimization, yielding coordinated motion of the network. The consequences of using these methods are explored for various sensing modalities, including bearingsonly sensing, rangeonly sensing, and magnetic field sensing, all with potential for search and rescue applications. For each sensing modality, the behavior of this nonparametric method is compared and contrasted with the results of linearized methods, and simulations are performed of a target search using the dynamics of actual vehicles. Monte Carlo results demonstrate that as network size increases, the sensors
Indoor Location Sensing Using GeoMagnetism
 In ACM MobiSys
, 2011
"... We present an indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building. The presence of these large steel members warps the geomagnetic field in a way that is spatially varying but temporally stable. To l ..."
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Cited by 45 (0 self)
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We present an indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building. The presence of these large steel members warps the geomagnetic field in a way that is spatially varying but temporally stable. To localize, we measure the magnetic field using an array of ecompasses and compare the measurement with a previously obtained magnetic map. We demonstrate accuracy within 1 meter 88 % of the time in experiments in two buildings and across multiple floors within the buildings. We discuss several constraint techniques that can maintain accuracy as the sample space increases. Categories and Subject Description