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90
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.
Gaussian sum particle filtering
 Signal Processing 51
, 2003
"... Abstract—In this paper, we use the Gaussian particle filter introduced in a companion paper to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters ..."
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Cited by 70 (3 self)
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Abstract—In this paper, we use the Gaussian particle filter introduced in a companion paper to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state space (DSS) models with nonGaussian noise. With nonGaussian noise approximated by Gaussian mixtures, the nonGaussian noise models are approximated by banks of Gaussian noise models, and Gaussian mixture filters are developed using algorithms developed for Gaussian noise DSS models. 1 As a result, problems involving heavytailed densities can be conveniently addressed. Simulations are presented to exhibit the application of the framework developed herein, and the performance of the algorithms is examined. Index Terms—Dynamic statespace models, extended Kalman
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.
Particle filter theory and practice with positioning applications
 IEEE Aerospace and Electronic Systems Magazine
, 2010
"... N.B.: When citing this work, cite the original article. ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to ..."
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Cited by 44 (11 self)
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N.B.: When citing this work, cite the original article. ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted
State estimation for aggressive flight in GPSdenied environments using onboard sensing
 in Proc. of the 2012 IEEE Int. Conf. on Robotics and Automation
, 2012
"... Abstract — In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in realtime on a fixedwing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in ..."
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Cited by 20 (1 self)
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Abstract — In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in realtime on a fixedwing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudolinear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multistep forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixedwing vehicle flying in a challenging indoor environment. I.
Segmenting and Tracking the Left Ventricle by Learning the Dynamics
 in Cardiac Images,” MIT LIDS
, 2005
"... Abstract. Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain heart conditions. Existing LV segmentation techniques are founded mostly upon algorithms for segmenting static ..."
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Cited by 20 (1 self)
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Abstract. Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain heart conditions. Existing LV segmentation techniques are founded mostly upon algorithms for segmenting static images. In order to exploit the dynamic structure of the heart in a principled manner, we approach the problem of LV segmentation as a recursive estimation problem. In our framework, LV boundaries constitute the dynamic system state to be estimated, and a sequence of observed cardiac images constitute the data. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past segmentations. This requires a dynamical system model of the LV, which we propose to learn from training data through an informationtheoretic approach. To incorporate the learned dynamic model into our segmentation framework and obtain predictions, we use ideas from particle filtering. Our framework uses a curve evolution method to combine such predictions with the observed images to estimate the LV boundaries at each time. We demonstrate the effectiveness of the proposed approach on a large set of cardiac images. We observe that our approach provides more accurate segmentations than those from static image segmentation techniques, especially when the observed data are of limited quality. 1
Multiple human tracking with wireless distributed pyroelectric sensors
 Proc. SPIE
, 2008
"... Abstract—This paper presents a wireless pyroelectric sensor system, composed of sensing modules (slaves), a synchronization and error rejection module (master), and a data fusion module (host), to perform human tracking. The computation workload distribution among slave, master, and host is investig ..."
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Cited by 18 (4 self)
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Abstract—This paper presents a wireless pyroelectric sensor system, composed of sensing modules (slaves), a synchronization and error rejection module (master), and a data fusion module (host), to perform human tracking. The computation workload distribution among slave, master, and host is investigated. The performances and costs of different signalprocessing and target– tracking algorithms are discussed. A prototype system is described containing pyroelectric sensor modules that are able to detect the angular displacement of a moving thermal target. Fresnel lens arrays are used to modulate the sensor field of view. The sensor system has been used to track a single human target. Index Terms—Fresnel lens, human motion tracking, pyroelectric sensor, wireless sensor network. I.
Comparison of sequential data assimilation methods for the Kuramoto–Sivashinsky equation
, 2009
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A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving New Algorithms
 in American Control Conference
, 2011
"... Abstract — We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This ..."
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Cited by 13 (8 self)
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Abstract — We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived straightforwardly by providing methods for computing these moments. Based on this insight, we derive the cubature Kalman smoother and propose a novel robust filtering and smoothing algorithm based on Gibbs sampling. I.
Kalman Filtering in Wireless Sensor Networks REDUCING COMMUNICATION COST IN STATEESTIMATION PROBLEMS
"... Awireless sensor network (WSN) is a collection of physically distributed sensing devices that can communicate through a shared wireless channel. Sensors can be deployed, for example, to detect the presence of a contaminant in a water reservoir, to estimate the temperature in an orange grove, or to t ..."
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Cited by 13 (1 self)
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Awireless sensor network (WSN) is a collection of physically distributed sensing devices that can communicate through a shared wireless channel. Sensors can be deployed, for example, to detect the presence of a contaminant in a water reservoir, to estimate the temperature in an orange grove, or to track the position of a moving target. The promise of WSNs stems from the benefits of distributed sensing and control. For example, in the targettracking setup depicted in Figure 1, where sensors measure their distance to a target whose trajectory is to be estimated, the benefit of distributed sensing is the availability of observations with high signaltonoise ratio (SNR). Whether collected by a passive radar, which estimates distances by the strength of an electromagnetic signature emitted by the