| 1. |
- Ardeshiri, Tohid, 1980-, et al.
(författare)
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An adaptive PHD filter for tracking with unknown sensor characteristics
- 2013
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Konferensbidrag (refereegranskat)abstract
- In multi-target tracking, the discrepancy between the nominal and the true values of the model parameters might result in poor performance. In this paper, an adaptive Probability Hypothesis Density (PHD) filter is proposed which accounts for sensor parameter uncertainty. Variational Bayes technique is used for approximate inference which provides analytic expressions for the PHD recursions analogous to the Gaussian mixture implementation of the PHD filter. The proposed method is evaluated in a multi-target tracking scenario. The improvement in the performance is shown in simulations.
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| 2. |
- Ardeshiri, Tohid, et al.
(författare)
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Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
- 2015
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Ingår i: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:12, s. 2450-2454
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Tidskriftsartikel (refereegranskat)abstract
- We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.
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| 3. |
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| 4. |
- Ardeshiri, Tohid, et al.
(författare)
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Greedy Reduction Algorithms for Mixtures of Exponential Family
- 2015
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Ingår i: IEEE Signal Processing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1070-9908 .- 1558-2361. ; 22:6, s. 676-680
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.
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| 5. |
- Ardeshiri, Tohid, 1980-, et al.
(författare)
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On Reduction of Mixtures of the Exponential Family Distributions
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Many estimation problems require a mixture reduction algorithm with which an increasing number of mixture components are reduced to a tractable level. In this technical report a discussion on dierent aspects of mixture reduction is given followed by a presentation of numerical simulation on reduction of mixture densities where the component density belongs to the exponential family of distributions.
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| 6. |
- Ardeshiri, Tohid, et al.
(författare)
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Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this technical report, some derivations for the smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes smoother for linear state-space models with unknownprocess and measurement noise covariances in [1] are presented. Further, the variational iterations are compared with iterations of the Expectation Maximization (EM) algorithm for smoothing linear state-space models with unknown noise covariances.[1] T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, ApproximateBayesian smoothing with unknown process and measurement noise covariances, submitted to Signal Processing Letters, 2015.
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| 7. |
- Braga, André R., et al.
(författare)
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Cooperative Terrain Based Navigation and Coverage Identification Using Consensus
- 2015
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Ingår i: 18th International Conference on Information Fusion (Fusion), 2015. - : IEEE. - 9780982443866 ; , s. 1190-1197
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Konferensbidrag (refereegranskat)abstract
- This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approximate the global sufficient statistics throughout the network. The evaluation of the proposed algorithm is carried out on a Terrain Based Navigation problem where the unknown parameters of the observation noise model contain relevant information about the terrain properties.
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| 8. |
- Fritsche, Carsten, et al.
(författare)
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A fresh look at Bayesian Cramér-Rao bounds for discrete-time nonlinear filtering
- 2014
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Ingår i: 17th International Conference on Information Fusion, FUSION 2014; Salamanca; Spain; 7 July 2014 through 10 July 2014. - : Institute of Electrical and Electronics Engineers Inc.. - 9788490123553 ; , s. Art. no. 6916255-
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Konferensbidrag (refereegranskat)abstract
- In this paper, we aim to relate different Bayesian Cramér-Rao bounds which appear in the discrete-time nonlinear filtering literature in a single framework. A comparative theoretical analysis of the bounds is provided in order to relate their tightness. The results can be used to provide a lower bound on the mean square error in nonlinear filtering. The findings are illustrated and verified by numerical experiments where the tightness of the bounds are compared.
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| 9. |
- Fritsche, Carsten, 1978-, et al.
(författare)
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Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering
- 2018
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Ingår i: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018. - : IEEE. - 9781538646595 - 9781538646588 ; , s. 4289-4293
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Konferensbidrag (refereegranskat)abstract
- In this paper, marginal versions of the Bayesian Bhattacharyya lower bound (BBLB), which is a tighter alternative to the classical Bayesian Cramer-Rao bound, for discrete-time filtering are proposed. Expressions for the second and third-order marginal BBLBs are obtained and it is shown how these can be approximately calculated using particle filtering. A simulation example shows that the proposed bounds predict the achievable performance of the filtering algorithms better.
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| 10. |
- Fritsche, Carsten, et al.
(författare)
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Marginal Weiss-Weinstein bounds for discrete-time filtering
- 2015
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Ingår i: 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). - : IEEE. - 9781467369978 ; , s. 3487-3491
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Konferensbidrag (refereegranskat)abstract
- A marginal version of the Weiss-Weinstein bound (WWB) is proposed for discrete-time nonlinear filtering. The proposed bound is calculated analytically for linear Gaussian systems and approximately for nonlinear systems using a particle filtering scheme. Via simulation studies, it is shown that the marginal bounds are tighter than their joint counterparts.
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| 11. |
- Fritsche, Carsten, et al.
(författare)
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On the Cramér-Rao lower bound under model mismatch
- 2015
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Ingår i: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467369978 ; , s. 3986-3990
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Konferensbidrag (refereegranskat)abstract
- Cramér-Rao lower bounds (CRLBs) are proposed for deterministic parameter estimation under model mismatch conditions where the assumed data model used in the design of the estimators differs from the true data model. The proposed CRLBs are defined for the family of estimators that may have a specified bias (gradient) with respect to the assumed model. The resulting CRLBs are calculated for a linear Gaussian measurement model and compared to the performance of the maximum likelihood estimator for the corresponding estimation problem.
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| 12. |
- Fritsche, Carsten, et al.
(författare)
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Online EM algorithm for jump Markov systems
- 2012
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Ingår i: 15th International Conference on Information Fusion (FUSION), 2012. - 9781467304177 - 9780982443842 ; , s. 1941-1946
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Konferensbidrag (refereegranskat)abstract
- The Expectation-Maximization (EM) algorithm in combination with particle filters is a powerful tool that can solve very complex problems, such as parameter estimation in general nonlinear non-Gaussian state space models. We here apply the recently proposed online EM algorithm to parameter estimation in jump Markov models, that contain both continuous and discrete states. In particular, we focus on estimating process and measurement noise distributions being modeled as mixtures of members from the exponential family.
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| 13. |
- Kasebzadeh, Parinaz, 1985-, et al.
(författare)
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Joint Antenna and Propagation Model Parameter Estimation using RSS measurements
- 2015
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Konferensbidrag (refereegranskat)abstract
- In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods.
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| 14. |
- Lundquist, Christian, 1978-, et al.
(författare)
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Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Measurements of individual wheel speeds and absolute position from a global navigation satellite system (GNSS) are used for high-precision estimation of vehicle tire radii in this work. The radii deviation from its nominal value is modeled as a Gaussian process and included as noise components in a vehicle model. The novelty lies in a Bayesian approach to estimate online both the state vector of the vehicle model and noise parameters using a marginalized particle filter. No model approximations are needed such as in previously proposed algorithms based on the extended Kalman filter. The proposed approach outperforms common methods used for joint state and parameter estimation when compared with respect to accuracy and computational time. Field tests show that the absolute radius can be estimated with millimeter accuracy, while the relative wheel radius on one axleis estimated with submillimeter accuracy.
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| 15. |
- Lundquist, Christian, et al.
(författare)
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Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Measurements of individual wheel speeds and absolute position from a global navigation satellite system (gnss) are used for high-precision estimation of vehicle tire radii in this work. The radii deviation from its nominal value is modeled as a Gaussian process and included as noise components in a vehicle model. The novelty lies in a Bayesian approach to estimate online both the state vector of the vehicle model and noise parameters using a marginalized particle filter. No model approximations are needed such as in previously proposed algorithms based on the extended Kalman filter. The proposed approach outperforms common methods used for joint state and parameter estimation when compared with respect to accuracy and computational time. Field tests show that the absolute radius can be estimated with millimeter accuracy, while the relative wheel radius on one axle is estimated with submillimeter accuracy.
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| 16. |
- Lundquist, Christian, et al.
(författare)
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Tire Radii Estimation Using a Marginalized Particle Filter
- 2014
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 15:2, s. 663-672
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, the measurements of individual wheel speeds and the absolute position from a global positioning system are used for high-precision estimation of vehicle tire radii. The radii deviation from its nominal value is modeled as a Gaussian random variable and included as noise components in a simple vehicle motion model. The novelty lies in a Bayesian approach to estimate online both the state vector and the parameters representing the process noise statistics using a marginalized particle filter (MPF). Field tests show that the absolute radius can be estimated with submillimeter accuracy. The approach is tested in accordance with regulation 64 of the United Nations Economic Commission for Europe on a large data set (22 tests, using two vehicles and 12 different tire sets), where tire deflations are successfully detected, with high robustness, i.e., no false alarms. The proposed MPF approach outperforms common Kalman-filter-based methods used for joint state and parameter estimation when compared with respect to accuracy and robustness.
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| 17. |
- Saha, Saikat, et al.
(författare)
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Marginalized Particle Filters for Bayesian Estimation of Gaussian Noise Parameters
- 2010
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Ingår i: Proceedings of the 13th Conference on Information Fusion. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily accuracy. However, the curse of dimensionality prevents its application in cases where the state dimensionality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach for the case of unknown noise distribution parameters that avoid both aforementioned problem. First, the standard approach of augmenting the state vector with sensor offsets and scale factors is avoided, so the state dimension is not increased. Second, the mean and covariance of both process and measurement noises are represented with parametric distributions, whose statistics are updated adaptively and analytically using the concept of conjugate prior distributions. The resulting marginalized particle filter is applied to and illustrated with a standard example from literature.
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| 18. |
- Wahlström, Niklas, et al.
(författare)
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Extended Target Tracking Using Gaussian Processes
- 2015
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 63:16, s. 4165-4178
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used for gating and association. Furthermore, we use an efficient recursive implementation of the algorithm by deriving a state space model in which the Gaussian process regression problem is cast into a state estimation problem.
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| 19. |
- Zhao, Yuxin, 1986-, et al.
(författare)
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Particle Filtering for Positioning Based on Proximity Reports
- 2015
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Konferensbidrag (refereegranskat)abstract
- The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider positioning of devices based on time series proximity reports from a mobile device to a network node. This corresponds to nonlinear measurements with respect to the device position in relation to the network nodes. Therefore, particle filtering is applicable for positioning. Positioning performance is evaluated in a typical office area with Bluetooth-low-energy beacons deployed for proximity detection and report. Accuracy is concluded to vary spatially over the office floor, and in relation to the beacon deployment density.
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| 20. |
- Özkan, Emre, 1980-, et al.
(författare)
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A Bayesian Approach to Jointly Estimate Tire Radii and Vehicle Trajectory
- 2011
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Ingår i: Proceedings of the International IEEE Conference on Intelligent Transportation Systems. - Washington DC, USA : IEEE conference proceedings. - 9781457721984 ; , s. 1-6
-
Konferensbidrag (refereegranskat)abstract
- High-precision estimation of vehicle tire radii is considered, based on measurements on individual wheel speeds and absolute position from a global navigation satellite system (GNSS). The wheel speed measurements are subject to noise with time-varying covariance that depends mainly on the road surface. The novelty lies in a Bayesian approach to estimate online the time-varying radii and noise parameters using a marginalized particle filter, where no model approximations are needed such as in previously proposed algorithms based on the extended Kalman filter. Field tests show that the absolute radius can be estimated with millimeter accuracy, while the relative wheel radius on one axle is estimated with submillimeter accuracy.
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| 21. |
- Özkan, Emre, et al.
(författare)
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Marginalized Adaptive Particle Filtering for Nonlinear Models with Unknown Time-Varying Noise Parameters
- 2013
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 49:6, s. 1566-1575
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of the noise distribution is typically crucial for the state estimation of general state-space models. However, properties of the noise process are often unknown in the majority of practical applications. The distribution of the noise may also be non-stationary or state dependent and that prevents the use of off-line tuning methods. For linear Gaussian models, Adaptive Kalman filters (AKF) estimate unknown parameters in the noise distributions jointly with the state. For nonlinear models, we provide a Bayesian solution for the estimation of the noise distributions in the exponential family, leading to a marginalized adaptive particle filter (MAPF) where the noise parameters are updated using finite dimensional sufficient statistics for each particle. The time evolution model for the noise parameters is defined implicitly as a Kullback-Leibler norm constraint on the time variability, leading to an exponential forgetting mechanism operating on the sufficient statistics. Many existing methods are based on the standard approach of augmenting the state with the unknown variables and attempting to solve the resulting filtering problem. The MAPF is significantly more computationally efficient than a comparable particle filter that runs on the full augmented state. Further, the MAPF can handle sensor and actuator offsets as unknown means in the noise distributions, avoiding the standard approach of augmenting the state with such offsets. We illustrate the MAPF on first a standard example, and then on a tire radius estimation problem on real data.
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| 22. |
- Özkan, Emre, et al.
(författare)
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Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering
- 2011
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Ingår i: Acoustics, Speech and Signal Processing (ICASSP), 2011. - : IEEE. - 9781457705380 - 9781457705373 ; , s. 5924-5927
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Konferensbidrag (refereegranskat)abstract
- In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle filters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of components. The joint estimation of the state and the noise density is done via particle filters. Furthermore, the number of components and the noise statistics are allowed to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced.
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| 23. |
- Özkan, Emre, et al.
(författare)
-
Online EM algorithm for joint state and mixture measurement noise estimation
- 2012
-
Ingår i: 15th International Conference on Information Fusion (FUSION), 2012. - : IEEE. - 9781467304177 - 9780982443842 ; , s. 1935-1940
-
Konferensbidrag (refereegranskat)abstract
- In this study, we aim to estimate the unknown multi-modal measurement noise distribution of nonlinear state space models. The unknown noise distribution is modeled as a mixture of exponential family of distributions. We use the Expectation-Maximization (EM) method in order to jointly estimate the unknown parameters as well as the states. The online version of the EM algorithm is implemented by using particle filtering techniques. The resulting algorithm is a noise adaptive particle filter which is applicable to many sensor models having multi-modal noise distributions with unknown parameters.
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| 24. |
- Özkan, Emre, et al.
(författare)
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Rao-Blackwellised Particle Filter for Star-ConvexExtended Target Tracking Models
- 2016
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Ingår i: 2016 19th International Conference on Information Fusion. - 9780996452748 ; , s. 1193-1199
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Konferensbidrag (refereegranskat)abstract
- In this paper, we explore the potential gains in using Sequential Monte Carlo (SMC) methods for extended target tracking (ETT) models based on Gaussian processes (GP). The existing random hypersurface based ETT models use Extended/Unscented Kalman filter for inference, which may lead to poor performance under high uncertainty. Particle filters (PFs) are known to provide robust performance in the cases where the non-linear Kalman filtering solutions fail. We design a Rao-Blackwellised particle filter (RBPF) where we exploit the conditional linear Gaussian structure of the GP parameters. We illustrate the gain in the performance with simulations.
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| 25. |
- Özkan, Emre, et al.
(författare)
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Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems
- 2015
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 63:3, s. 754-765
-
Tidskriftsartikel (refereegranskat)abstract
- We present an online method for joint state and parameter estimation in jump Markov non-linear systems (JMNLS). State inference is enabled via the use of particle filters which makes the method applicable to a wide range of non-linear models. To exploit the inherent structure of JMNLS, we design a Rao-Blackwellized particle filter (RBPF) where the discrete mode is marginalized out analytically. This results in an efficient implementation of the algorithm and reduces the estimation error variance. The proposed RBPF is then used to compute, recursively in time, smoothed estimates of complete data sufficient statistics. Together with the online expectation maximization algorithm, this enables recursive identification of unknown model parameters including the transition probability matrix. The method is also applicable to online identification of jump Markov linear systems(JMLS). The performance of the method is illustrated in simulations and on a localization problem in wireless networks using real data.
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