| 1. |
- 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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| 2. |
- 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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| 3. |
- Lundquist, Christian, et al.
(författare)
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Estimation of the Free Space in Front of a Moving Vehicle
- 2009
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- There are more and more systems emerging making use of measurements from a forward looking radar and a forward looking camera. It is by now well known how to exploit this data in order to compute estimates of the road geometry, tracking leading vehicles, etc. However, there is valuable information present in the radar concerning stationary targets, that is typically not used. The present work shows how radar measurements of stationary targets can be used to provide a reliable estimate of the drivable space in front of a moving vehicle.In the present paper three conceptually different methods to estimate stationary objects or road borders are presented and compared. The first method considered is occupancy grid mapping, which discretizes the map surrounding the ego vehicle and the probability of occupancy is estimated for each grid cell. The second method applies a constrained quadratic program in order to estimate the road borders. The problem is stated as a constrained curve fitting problem. The third method associates the radar measurements to extended stationary objects and tracks them as extended targets.The required sensors, besides the standard proprioceptive sensors of a modern car, are a forward looking long range radar and a forward looking camera. Hence, there is no need to introduce any new sensors, it is just a matter of making better use of the sensor information that is already present in a modern car. The approach has been evaluated and tested on real data from highways and rural roads in Sweden and the results are very promising.
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| 4. |
- Orguner, Umut, et al.
(författare)
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Improved Target Tracking with Road Network Information
- 2009
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Ingår i: Proceedings of the '09 IEEE Aerospace Conference. - Linköping : Linköping University Electronic Press. - 9781424426225 - 9781424426218 ; , s. 1-11
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we consider the problem of tracking targets, which can move both on-road and off-road, with particle filters utilizing the road-network information. It is argued that the constraints like speed-limits and/or one-way roads generally incorporated into on-road motion models make it necessary to consider additional high-bandwidth off-road motion models. This is true even if the targets under consideration are only allowed to move on-road due to the possibility of imperfect road-map information and drivers violating the traffic rules. The particle filters currently used struggles during sharp mode transitions, with poor estimation quality as a result. This is due to the fact the number of particles allocated to each motion mode is varying according to the mode probabilities. A recently proposed interacting multiple model (IMM) particle filtering algorithm, which keeps the number of particles in each mode constant irrespective of the mode probabilities, is applied to this problem and its performance is compared to a previously existing algorithm. The results of the simulations on a challenging bearing-only tracking scenario show that the proposed algorithm, unlike the previously existing algorithm, can achieve good performance even under the sharpest mode transitions.
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| 5. |
- Orguner, Umut
(författare)
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Notes on Differential Entropy Calculation Using Particles
- 2008
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This report outlines a method to calculate the differential entropy of a probability density represented by a number of particles and weights. When only the particles and the weights are given, entropy calculation is cumbersome and requires continuous approximation of the density by using some kernel functions. However, in a particle ltering framework, Bayes rule provides a direct sample based approximation to the problem.
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| 6. |
- Orguner, Umut
(författare)
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Notes on Differential Entropy of Mixtures
- 2008
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This report proves that the differential entropy of particle mixtures is equal to -∞ unlike the wrong claim in the literature that is equal to the discrete entropy of particle weights. It then gives an upper bound for the differential entropy of the Gaussian mixtures which can be used in practical applications.
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| 7. |
- Skoglar, Per, 1977-, et al.
(författare)
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On Information Measures based on Particle Mixture for Optimal Bearings-only Tracking
- 2009
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Ingår i: Proceedings of the 2009 IEEE Aerospace Conference. - Linköping : IEEE conference proceedings. - 9781424426225 - 9781424426218 ; , s. 1-14
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Konferensbidrag (refereegranskat)abstract
- In this work we consider a target tracking scenario where a moving observer with a bearings-only sensor is tracking a target. The tracking performance is highly dependent on the trajectory of the sensor platform, and the problem is to determine how it should maneuver for optimal tracking performance. The problem is considered as a stochastic optimal control problem and two sub-optimal control strategies are presented based on the Information filter and the determinant of the information matrix as the optimization objective. Using the determinant of the information matrix as an objective function in the planning problem is equivalent to using differential entropy of the posterior target density when it is Gaussian. For the non-Gaussian case, an approximation of the differential entropy of a density represented by a particle mixture is proposed. Furthermore, a gradient approximation of the differential entropy is derived and used in a stochastic gradient search algorithm applied to the planning problem.
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| 8. |
- Skoglar, Per, et al.
(författare)
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On Information Measures for Bearings-only Estimation of a Random Walk Target
- 2009
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This report considers the bearings-only estimation problem of a random walk target. The estimation performance for a number of information measures in the Extended Kalman filter framework are investigated, both from a theoretical point of view and by simulation examples.
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