| 1. |
- Andersson, Maria, et al.
(författare)
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Fusion of Acoustic and Optical Sensor Data for Automatic Fight Detection in Urban Environments
- 2010
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Ingår i: Information Fusion (FUSION), 2010 13th Conference on. - : IEEE conference proceedings. - 9780982443811 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- We propose a two-stage method for detection of abnormal behaviours, such as aggression and fights in urban environment, which is applicable to operator support in surveillance applications. The proposed method is based on fusion of evidence from audio and optical sensors. In the first stage, a number of modalityspecific detectors perform recognition of low-level events. Their outputs act as input to the second stage, which performs fusion and disambiguation of the firststage detections. Experimental evaluation on scenes from the outdoor part of the PROMETHEUS database demonstrated the practical viability of the proposed approach. We report a fight detection rate of 81% when both audio and optical information are used. Reduced performance is observed when evidence from audio data is excluded from the fusion process. Finally, in the case when only evidence from one camera is used for detecting the fights, the recognition performance is poor.
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| 2. |
- Bishop, A.N., et al.
(författare)
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Global Robot Localization with Random Finite Set Statistics
- 2010
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Ingår i: Fusion 2010. - : IEEE. - 9780982443811 ; , s. 5711873-
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Konferensbidrag (refereegranskat)abstract
- We re-examine the problem of global localization of a robot using a rigorous Bayesian framework based on the idea of random finite sets. Random sets allow us to naturally develop a complete model of the underlying problem accounting for the statistics of missed detections and of spurious/erroneously detected (potentially unmodeled) features along with the statistical models of robot hypothesis disappearance and appearance. In addition, no explicit data association is required which alleviates one of the more difficult sub-problems. Following the derivation of the Bayesian solution, we outline its first-order statistical moment approximation, the so called probability hypothesis density filter. We present a statistical estimation algorithm for the number of potential robot hypotheses consistent with the accumulated evidence and we show how such an estimate can be used to aid in re-localization of kidnapped robots. We discuss the advantages of the random set approach and examine a number of illustrative simulations.
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| 3. |
- Brax, Christoffer, et al.
(författare)
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Evaluating Precise and Imprecise State-Based Anomaly Detectors for Maritime Surveillance
- 2010
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Ingår i: Proceedings of the 13th International Conference on Information Fusion. - : IEEE. - 9780982443811 ; , s. Article number 5711997-
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Konferensbidrag (refereegranskat)abstract
- We extend the State-Based Anomaly Detection approach by introducing precise and imprecise anomaly detectors using the Bayesian and credal combination operators, where evidences over time are combined into a joint evidence. We use imprecision in order to represent the sensitivity of the classification regarding an object being normal or anomalous. We evaluate the detectors on a real-world maritime dataset containing recorded AIS data and show that the anomaly detectors outperform previously proposed detectors based on Gaussian mixture models and kernel density estimators. We also show that our introduced anomaly detectors perform slightly better than the State-Based Anomaly Detection approach with a sliding window.
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| 4. |
- Callmer, Jonas, 1981-, et al.
(författare)
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Probabilistic Stand Still Detection using Foot Mounted IMU
- 2010
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Ingår i: Proceedings of the 13th International Conference on Information Fusion. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- We consider stand still detection for indoor localization based on observations from a foot-mounted inertial measurement unit (IMU). The main contribution is a statistical framework for stand-still detection, which is a fundamental step in zero velocity update (ZUPT) to reduce the drift from cubic to linear in time. First, the observations are transformed to a test statistic having non-central chi-square distribution during zero velocity. Second, a hidden Markov model is used to describe the mode switching between stand still, walking, running, crawling and other possible movements. The resulting algorithm computes the probability of being in each mode, and it is easily extendable to a dynamic navigation framework where map information can be included. Results of first mode probability estimation, second map matching without ZUPT and third step length estimation with ZUPT are provided.
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| 5. |
- Erlandsson, Tina, et al.
(författare)
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Information fusion supporting team situation awareness for future fighting aircraft
- 2010
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Ingår i: Proceedings of the 13th Conference on Information Fusion (FUSION), 2010. - New York : IEEE conference proceedings. - 9780982443811 ; , s. 1-8
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In the military aviation domain, the decision maker, i.e. the pilot, often has to process huge amounts of information in order to make correct decisions. This is further aggravated by factors such as time-pressure, high workload and the presence of uncertain information. A support system that aids the pilot to achieve his/her goals has long been considered vital for performance progress in military aviation. Research programs within the domain have studied such support systems, though focus has not been on team collaboration. Based on identified challenges of assessing team situation awareness we suggest an approach to future military aviation support systems based on information fusion. In contrast to most previous work in this area, focus is on supporting team situation awareness, including team threat evaluation. To deal with these challenges, we propose the development of a situational adapting system, which presents information and recommendations based on the current situation.
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| 6. |
- Granström, Karl, 1981-, et al.
(författare)
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A Gaussian Mixture PHD Filter for Extended Target Tracking
- 2010
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Ingår i: Proceedings of the 13th International Conference on Information Fusion. - Linköping : Linköping University Electronic Press. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for tracking of multiple extended targets. A general modification of the PHD filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture PHD filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target PHD filter is shown in a comparison with a standard PHD filter.
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| 7. |
- Gustafsson, Fredrik, et al.
(författare)
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Particle Filtering with Dependent Noise
- 2010
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Ingår i: Proceedings of the 13th Conference on Information Fusion. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- The theory and applications of the particle filter (PF) have developed tremendously during the past two decades. However, there appear to be no version of the PF readily applicable to the case of dependent process and measurement noise. This is in contrast to the Kalman filter, where the case of correlated noise is a standard modification. Further, the fact that sampling continuous time models give dependent noise processes is an often neglected fact in literature. We derive the optimal proposal distribution in the PF for general and Gaussian noise processes, respectively. The main result is a modified prediction step. It is demonstrated that the original Bootstrap particle filter gets a particular simple and explicit form for dependent Gaussian noise. Finally, the practical importance of dependent noise is motivated in terms of sampling of continuous time models.
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| 8. |
- Högberg, Johanna, 1978-, et al.
(författare)
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Weighted unranked tree automata as a framework for plan recognition
- 2010
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Ingår i: 2010 13th International Conference on Information Fusion. - : IEEE. - 9780982443811 - 9780982443811
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- As the amount of information accessible to military intelligence continues to surge, operator assisted surveillance becomes less tractable. To process the information stream efficiently, automatic systems for threat detection are called for. These systems must be sufficiently robust to process incomplete or noisy data, and capable of dealing with uncertainties and probabilities. For safety reasons and accountability, it is imperative that the surveillance systems are specified in a formal framework that allows for rigorous mathematical verification. To this end, we demonstrate how the unobstructed keyhole plan recognition problem can be modelled within the framework of weighted unranked tree automata, and outline a software system for recognition of hostile behavior.
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| 9. |
- Johansson, Fredrik, et al.
(författare)
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Real-time Allocation of Defensive Resources to Rockets, Artillery, and Mortars
- 2010
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Ingår i: FUSION 2010. - : IEEE conference proceedings. - 9780982443811 ; , s. Article number 5712026-
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Konferensbidrag (refereegranskat)abstract
- The protection of defended assets such as military bases and population centers against ballistic weapons (e.g. rockets and mortars) is a highly relevant problem in the military conflicts of today and tomorrow. In order to neutralize threats of this kind, they have to be detected and engaged before causing any damage to the defended assets. We propose algorithms for solving the resource allocation problem in real-time, and empirically investigate their performance using the open source testbed SWARD. The results show that a particle swarm optimization algorithm produce high quality solution for small-scale problems, and that agenetic algorithm yields the best solutions for the largest tested problem instances.
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| 10. |
- Johansson, Fredrik, et al.
(författare)
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SWARD : System for Weapon Allocation Research & Development
- 2010
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Ingår i: FUSION 2010. - : IEEE conference proceedings. - 9780982443811 ; , s. Article number 5712067-
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Konferensbidrag (refereegranskat)abstract
- The allocation of firing units to hostile targets is an important process within the air defense domain. Many algorithms have been proposed for solving various weapon allocation problems, but evaluation of the performance of such algorithms is problematic, since it does not exist any standard scenarios on which to test the algorithms. It is to a large extent unknown how weapon allocation algorithms compare to each other when it comes to solution quality. We have developed the testbed SWARD, making it possible to systematically compare algorithm performance, and to support the development of new weapon allocation algorithms.
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| 11. |
- Johansson, Ronnie, et al.
(författare)
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Information Acquisition Strategies for Bayesian Network-based Decision Support
- 2010
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Ingår i: FUSION 2010. - : IEEE conference proceedings. - 9780982443811 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Determining how to utilize information acquisition resources optimally is a difficult task in the intelligence domain. Nevertheless, an intelligence analyst can expect little or no support for this from software tools today. In this paper, we describe a proof of concept implementation of a resource allocation mechanism for an intelligence analysis support system. The system uses a Bayesian network to structure intelligence requests, and the goal is to minimize the uncertainty of a variable of interest. A number of allocation strategies are discussed and evaluated through simulations.
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| 12. |
- Karlsson, Alexander, et al.
(författare)
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An Empirical Comparison of Bayesian and Credal Combination Operators
- 2010
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Ingår i: FUSION 2010. - : IEEE conference proceedings. - 9780982443811 ; , s. Article number 5711907-
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Konferensbidrag (refereegranskat)abstract
- We are interested in whether or not representing and maintaining imprecision is beneficial when combining evidences from multiple sources. We perform two experiments that contain different levels of risk and where we measure the performance of the Bayesian and credal combination operators by using a simple score function that measures the informativeness of a reported decision set. We show that the Bayesian combination operator performed on centroids of operand credal sets outperforms the credal combination operator when no risk is involved in the decision problem. We also show that if a risk component is present in the decision problem, a simple cautious decision policy for the Bayesian combination operator can be constructed that outperforms the corresponding credal decision policy.
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| 13. |
- Lundquist, Christian, 1978-, et al.
(författare)
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Estimating Polynomial Structures from Radar Data
- 2010
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Ingår i: Proceedings of the 13th International Conference on Information Fusion. - Edinburgh, Scotland : Linköping University Electronic Press. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- Situation awareness for vehicular safety and autonomy functions includes knowledge of the drivable area. This area is normally constrained between stationary road-side objects as guard-rails, curbs, ditches and vegetation. We consider these as extended objects modeled by polynomials along the road, and propose an algorithm to track each polynomial based on noisy range and bearing detections, typically from a radar. A straightforward Kalman filter formulation of the problem suffers from the errors-in-variables (EIV) problem in that the noise enters the system model. We propose an EIV modification of the Kalman filter and demonstrates its usefulness using radar data from public roads.
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| 14. |
- Orguner, Umut, 1977-, et al.
(författare)
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Multi Target Tracking with Acoustic Power Measurements using Emitted Power Density
- 2010
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Ingår i: Proceedings of the 13th International Conference on Information Fusion. - Linköping : Linköping University Electronic Press. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method to achieve multi target tracking using acoustic power measurements obtained from an acoustic sensor network. We first present a novel concept called emitted power density (EPD) which is an aggregate information state that holds the emitted power distribution of all targets in the scene over the target state space. It is possible to find prediction and measurement update formulas for an EPD which is conceptually similar to a probability hypothesis density (PHD). We propose a Gaussian process based representation for making the related EPD updates using Kalman filter formulas. These updates constitute a recursive EPD-filter which is based on the discretization of the position component of the target state space. The results are illustrated on a real data scenario where experiments are done with two targets constrained to a road segment.
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| 15. |
- 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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| 16. |
- Savic, Vladimir, et al.
(författare)
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Pseudo-junction tree method for cooperative localization in wireless sensor networks
- 2010
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Ingår i: IEEE Proc. of Intl. Conf. on Information Fusion (FUSION). - 9780982443811 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Nonparametric belief propagation (NBP) is well-known probabilistic method for cooperative localization in sensor networks. However, due to the double counting problem, NBP convergence is not guaranteed in the networks with loops or even if NBP converges, it could provide us less accurate estimates. The well-known solution for this problem is nonparametric generalized belief propagation based on junction tree (NGBP-JT). However, there are two problems: how to efficiently form the junction tree in an arbitrary network, and how to decrease the number of particles while keeping the good performance. Therefore, in this paper, we propose the formation of pseudo-junction tree (PJT), which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of particles, we use a set of very strong constraints. The resulting localization method, NGBP based on PJT (NGBP-PJT), overperforms NBP in terms of accuracy and communication cost in any arbitrary network.
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| 17. |
- Sjanic, Zoran, 1975-, et al.
(författare)
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Simultaneous Navigation and SAR Auto-Focusing
- 2010
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Ingår i: Proceedings of 13th International Conference on Information Fusion. - Linköping : Linköping University Electronic Press. - 9780982443811
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Synthetic Aperture Radar (SAR) equipment is an all-weather radar imaging system that can create high resolution images by means of utilising the movement of the flying platform. Accurate knowledge of the flown trajectory is essential in order to get focused images. Recently SAR systems are becoming more used on smaller and cheaper flying platforms like Unmanned Aerial Vehicles (UAV). Since UAVs in general have navigation systems with poorer performance than manned aircraft, the resulting images will inevitably be unfocused. At the same time, the unfocused images carry the information about the platforms trajectory that can be utilised. Here a way of using SAR images and their focus measure in a sensor fusion framework in order to simultaneously obtain both improved images and trajectory estimate is presented. The method is illustrated on a simple simulated example with promising results. Finally a discussion about the results and future work is given.
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| 18. |
- Svensson, Daniel, 1979, et al.
(författare)
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Joint probabilistic data association filter for partially unresolved target groups
- 2010
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Ingår i: Proceedings of the 13th International Conference on Information Fusion. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- In many surveillance problems the observedobjects are so closely spaced that they cannot always be resolvedby the sensor(s). Typical examples for partially unresolvedmeasurements are the surveillance of aircraft in formation,and convoy tracking for ground surveillance. Ignoringthe limited sensor resolution in a tracking systemmay lead to degraded tracking performance, in particularunwanted track-losses. In this paper, we further discuss arecently presented extension of the resolution model by Kochand van Keuk to the case of arbitrary object numbers, andit is shown how that model can be incorporated into theJoint Probabilistic Data Association Filter (JPDAF). Further,through simulations of a ground target tracking scenario,it is shown how the incorporation of the resolutionmodel improves tracking performance when targets are partiallyunresolved.
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| 19. |
- Wahlström, Niklas, 1984-, et al.
(författare)
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Magnetometers for Tracking Metallic Targets
- 2010
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Ingår i: Proceedings of 13th International Conference on Information Fusion. - 9780982443811
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Konferensbidrag (refereegranskat)abstract
- Starting from Maxwell's equations, we derive a sensor model for three-axis magnetometers suitable for localization and tracking applications. The model depends on the relative position between the sensor and the target, and a physical magnetic multipole model of the target. Both point targets (far-field) and extended target (near-field) models are provided. The models are validated on data taken from various road vehicles. The suitability of magnetometers for tracking is analyzed in terms of local observability and Cramér Rao lower bound as a function of the sensor positions in a two sensor scenario. Results from field test data indicate excellent tracking of position and velocity of the target, as well as identification of the magnetic target model suitable for target classification.
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