Institution
Saab AB
Company•Thun, Switzerland•
About: Saab AB is a company organization based out in Thun, Switzerland. It is known for research contribution in the topics: Antenna (radio) & Signal. The organization has 862 authors who have published 928 publications receiving 8807 citations. The organization is also known as: Saab AB & Svenska Aeroplan AB.
Topics: Antenna (radio), Signal, Radar, Radar imaging, Antenna rotator
Papers published on a yearly basis
Papers
More filters
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TL;DR: The derivation of the details for the marginalized particle filter for a general nonlinear state-space model is derived and it is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states.
Abstract: The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian 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 state-space 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 high-dimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported.
649 citations
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TL;DR: In this paper, a 3D finite element model of bolted composite joints was developed to determine non-uniform stress distributions through the thickness of composite laminates in the vicinity of a bolt hole.
223 citations
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TL;DR: In this article, the effects of spanwise distribution on the aircraft aerodynamic efficiency were studied through an inverse twist design approach, combining both a low fidelity panel method and a high-fidelity Reynolds-averaged Navier-Stokes solution method.
210 citations
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06 Jul 2009TL;DR: This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian Mixture Model and the adaptive Kernel Density Estimator, and indicates that KDE more accurately captures finer details of normal data.
Abstract: This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian Mixture Model (GMM) and the adaptive Kernel Density Estimator (KDE). A novel performance measure related to anomaly detection, together with an intermediate performance measure related to normalcy modeling, are proposed and evaluated using recorded AIS data of vessel traffic and simulated anomalous trajectories. The normalcy modeling evaluation indicates that KDE more accurately captures finer details of normal data. Yet, results from anomaly detection show no significant difference between the two techniques and the performance of both is considered suboptimal. Part of the explanation is that the methods are based on a rather artificial division of data into geographical cells. The paper therefore discusses other clustering approaches based on more informed features of data and more background knowledge regarding the structure and natural classes of the data.
168 citations
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TL;DR: This article proposes and investigates the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) and the discords algorithm, a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold.
Abstract: Detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms typically suffer from one or more limitations: They are not designed for sequential analysis of incomplete trajectories or online learning based on an incrementally updated training set. Moreover, they typically involve tuning of many parameters, including ad-hoc anomaly thresholds, and may therefore suffer from overfitting and poorly-calibrated alarm rates. In this article, we propose and investigate the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) for online learning and sequential anomaly detection in trajectories. This is a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold. The discords algorithm, originally proposed by Keogh et al. , is another parameter-light anomaly detection algorithm that has previously been shown to have good classification performance on a wide range of time-series datasets, including trajectory data. We implement and investigate the performance of SHNN-CAD and the discords algorithm on four different labeled trajectory datasets. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning during unsupervised online learning and sequential anomaly detection in trajectories.
151 citations
Authors
Showing all 863 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jan Vaara | 5 | 17 | 88 |
Anders Pettersson | 5 | 6 | 85 |
Haglund Leif | 5 | 10 | 61 |
Torvald Martensson | 5 | 16 | 120 |
Zlatan Kapidžić | 5 | 5 | 121 |
Anders Modén | 5 | 7 | 72 |
Johannes Wintenby | 5 | 8 | 123 |
Ulrika Ohlander | 5 | 13 | 65 |
Grankäll Tommy | 5 | 6 | 49 |
Murat Şamil Aslan | 5 | 17 | 69 |
Mattias Hallgren | 5 | 6 | 715 |
Leif Haglund | 4 | 6 | 36 |
Per-Erik Cardell | 4 | 13 | 31 |
Tommy Grankäll | 4 | 9 | 53 |
Petrini Erik | 4 | 7 | 81 |