Saab AB

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.

Abstract: A three-dimensional finite element model of bolted composite joints has been developed to determine non-uniform stress distributions through the thickness of composite laminates in the vicinity of a bolt hole. An experimental programme was conducted to measure deformations, strains, and bolt load on test specimens for validation of the numerical model developed. Strains in the radial direction at different radii and angles were measured in the vicinity of the bolt hole at the shear plane between the plates. The degree of secondary bending in the joints was determined from strain measurements on both sides of the laminate at certain points. In the experiments, a number of parameters such as laminate layup, laminate thickness, bolt diameter, bolt type, clamping force and lateral support were varied. Each specimen configuration was analysed using the three-dimensional finite element model. Generally, comparison between computed and experimental results showed good agreement.

Abstract: In this paper, we present a progressive aerodynamic study of a blended wing body (BWB) configuration within a European project, MOB (A computational design engine incorporating multi-disciplinary design and optimisation for blended wing body configuration). The paper starts with an overview of various blended wing body aircraft design projects in relation to their aerodynamic behaviour. After a theoretical assessment of the ideal aerodynamic performance for the baseline configuration, viscous flow simulations were carried out to investigate the aerodynamic performance of the baseline design. The effects of spanwise distribution on the BWB 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. Following the inverse design studies, the BWB wing was mapped to an aerofoil optimisation problem and the optimised aerofoil was projected back to the BWB wing to investigate further performance improvement. Finally, three-dimensional aerodynamic surface optimisation of the BWB is carried out based on both continuous and discrete adjoint approaches. A progressive improvement of the aerodynamic performance is demonstrated for the given BWB planform and the design cruise condition.

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.

Abstract: In this paper, unsupervised clustering of normal vessel traffic patterns is proposed and implemented, where patterns are represented as the momentary location, speed and course of tracked vessels. The learnt cluster models are used for anomaly detection in sea traffic. The Gaussian Mixture Model is used as cluster model and a greedy version of the Expectation-Maximization algorithm is used as clustering algorithm. The models have been trained and evaluated using real recorded sea traffic. A qualitative analysis reveals that the most distinguishing anomalies found in the traffic are vessels crossing sea lanes and vessels traveling close to and in the opposite direction of sea lanes. In order to detect complex anomalies involving multiple vessels and/or behavior that develop over time, a more sophisticated pattern model should be developed. Yet, the generality of the proposed model is stressed, as it is potentially applicable to other domains involving surveillance of moving objects.