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Auflistung nach Autor:in "Mihaylova, Lyudmila"

1 - 6 von 6
  • Konferenzbeitrag
    Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection
    (Informatik 2007 – Informatik trifft Logistik – Band 2, 2007) Bhaskar, Harish; Mihaylova, Lyudmila; Maskell, Simon
    Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background subtraction (BS) refers to the process of segmenting moving regions from video sensor data and is usually performed at pixel level. In its standard form this technique involves building a model of the background and extracting regions of the foreground. In this paper, we propose a cluster-based BS technique using a mixture of Gaussians. An adaptive mechanism is developed that allows automated learning of the model parameters. The efficiency of the designed technique is demonstrated in comparison with a pixel-based BS [ZdH06].
  • Konferenzbeitrag
    Bearings only versus bearings and extent tracking for missile guidance systems utilising particle methods
    (INFORMATIK 2010. Service Science – Neue Perspektiven für die Informatik. Band 2, 2010) Watson, Alexander; Mihaylova, Lyudmila; Vorley, Dave
    This paper addresses the problem of bearings only and bearings and extent tracking for closed loop missile guidance systems using particle methods compared with derivative free methods. Lack of observability is a typical phenomenon for the bearings only tracking problem. We demonstrate that exploiting angular extent information does help mitigating the lack of observability issues. The simulation results show that the particle filter ourperforms the derivative free algorithms.
  • Konferenzbeitrag
    Enhanced positioning techniques for hybrid wireless networks
    (INFORMATIK 2011 – Informatik schafft Communities, 2011) Zvikhachevskaya, Anna; Gourov, Vassil; Awang Md Isa, Azmi; Mihaylova, Lyudmila; Markarian, G.
    This paper presents a reliable and accurate positioning method, which provides location estimates for the mobile user in a wireless network where IEEE802.11/WiFi, IEEE802.16/WiMAX, 3GPP LTE and Bluetooth wireless technologies are deployed. The developed data fusion algorithm utilises measurements and features such as Time of Arrival (TOA) and multiple input, multiple output (MIMO) antennas and wireless links between mobile users in order to enhance the positioning accuracy. Therefore, in the proposed concept of mobile user positioning is proposed and it is applied to hybrid wireless network environment. Results satisfy the FCC requirements for the network and mobile-centric positioning solution.
  • Konferenzbeitrag
    Information driven approach for sensor positioning in wireless sensor networks
    (INFORMATIK 2011 – Informatik schafft Communities, 2011) Ali, Arshad; Xydeas, Costas; Mihaylova, Lyudmila; Gning, El Hadji Amadou
    Wireless Sensor Networks (WSNs) are amongst the most important of the new emerging technologies and have shown an explosive growth in recent years for monitoring physical phenomena. Large scale WSNs face various challenges such as lack of coverage, large deployment areas and need of efficient sensor positioning. This paper introduces an approach for sensor management by using Kriging interpolation. The proposed technique affords monitoring of phenomena of interest in a distributed manner. A very good accuracy is achieved by using the available data coming from different sensor nodes. This is illustrated over an example for temperature monitoring.
  • Konferenzbeitrag
    Parallelised gaussian mixture filtering for vehicular traffic flow estimation
    (Informatik 2009 – Im Focus das Leben, 2009) Mihaylova, Lyudmila; Gning, Amadou; Doychinov, Viktor; Boel, René
  • Konferenzbeitrag
    A sequential Monte Carlo approach for extended object tracking in the presence of clutter
    (INFORMATIK 2011 – Informatik schafft Communities, 2011) Petrov, Nikolay; Mihaylova, Lyudmila; Gning, Amadou; Angelova, Donka
    Extended objects are characterised with multiple measurements originated from different locations of the object surface. This paper presents a novel Sequential Monte Carlo (SMC) approach for extended object tracking in the presence of clutter. The problem is formulated for general nonlinear problems. The main contribution of this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded region. Simulation results are presented when the object is surrounded by a circular region. Accurate estimation results are presented both for the object kinematic state and object extent.