Constrained tracking filters for A-SMGCS

Farina, A.; Ferranti, Luca; Golino, G.

doi:10.1109/icif.2003.177476

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…Airport congestion is already a major issue for airport management and it is important to estimate airport capacity and the associated delays (Stamatopoulos et al 2004). Advanced Surface Movement, Guidance and Control Systems (A-SMGCS) is a set of technologies and procedures that aim to efficiently manage airport traffic, integrating surveillance, control, guidance, and routing (Garcia et al 2002, Piazza 2002, Farina et al 2003. Due to a larger number of commercial and private airplanes operating simultaneously, runway safety becomes a greater concern.…”

Section: Introductionmentioning

confidence: 99%

Neural network models to detect airplane near-collision situations

Hielscher

Doshi

Gupta

et al. 2010

Transportation Planning and Technology

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Section: Introductionmentioning

confidence: 99%

Neural network models to detect airplane near-collision situations

Hielscher

Doshi

Gupta

et al. 2010

Transportation Planning and Technology

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“…When the information about roads is as accurate as (or even better than) radar measurements, it is naturally desired to use such information to improve target tracking. In the literature, road information has been incorporated into target state estimation (1) [Koch, 2004;Ulmke, 2003;Ulmke andKoch, 2005, 2006], (4) via a particle filter [Agate, and Sullivan, 2003], (5) as pseudo measurements [Farina, Ferranti, and Golino, 2003], (6) via one-dimensional (1D) representation of a target motion along a curvilinear road [Yang, Bakich, and Blasch, 2005], and (7) as linear or nonlinear state constraints in an optimization procedure Blasch, 2008, 2009].…”

Section: Introductionmentioning

confidence: 99%

Ground target track bias estimation using opportunistic road information

Yang¹,

Blasch

Patrick

et al. 2010

Proceedings of the IEEE 2010 National Aerospace &Amp; Electronics Conference

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Track-to-track fusion is widely used by largescale distributed surveillance systems to integrate multisensor tracking data. A critical step prior to track-totrack fusion is track-to-track assignment. However, when ground targets are tracked by airborne sensors, the target tracks contain not only tracking sensor errors but also navigation errors of the sensor platforms. If the errors are not compensated for properly, biased tracks create false assignments and lead to erroneously fused tracks. In this paper, track biases produced by individual trackers are characterized in terms of translation and rotation (and to a lesser extent by scaling) with respect to the common reference frame in which the track-to-track fusion will take place. Opportunistic information about the roads on which ground targets are moving is explored to estimate the track biases, akin to a system calibration, which can be used not only to remove biases from past and present tracks but also to provide corrections for future estimates. The proposed bias estimate method is based on binary image matching to estimate rotation and translation. A two-dimensional fast Fourier transform (2D FFT) is used to implement 2D search and correlation efficiently. Simulation results are presented to illustrate the proposed opportunistic road information based bias estimation (ORIBE) method and its performance as a function of target track accuracy and spatial resolution in forming track and road map images.

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“…This topic has been recently addressed by several authors [1 ]- [4]. In all references, road map information is managed by means of a YS-DMM (Yariable StructureDynamic Multiple Model) approach wherein different roadmodels and associated road-filters are used to take care of the possible hypotheses in proximity of the road junctions.…”

Section: Introductionmentioning

confidence: 99%

Tracking a target moving in a known road network by square-root equality-constrained filtering

Chisci

Mavino

Papi

et al. 2008

2008 IEEE Radar Conference

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In this paper, a square-root equality-constrained linear filter is used to track a target moving along a road. This choice provides a computationally efficient, algorithmically simple and numerically robust solution for the implementation of a VS-DMM (Variable Structure -Dynamic Multiple Model) filter. A performance evaluation via Monte Carlo simulations shows the effectiveness of the proposed approach. II. GENERALIZED INFORMATION ARRAYSHereafter the notation v f"V (m, Q) will be used to denote that v is a random vector of mean m and covariance Q. For the purpose of linear estimation, all the useful information on a random vector x can be represented by the following linear model:

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Constrained tracking filters for A-SMGCS

Cited by 14 publications

References 6 publications

Neural network models to detect airplane near-collision situations

Neural network models to detect airplane near-collision situations

Ground target track bias estimation using opportunistic road information

Tracking a target moving in a known road network by square-root equality-constrained filtering

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