Estimation with Applications to Tracking and Navigation

Bar‐Shalom, Yaakov; Kirubarajan, Thiagalingam; Li, X.-Rong

doi:10.1002/0471221279

Cited by 5,811 publications

(5,320 citation statements)

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“…Finally, it has to be explained that, in the Central Processing Subsystem (CPS) of any MLAT system, there is always implemented a set of tracking algorithms [29], which significantly reduce the standard deviation of the "rough" output of an MLAT localization algorithm. However, this paper was not aimed to carry out the performance analysis of those tracking algorithms but, rather, to analyze the performance of the sole localization algorithms.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

Mantilla-Gaviria

Leonardi

Galati

et al. 2014

SIViP

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We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numer- ically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms; some general guidelines are drawn and conclusions are provided.

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Section: Simulation and Resultsmentioning

confidence: 99%

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

Mantilla-Gaviria

Leonardi

Galati

et al. 2014

SIViP

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“…To compare the consistency of the different mapping algorithms, the normalised estimation error squared (NEES) [3] of the map estimates from different algorithms are computed. The formula is…”

Section: A Simulation Results Using a Small Data Setmentioning

confidence: 99%

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D Map

Huang

Dissanayake

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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“…Kalman filtering). For others, approximate techniques have been developed, such as extended Kalman [1], particle [2,4], and "unscented" filters [13]. In particular particle filters have become widespread, because of their great ease and flexibility in approximating complex pdfs, and dealing with a wide range of dynamical and measurement models Their multi-modal nature makes them particularly suited for object tracking in cluttered environments, where uni-modal techniques might get stuck and loose track.…”

Section: Previous Workmentioning

confidence: 99%

“…whereas ν k is the user defined process noise, which has to be chosen to account for velocity changes during each sampling interval T k , and Γ k is the time dependent noise gain [1,2]. The shape component Σ = (c, b) is composed of a discrete parameter c k modeling the cluster membership and the continuous valued weight vector b.…”

Section: Dynamicsmentioning

confidence: 99%

A Bayesian Framework for Multi-cue 3D Object Tracking

Giebel

Gavrila

Schnörr

2004

Lecture Notes in Computer Science

102

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Abstract. This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linear subspace models or Dynamic Point Distribution Models (DPDMs), which can deal with both continuous and discontinuous appearance changes; the representation is learned fully automatically from training data. The representation is enriched with texture information by means of intensity histograms, which are compared using the Bhattacharyya coefficient. Direct 3D measurement is furthermore provided by a stereo system. State propagation is achieved by a particle filter which combines the three cues shape, texture and depth, in its observation density function. The tracking framework integrates an independently operating object detection system by means of importance sampling. We illustrate the benefit of our integrated multi-cue tracking approach on pedestrian tracking from a moving vehicle.

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Estimation with Applications to Tracking and Navigation

Cited by 5,811 publications

References 0 publications

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

Localization algorithms for multilateration (MLAT) systems in airport surface surveillance

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D Map

A Bayesian Framework for Multi-cue 3D Object Tracking

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