Solution to a multisensor tracking problem with sensor registration errors

Nabaa, Nassib

doi:10.1109/7.745706

Cited by 91 publications

(38 citation statements)

References 12 publications

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“…It can be accomplished by extrapolation of pairs of measurements to a common time reference and posterior differentiation (see, for instance, [2,6]), by the procedure described in this paper or by other techniques (e.g. such as definition of pseudo measures from combinations of measures [4,5,10] or parallel target state and bias estimation [3,7,9]). In general, independently of the method applied, an observation of the bias terms is obtained from each measure or group of measures coming from the involved sensors.…”

Section: Bias Estimation and Correction Methodologymentioning

confidence: 99%

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Generic multisensor multitarget bias estimation architecture

Besada

Miguel

Bernardos

et al. 2012

IET Radar Sonar Navig.

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Current bias estimation algorithms for air traffic control (ATC) surveillance are focused on radar sensors, but the integration of new sensors (especially automatic dependent surveillance-broadcast and wide area multilateration) demands the extension of traditional procedures. This study describes a generic architecture for bias estimation applicable to multisensor multitarget surveillance systems. It consists on first performing bias estimations using measurements from each target, of a subset of sensors, assumed to be reliable, forming track bias estimations. All track bias estimations are combined to obtain, for each of those sensors, the corresponding sensor bias. Then, sensor bias terms are corrected, to subsequently calculate the target or sensor-target pair specific biases. Once these target-specific biases are corrected, the process is repeated recursively for other sets of less reliable sensors, assuming bias corrected measures from previous iterations are unbiased. This study describes the architecture and outlines the methodology for the estimation and the bias estimation design processes. Then the approach is validated through simulation, and compared with previous methods in the literature. Finally, the study describes the application of the methodology to the design of the bias estimation procedures for a modern ATC surveillance application, specifically for off-line assessment of ATC surveillance performance.

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Section: Bias Estimation and Correction Methodologymentioning

confidence: 99%

“…[2][3][4][5][6][7][8][9][10][11][12]). The basic idea is estimating every bias terms in the measurements potentially causing consistency mismatch, and removing them from raw measures, providing the tracking filters with bias-corrected (mostly unbiased) measures.…”

Section: Introductionmentioning

confidence: 99%

Generic multisensor multitarget bias estimation architecture

Besada

Miguel

Bernardos

et al. 2012

IET Radar Sonar Navig.

View full text Add to dashboard Cite

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“…An additional on-line refinement must be carried out with the fusion data while the coalition is active, to remove residual systematic errors of slow time variation among the sources of information, and guarantee the stability of fusion. This process of dynamic multi-sensor alignment is an important component for sensor fusion is referred in sensor fusion literature to as multi-sensor registration [35][36][37][38][39]. As mentioned above, on-line solutions are needed to estimate on-line the potentially timevariant systematic errors in parallel with tracking, using the same available data.…”

Section: Multi-camera Registrationmentioning

confidence: 99%

Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture

2010

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In this paper we present an extension of Cooperative Surveillance Multi-Agent System (CS-MAS) architecture to incorporate dynamic coalition formation. A specific coalition formation using fusion skills is shown so that the fusion process is distributed now in two layers: (i) a global layer in the fusion center, which initialize the coalitions and (ii), local layer within coalitions, with the dynamic instantiation of a local fusion agent. There are several types of autonomous agents: surveillancesensor agents, fusion center agent, local fusion agent, interface agents, record agents, planning agents, etc. Autonomous agents differ in their ability to carry out a specific surveillance task. A surveillance-sensor agent controls and manages individual sensors (usually video cameras). It has different capabilities depending on its functional complexity and limitation related to specific sensor nature aspects. In this work we add a new autonomous agent called local fusion agent to the CS-MAS architecture, addressing specific problems of on-line sensor alignment, registration, bias removal and data fusion. The local fusion agent it is dynamically created by the fusion center agent and involves several surveillance-sensor agents working in a coalition. We show how the inclusion of this new dynamic local fusion agent guarantee that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions.

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“…For instance, in a multi-radar multi-target system, each radar range and azimuth bias can cause significant errors in the target location. Therefore, many sensor registration algorithms have been proposed in the literature, such as the least squares (LS) method [2] and the maximum likelihood estimator method [3][4].…”

Section: Introductionmentioning

confidence: 99%

Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion

Zhu

Leung

et al. 2016

International Journal of Advanced Robotic Systems

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In this paper, we propose a novel simultaneous registration and fusion approach for tracking. This method is based on a recursive Variational Bayesian (RVB) algorithm, which is the online variant of the Variational Bayesian (VB) approach. Under the Bayesian framework, the states and parameters are recursively estimated. It is shown by simulation that the proposed RVB method has better estimation performance than the conventional approach.

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Solution to a multisensor tracking problem with sensor registration errors

Cited by 91 publications

References 12 publications

Generic multisensor multitarget bias estimation architecture

Generic multisensor multitarget bias estimation architecture

Data fusion to improve trajectory tracking in a Cooperative Surveillance Multi-Agent Architecture

Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion

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