Joint sensor registration and track-to-track fusion for distributed trackers

Okello, N.; Challa, Subhash

doi:10.1109/taes.2004.1337456

Cited by 110 publications

(53 citation statements)

References 12 publications

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“…To translate range-azimuth-height measurement to the common stereographic plane where all bias calculations are performed, a non-linear coordinate transformation method [22,23] is used. Although [11] proposes the use of ECEF coordinates for bias estimation, from our experience stereographic transformations and related linearisations can be successfully used for network coverage up to 5.000 km. This method implements a function to be called i /R a dar(-)-To project the error terms into the stereographic plane, a first order approximation of this transformation can be made, resulting…”

Section: Atc Radar Error Modelsmentioning

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: Atc Radar Error Modelsmentioning

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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“…The traditional method is that the sensor bias is included in the augmented state equations, and then the Kalman filter, extended Kalman filter (EKF) or unscented Kalman filter (UKF) is used to estimate the augmented states' systems. In [7], Okello, et al formulated the joint registration and fusion at the track level as a Bayesian estimation problem, and proposed the extended Kalman filter (EKF) by augmenting the state vector with sensor bias. In [8], the augmented Kalman filter was proposed to perform the sensor registration.…”

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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“…A constant B s (k)C s (k) also results in incomplete observability as discussed in [11]. Using the measured azimuth θ m s (k) and range r m s (k) from sensor s, B s (k) can be written as [19] (6) Finally, the new covariance matrix of the measurement in Cartesian coordinates (omitting index k in the measurements for clarity) is given by where one can use the observed range and azimuth as well.…”

mentioning

confidence: 99%

A practical bias estimation algorithm for multisensor-multitarget tracking

Taghavi

Kirubarajan

et al. 2016

IEEE Trans. Aerosp. Electron. Syst.

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Bias estimation or sensor registration is an essential step in ensuring the accuracy of global tracks in multisensor-multitarget tracking. Most previously proposed algorithms for bias estimation rely on local measurements in centralized systems or tracks in distributed systems, along with additional information like covariances, filter gains or targets of opportunity. In addition, it is generally assumed that such data are made available to the fusion center at every sampling time. In practical distributed multisensor tracking systems, where each platform sends local tracks to the fusion center, only state estimates and, perhaps, their covariances are sent to the fusion center at non-consecutive sampling instants or scans. That is, not all the information required for exact bias estimation at the fusion center is available in practical distributed tracking systems. In this paper, a new algorithm that is capable of accurately estimating the biases even in the absence of filter gain information from local platforms is proposed for distributed tracking systems with intermittent track transmission. Through the calculation of the Posterior Cramér-Rao lower bound and various simulation results, it is shown that the performance of the new algorithm, which uses the tracklet idea and does not require track transmission at every sampling time or exchange of filter gains, can approach the performance of the exact bias estimation algorithm that requires local filter gains.

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Joint sensor registration and track-to-track fusion for distributed trackers

Cited by 110 publications

References 12 publications

Generic multisensor multitarget bias estimation architecture

Generic multisensor multitarget bias estimation architecture

Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion

A practical bias estimation algorithm for multisensor-multitarget tracking

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