&lt;title&gt;Joint MAP bias estimation and data association: algorithms&lt;/title&gt;

Danford, Scott; Kragel, Bret D.; Poore, Aubrey B.

doi:10.1117/12.735202

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…(14) and (15) exhibits a comprehensive consideration of multiple factors, including the difference between local tracks, the tolerant extent of non-rigid transformation and the appearance of unpaired tracks. Moreover, the constraints that the correspondence matrix needs to satisfy were specified explicitly.…”

Section: Original Tps-rpm Formulationmentioning

confidence: 99%

“…A joint track association and relative bias estimation problem was formulated and a solution based on the Dijkstra search was developed in [13]. A solution based on the branch-and-bound framework for the same problem was described in [14]. Another joint method was proposed in [15] which achieves the maximum likelihood estimates for relative bias and date association based on the Murty's K-best algorithm.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Robust track-to-track association in the presence of sensor biases and missed detections

Zhu

Wang

2016

Information Fusion

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Section: Original Tps-rpm Formulationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Robust track-to-track association in the presence of sensor biases and missed detections

Zhu

Wang

2016

Information Fusion

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“…An integrated way may be promising to implement data association and bias estimation jointly. Related work about this topic can be found in [17][18][19], where the sensor biases are assumed to be a direct additive term on local estimates, and only relative biases can be estimated. In addition, in the case of detection probability less than one, the target sets detected by different sensors do not coincide.…”

Section: Extension To the Multi-target Case Or The Case Of Detection mentioning

confidence: 99%

“…An integrated way may be promising to implement data association and bias estimation jointly. Related work about this topic can be found in [17–19], where the sensor biases are assumed to be a direct additive term on local estimates, and only relative biases can be estimated.…”

Section: Proposed Approach To Sensor Registration and Track Fusionmentioning

confidence: 99%

Track fusion in the presence of sensor biases

Zhu

Chen

2014

IET signal process.

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A computationally effective approach is developed in this study to deal with the problem of track fusion in the presence of sensor biases. Aiming at the case that sensor biases are implicitly included in the local estimates, a pseudo-measurement equation is derived based on the Taylor series expansion firstly, which reveals the relationship explicitly between local estimates and the sensor biases; and then, the bias estimates can be obtained in the rule of recursive least squares; finally, based on the derived pseudo-measurement equation, the sensor biases can be removed from the original local estimates and track fusion can be carried out directly and easily. Monte Carlo simulations demonstrate the efficiency and effectiveness of the proposed approach compared with the competing algorithms.

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“…Recently, more attention has been paid to the joint association and bias removal approaches [7]- [10]. These approaches, which formulate a nonconvex mixed integer nonlinear programming problem, are usually difficult to solve [8], [9]. There are some proposed algorithms using the iterative strategy that performs TTTA and registration alternately.…”

Section: Introductionmentioning

confidence: 99%

Track-to-Track Association for Biased Data Based on the Reference Topology Feature

Tian

Wang

Shan

et al. 2014

IEEE Signal Process. Lett.

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In this letter, we propose a novel track-to-track association (TTTA) algorithm based upon the reference topology (RET) feature, which is insensitive to sensor biases. A rigorous mathematical definition of RET is presented. The insensitivity of RET to sensor biases is analyzed theoretically. In order to construct the association cost matrix, we make use of the optimal subpattern assignment (OSPA) metric to measure the distance between two RETs. Simulation results demonstrate the advantages of the proposed algorithm.Index Terms-Multitarget-multisensor tracking, optimal subpattern assignment (OSPA) metric, reference topology (RET), track-to-track association (TTTA).

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<title>Joint MAP bias estimation and data association: algorithms</title>

Cited by 10 publications

References 7 publications

Robust track-to-track association in the presence of sensor biases and missed detections

Robust track-to-track association in the presence of sensor biases and missed detections

Track fusion in the presence of sensor biases

Track-to-Track Association for Biased Data Based on the Reference Topology Feature

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