TRACKER: a sensor fusion simulator for generalised tracking

Okello, N.; Tang, D.; McMichael, D.W.

doi:10.1109/idc.1999.754184

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Figure 1 shows two targets on contrarotating linear spiral paths; their states coincide once every revolution. The state-difference test statistic clearly identifies them as different tracks when they are well separated, but it does not distinguish them when their states are similar 5 . The Bayes factor is an integrating measure, and it becomes steadily more strongly in favour of the two-target hypothesis.…”

Section: Associating Two Tracksmentioning

confidence: 92%

“…It is possible to treat the trackers for mixed linear Gaussian and discrete Markov processes in a unified way. Analytical association results can be found for such generalised tracks [3,4,5]. The joint state .…”

Section: The Tracking Modelmentioning

confidence: 99%

“…with some mathematical justification, however impractical5 It is an property of this motion that the difference between the states of the targets is periodic, and this accounts for the patterns of the state difference statistics and log Bayes factors.…”

mentioning

confidence: 99%

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Optimal track fusion using Bayes factors

McMichael¹,

Karan²

1999

1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium A

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In deciding whether to associate and fuse a group of tracks sent from independent local trackers, use should be made of all the data supporting them during theircommon history. This paper provides a closed form expression for the relevant Bayes factor, which is the ratio of the probability that the tracks are caused by a single target to the probability that they are each caused by a different target. The expression is recursive, and it applies to processes which may include a linear Gaussian process and several discrete Markov processes. Two variants are provided, one that requires the data to be sent from local trackers to the fusion centre, and one that only requires discrete probabilities state estimates and covariance matrices.

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Section: Associating Two Tracksmentioning

confidence: 92%

Section: The Tracking Modelmentioning

confidence: 99%

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Optimal track fusion using Bayes factors

McMichael¹,

Karan²

1999

1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium A

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“…There are a few examples of the integration of MATLAB and Java in engineering systems (see [11], [12]). However, the combination of Java and MATLAB for use in autonomous robot control is not well represented in the literature.…”

Section: B Matlab Supportmentioning

confidence: 99%

Incorporation of MATLAB into a distributed behavioral robotics architecture

Nelson

Doitsidis

Long

et al.

2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)

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Abstract-This paper presents a method that integrates MATLAB into a distributed behavioral robotics architecture. The architecture is written in Java and uses the Jini platform for distributed object registration, lookup and remote method invocation. The method described here can be used to integrate MATLAB into any Java-based behavioral architecture. The form of the integration allows a running MATLAB workspace to be accessed as a distributed object within the larger Java/Jini-based architecture. This is beneficial because MATLAB scripts and functions may be called in interpreted form and can make full use of MATLAB tool boxes and have access to the MATLAB workspace environment. This is not possible when MATLAB scripts are compiled into stand-alone C++, Java or p-code. The use of the architecture is demonstrated on an iRobot ATRV-JR robot and remote computer workstation. Experiments have been conducted to quantify GPS and odometry errors in outdoor environments using automated methods supported by the distributed architecture.

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Experimental validation of a MATLAB based control architecture for multiple robot outdoor navigation

Doitsidis

Nelson²,

Valavanis³

et al.

Proceedings of the 2005 IEEE International Symposium On, Mediterrean Conference on Control and Automation Intelligent Control,

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Design, implementation and experimental validation of a MATLAB based autonomous robot control framework is presented, supported by, and integrated into a distributed field robot architecture known as distributed-SFX. The MATLAB based framework is composed of multi sensor fuzzy logic robot controllers that utilize laser, GPS and odometer data, fusing such sensor data and filtering out noise, to improve collision free navigation. Extensive outdoor environment experiments with single and multiple mobile robots are performed to demonstrate waypoint and goal point navigation, and raster scan search patterns in unknown environments with static and dynamic obstacles. Results and videos are provided to justify the proposed approach.

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TRACKER: a sensor fusion simulator for generalised tracking

Cited by 5 publications

References 5 publications

Optimal track fusion using Bayes factors

Optimal track fusion using Bayes factors

Incorporation of MATLAB into a distributed behavioral robotics architecture

Experimental validation of a MATLAB based control architecture for multiple robot outdoor navigation

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