Technique for radar and infrared search and track data fusion

Quaranta, Carlo; Balzarotti, Giorgio

doi:10.1117/1.oe.52.4.046401

Cited by 11 publications

(10 citation statements)

References 7 publications

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“…For simplifying the description, we assume the synchronization of the data from the sensors. Different conditions can be managed as described in 5 . Ts is the frame period.…”

Section: -An Example: Radar-irst Data Fusionmentioning

confidence: 99%

“…The synchronization of the data streams of sensors is obtained through the technique presented in 5 and in the sequel of the article we will assume the above data flow synchronized.…”

Section: -General Definition Of the Context For Sensors Data Fusionmentioning

confidence: 99%

“…That is an erroneous track-to-track association and is the main reason for the existence of a tracking system for the fused (associated) tracks. A possible solution to such an issue is to use the Joint Probabilistic Data Association (JPDA) algorithm, 8 and 9 , with some adaptations, 5 : the fused tracks and the tracks are managed, respectively, as tracks and detections in the generation of the validation matrix and in the events matrices of JPDA algorithm. With such approach, we can proceed by using ( ) Ω x as input and covariance matrix R as in any common one-sensor tracking system.…”

Section: -Mixed Detections To Fused Tracks Association and Trackingmentioning

confidence: 99%

“…whose statistic are given in 5 Finally, the mixed detections Ω obtained through the tests 1 to 4 will feed the data fusion system and, we remember, we use the following fusion equation: …”

Section: -Tracks To Tracks Association Fusion and Tracking Processmentioning

confidence: 99%

“…For all the tracks that have passed test number 1, we proceed to create the mixed detection Ω: of the various terms of Ω are evaluated in5 . Appendix B, in addition, PR being the covariance matrix of the estimation error provided by the Radar Data Processor,…”

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confidence: 99%

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A technique for sensors fusion with limited number of common measures

Quaranta

Balzarotti

2015

SPIE Proceedings

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An algorithm is proposed here that overcomes the problem of the not exhaustive number of common measures from sensors of different kind. In the presence of a suite of heterogeneous sensors, the data fusion process has to deal with the problem of managing different information, generally not directly comparable. The analysis of the mathematical model is carried out considering a data fusion system between radar and Infrared Search and Track (IRST) where the measurement of the range is achieved by radar only. Simulation results demonstrate the effectiveness of the algorithm as regards the fusion process, tracking and correctness of association among tracks from different sensors. A comparison with a known approach from the literature about the fusion equation is also performed.

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“…For simplifying the description, we assume the synchronization of the data from the sensors. Different conditions can be managed as described in 5 . Ts is the frame period.…”

Section: -An Example: Radar-irst Data Fusionmentioning

confidence: 99%

“…The synchronization of the data streams of sensors is obtained through the technique presented in 5 and in the sequel of the article we will assume the above data flow synchronized.…”

Section: -General Definition Of the Context For Sensors Data Fusionmentioning

confidence: 99%

Section: -Mixed Detections To Fused Tracks Association and Trackingmentioning

confidence: 99%

Section: -Tracks To Tracks Association Fusion and Tracking Processmentioning

confidence: 99%

mentioning

confidence: 99%

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