New class of Lagrangian relaxation-based algorithms for fast data association in multiple hypothesis tracking applications

Aubrey B. Poore; Alexander J. Robertson III; Peter J. Shea

doi:10.1117/12.213069

5 July 1995 New class of Lagrangian-relaxation-based algorithms for fast data association in multiple hypothesis tracking applications

Aubrey B. Poore, Alexander J. Robertson III, Peter J. Shea

Author Affiliations +

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213069
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Large classes of data association problems in multiple hypothesis tracking applications, including sensorfusion, can be formulated as multidimensional assignment problems. Lagrangian relaxation methods have beenshown to solve these problems to the noise level in the problem in real-time, especially for dense scenarios andfor multiple scans of data from multiple sensors. This work presents a new class of algorithms that circumventthe difficulties of similar previous algorithms. The computational complexity of the new algorithms is shownvia some numerical examples to be linear in the number of arcs.

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Aubrey B. Poore, Alexander J. Robertson III, and Peter J. Shea "New class of Lagrangian-relaxation-based algorithms for fast data association in multiple hypothesis tracking applications", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213069

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