New multidimensional data association algorithm for multisensor-multitarget tracking

Aubrey B. Poore; Alexander J. Robertson III

doi:10.1117/12.217718

1 September 1995 New multidimensional data association algorithm for multisensor-multitarget tracking

Aubrey B. Poore, Alexander J. Robertson III

Proceedings Volume 2561, Signal and Data Processing of Small Targets 1995; (1995) https://doi.org/10.1117/12.217718
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

Large classes of data association problems in multiple hypothesis tracking applications involving multiple and single sensor systems can be formulated as multidimensional assignment problems. Lagrangian relaxation methods have been shown to solve these problems to the noise level in the problem in real-time, especially for dense scenarios and for multiple scans of data from multiple sensors. This work presents a new class of algorithms that circumvent some of the shortcomings of previous algorithms. The computational complexity of the new algorithms is shown via some numerical examples to be linear in the number of arcs. Numerical results demonstrate the superior solution quality of the relaxation algorithm compared to proven greedy methods. Decomposition is also shown to provide improved execution times for clustered association problems that regularly arise in tracking.

Citation Download Citation

Aubrey B. Poore and Alexander J. Robertson III "New multidimensional data association algorithm for multisensor-multitarget tracking", Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); https://doi.org/10.1117/12.217718

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