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Matrix permanent plays a key role in data association probabilitycalculations. Exact algorithms (such as Ryser's) scale exponentially withmatrix size. Fully polynomial time randomized approximation schemes exist butare quite complex. This letter introduces to the tracking community a simpleapproximation algorithm with error bounds, recently developed by Bero Roos, andillustrates its potential use for estimating probabilities of data associationhypotheses.

Roos' Matrix Permanent Approximation Bounds for Data Association Probabilities