  Matrix permanent plays a key role in data association probability
calculations. Exact algorithms (such as Ryser's) scale exponentially with
matrix size. Fully polynomial time randomized approximation schemes exist but
are quite complex. This letter introduces to the tracking community a simple
approximation algorithm with error bounds, recently developed by Bero Roos, and
illustrates its potential use for estimating probabilities of data association
hypotheses.
