Roos' Matrix Permanent Approximation Bounds for Data Association Probabilities
Signal Processing
2018-07-18 v1 Methodology
Abstract
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.
Cite
@article{arxiv.1807.06480,
title = {Roos' Matrix Permanent Approximation Bounds for Data Association Probabilities},
author = {Lingji Chen},
journal= {arXiv preprint arXiv:1807.06480},
year = {2018}
}