A Neural Network Implementation of a Data Association Algorithm
Allen L. Barker, Donald E. Brown, Worthy N. Martin · INFORMS journal on computing · 1990
In this paper we are concerned with a time varying set of entities located in a fixed field. These entities are sensed at discrete time instances with a single sensor modality. At a given time instant a collection of bivariate Gaussian sensor reports is produced, estimating the locations of a subset of the entities present in the field. A database of reports is maintained which should ideally contain exactly one report for each entity that has been sensed. Whenever a collection of sensor reports is received the database must be updated to reflect the new information. This updating requires correspondence processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. We present an algorithm for performing this correspondence processing under the assumptions that each new collection of sensor reports contains at most one report for any entity, and that the database can be reasonably assumed to contain at most one report for any entity. This algorithm is based on pairwise distance measures between Gaussian distributions. We consider several such distance measures, and present simulation results indicating that the divergence is a reasonable choice. A formulation of the divergence between two bivariate Gaussians as a scalar product is given. We describe a neural network implementation of our algorithm, along with a proof that the network will converge and, under certain restrictions, exactly compute our correspondence algorithm. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.