Efficient data association with multivariate Gaussian distributed states
US6239740A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Jun 15, 1993 |
| Grant date | May 29, 2001 |
| Priority date | — |
| Expiry date | Jun 15, 2013 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S706/905
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
We describe an efficient algorithm for evaluating the (weighted bipartite graph of) associations between two sets of data with gaussian error, e.g., between a set of measured state vectors and a set of estimated state vectors. First a general method is developed for determining, from the covariance matrix, minimal d-dimensional error ellipsoids for the state vectors which always overlap when a gating criterion is satisfied. Circumscribing boxes, or d-ranges, for the data ellipsoids are then found and whenever they overlap the association probability is computed. For efficiently determining the intersections of the d-ranges a multidimensional search tree method is used to reduce the overall scaling of the evaluation of associations. Very few associations that lie outside the predetermined error threshold or gate are evaluated. Empirical testing for variously distributed data in both three and eight dimensions indicate that the scaling is significantly reduced from N.sup.2, where N is the size of the data set. Computational loads for many large scale (N>10-100) data association tasks may therefore be significantly reduced by this or related methods.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.