A Practical Guide to Level One Data Fusion Algorithms
- Authors
- Corporate Authors
- Defence Research Establishment Atlantic, Dartmouth NS (CAN)
- Abstract
- Level one data fusion is the process of combining data to order to track and classify individual entities. This document introduces the basic concepts and presents a core selection of standard algorithms, such as the Kalman Filter, the Interacting Multiple Model (IMM) filter, the Probabilistic Data Association Filter (PDAF) and its Joint variant, the Munkres algorithm for Nearest Neighbour (NN) association, and Multiple-Hypothesis Tracking (MHT), among others. It is intended to serve as a convenient one-stop repository of algorithms.
- Keywords
- Data association;Target tracking
- Report Number
- DREA-TM-2001-201 — Technical Memorandum
- Date of publication
- 01 Dec 2001
- Number of Pages
- 72
- DSTKIM No
- CA021480
- CANDIS No
- 518200
- Format(s):
- CD ROM
Document 1 of 1
- Date modified: