A Practical Guide to Level One Data Fusion Algorithms

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Authors
  1. Peters, D.J.
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.

Il y a un résumé en français ici.

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

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