Performance evaluation of fuzzy-based fusion rules for tracking applications

Performance evaluation of fuzzy-based fusion rules for tracking applications

The objective of this paper is to present and to evaluate the performance of particular fusion rules based on fuzzy T-Conorm/T-Norm operators for two tracking applications: (1) Tracking object’s type changes, supporting the process of objects’ identification (e.g. fighter against cargo, friendly aircraft against hostile ones), which, consequently is essential for improving the quality of genera...

Multitarget Tracking in Clutter based on Generalized Data Association: Performance Evaluation of Fusion Rules

The objective of this chapter is to present and compare different fusion rules which can be used for Generalized Data Association (GDA) for multitarget tracking (MTT) in clutter. Most of tracking methods including Target Identification (ID) or attribute information are based on classical tracking algorithms such PDAF, JPDAF, MHT, IMM, etc. and either on the Bayesian estimation and prediction of...

Fuzzy Logic Applications in Filtering and Fusion for Target Tracking

A fuzzy Kalman filter algorithm is developed for target tracking applications and its performance evaluated using several numerical examples. The approach is relatively novel. A comparison with Kalman filter and an adaptive tuning algorithm is carried out. The applicability and usefulness of fuzzy logic in data fusion is also demonstrated. The performance of both the extended Kalman filter and ...

Quadrature Rules for Fuzzy Integrals

Tracking object's type changes with fuzzy based fusion rule

In this paper the behavior of three combinational rules for temporal/sequential attribute data fusion for target type estimation are analyzed. The comparative analysis is based on: Dempster’s fusion rule proposed in Dempster-Shafer Theory; Proportional Conflict Redistribution rule no. 5 (PCR5), proposed in Dezert-Smarandache Theory and one alternative class fusion rule, connecting the combinati...