Adaptive Clutter Measurement Density Estimation for Improved Target Tracking
Authors:
- 15 April 2011
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Aerospace and Electronic Systems
- Vol. 47 (2) , 1457-1466
Abstract
In a surveillance situation the origin of each measurement is uncertain. Each measurement may be a false (clutter) measurement, or it may be a target detection. Probabilistic methods are usually used to discriminate between the clutter and the target measurements. Clutter measurement density is an important parameter in this process. The values of the clutter measurement density in the surveillance space are rarely known a priori, and are usually estimated using sensor data and track information. A novel approach is presented and evaluated for estimating the values of clutter measurement density, which significantly enhances target tracking. Simulation results validate this approach.Keywords
This publication has 13 references indexed in Scilit:
- Multiscan Multitarget Tracking in Clutter with Integrated Track Splitting FilterIEEE Transactions on Aerospace and Electronic Systems, 2009
- Integrated track splitting filter - efficient multi-scan single target tracking in clutterIEEE Transactions on Aerospace and Electronic Systems, 2007
- Joint integrated probabilistic data association: JIPDAIEEE Transactions on Aerospace and Electronic Systems, 2004
- Clutter map information for data association and track initializationIEEE Transactions on Aerospace and Electronic Systems, 2004
- PDAF with multiple clutter regions and target modelsIEEE Transactions on Aerospace and Electronic Systems, 2003
- Integrated real-time estimation of clutter density for trackingIEEE Transactions on Signal Processing, 2000
- Integrated probabilistic data associationIEEE Transactions on Automatic Control, 1994
- Interacting multiple model tracking with target amplitude featureIEEE Transactions on Aerospace and Electronic Systems, 1993
- Sonar tracking of multiple targets using joint probabilistic data associationIEEE Journal of Oceanic Engineering, 1983
- An algorithm for tracking multiple targetsIEEE Transactions on Automatic Control, 1979