Title :
Sequential Monte Carlo filtering for multi-aspect detection/tracking
Author :
Bruno, Marcelo G S ; de Araujo, R.V. ; Pavlov, Anton G.
Author_Institution :
Inst. Tecnologico de Aeronautica, Sao Jose Dos Campos
Abstract :
We propose in this paper a mixed-state sequential Monte Carlo (SMC) filter for joint multiframe detection and tracking of multiaspect targets in cluttered image sequences. The proposed detector/tracker is a sampling/importance resampling (SIR) particle filter that uses resampling according to the weights to combat particle degeneracy and also includes an additional Metropolis-Hastings (MH) move step to avoid particle impoverishment. The dynamic models for target motion and target aspect and the statistical model for the spatially correlated background clutter are assumed as prior knowledge in the design of the filter. The performance of the algorithm is investigated using simulated image sequences generated from real infrared airborne radar (IRAR) data
Keywords :
airborne radar; image sequences; importance sampling; particle filtering (numerical methods); radar clutter; radar imaging; sequential estimation; target tracking; Metropolis-Hastings step; cluttered image sequences; dynamic models; importance resampling particle filter; infrared airborne radar; mixed-state particle filters; multiaspect detection; multiaspect target tracking; multiframe detection; multiframe tracking; sampling particle filter; sequential Monte Carlo filtering; statistical model; target aspect; target motion; Detectors; Filtering; Filters; Image sampling; Image sequences; Monte Carlo methods; Particle tracking; Radar tracking; Sliding mode control; Target tracking; Gauss-Markov random fields; Markov Chain Monte Carlo; Mixed-state particle filters; multiaspect target tracking; multiframe detection;
Conference_Titel :
Aerospace Conference, 2005 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
0-7803-8870-4
DOI :
10.1109/AERO.2005.1559501
