DocumentCode
456434
Title
Deterministic Branching Gauss Particles in the Passive Sonar Tracking Problem
Author
Kazem, A. ; Salut, G.
Author_Institution
LAAS-CNRS, Toulouse
Volume
1
fYear
0
fDate
0-0 0
Firstpage
1170
Lastpage
1175
Abstract
This work is concerned with the tracking of passive sonar targets (bearing and Doppler measurements), by maximizing the probability density in the presence of noise. We exhibit a deterministic particle filtering technique that allows high performance with a reduced number of particles. Simulation results are given, including the case of unknown manoeuvres from the target, represented by a priori Poisson control inputs
Keywords
Doppler measurement; Gaussian processes; Poisson distribution; particle filtering (numerical methods); probability; sonar tracking; target tracking; Doppler measurement; Gauss particle; bearing measurement; deterministic particle filtering; passive sonar tracking; priori Poisson control; probability density; Acceleration; Acoustic signal detection; Filtering; Gaussian noise; Gaussian processes; Nonlinear filters; Particle tracking; Sonar detection; Sonar measurements; Target tracking; deterministic particle algorithm; nonlinear filtering; sonar tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Communication Technologies, 2006. ICTTA '06. 2nd
Conference_Location
Damascus
Print_ISBN
0-7803-9521-2
Type
conf
DOI
10.1109/ICTTA.2006.1684540
Filename
1684540
Link To Document