Title :
Deterministic Branching Gauss Particles in the Passive Sonar Tracking Problem
Author :
Kazem, A. ; Salut, G.
Author_Institution :
LAAS-CNRS, Toulouse
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;
Conference_Titel :
Information and Communication Technologies, 2006. ICTTA '06. 2nd
Conference_Location :
Damascus
Print_ISBN :
0-7803-9521-2
DOI :
10.1109/ICTTA.2006.1684540
