Title :
The Shifted Rayleigh Mixture Filter for Bearings-Only Tracking of Maneuvering Targets
Author :
Clark, J.M.C. ; Robbiati, S.A. ; Vinter, R.B.
Author_Institution :
Imperial Coll., London
fDate :
7/1/2007 12:00:00 AM
Abstract :
This paper introduces the shifted Rayleigh mixture filter (SRMF), which is based on jump Markov linear systems. The formulation permits the presence of clutter. For bearings-only tracking problems involving maneuvering targets, the conditional density of the target state given the available measurements evolves as a growing mixture of probability density functions associated with a history of manoeuvre "modes." Similar to other "mixture" algorithms, the SRMF approximates this conditional density by a Gaussian mixture of fixed order. Unlike the extended or unscented Kalman filters, the shifted Rayleigh filter incorporates an exact calculation of the posterior density, when the prior is assumed to be Gaussian, given the latest bearings measurement. Computer simulations are provided to demonstrate the performance of the algorithm.
Keywords :
Kalman filters; Markov processes; particle filtering (numerical methods); probability; target tracking; Gaussian mixture reduction; bearings-only tracking; jump Markov linear systems; maneuvering targets; manoeuvre modes; particle filter; probability density functions; shifted Rayleigh mixture filter; unscented Kalman filters; Computer simulation; Density measurement; Gaussian approximation; History; Linear systems; Nonlinear equations; Nonlinear filters; Particle tracking; Probability density function; Target tracking; Bearings-only tracking; Gaussian mixture reduction; jump Markov linear models; mixture algorithms; particle filter (PF); shifted Rayleigh filter; unscented Kalman filter;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2007.894378