Joint tracking of manoeuvring targets and classification of their manoeuvrability
EURASIP Journal on Applied Signal Processing
Bearings-only tracking of manoeuvring targets using particle filters
EURASIP Journal on Applied Signal Processing
Efficient Monte Carlo Filtering for Discretely Observed Jumping Processes
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Annealed SMC Samplers for Dirichlet Process Mixture Models
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Online Bayesian estimation of transition probabilities for Markovian jump systems
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Directional acoustic source orientation estimation using only two microphones
Digital Signal Processing
Axis rotation MTD algorithm for weak target detection
Digital Signal Processing
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Fixed rate state space models are the conventional models used to track the maneuvering objects. In contrast to fixed rate models, recently introduced variable rate particle filter (VRPF) is capable of tracking the target with a small number of states by imposing a Gamma distribution on the state arrival times while the object trajectory is approached by a single dynamic motion model. Using a single dynamic motion model limits the capability of estimating the characteristics of maneuvering and smooth regions of the trajectory. To overcome this weakness we introduce an adaptive tracking method which incorporates multiple model approach with the variable rate model structure. The proposed model referred to as multiple model variable rate particle filter (MM-VRPF) adaptively locates frequent state points to the maneuvering regions resulting in a much more accurate tracking while preserving the parsimonious representation for the smooth regions of the trajectory. This is achieved by including a mode variable into the conventional variable rate state vector that enables us to define different sojourn and motion parameters for each motion mode using the multiple model structure. Simulation results show that the proposed algorithm outperforms the conventional variable rate particle filter, fixed rate multiple model particle filter and interacting multiple model.