Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Image separation using particle filters
Digital Signal Processing
On a novel ACO-Estimator and its application to the Target Motion Analysis problem
Knowledge-Based Systems
Functional sampling density design for particle filters
Signal Processing
A new evolutionary particle filter for the prevention of sample impoverishment
IEEE Transactions on Evolutionary Computation
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
Convergence results for the particle PHD filter
IEEE Transactions on Signal Processing
A Basic Convergence Result for Particle Filtering
IEEE Transactions on Signal Processing
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
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Particle filter (PF) is a kind of flexible and powerful sequential Monte-Carlo technique designed to solve the optimal nonlinear parameter estimation numerically, and the degradation of particles in generic PF occurs when it is applied to the model switching dynamic system. To avoid this phenomenon, an ant stochastic decision based particle filter is proposed to encapsulate model switching information through dividing probabilistically particles into two model operations, and then a well defined re-sampling scheme is introduced to gain a better overlap with the true density function. To show the theoretic consistency with the generic PF, its basic convergence result is presented as well. Finally, we compare the performance of our proposed algorithm with that of other estimators (e.g., PF and moving ant estimator), and simulation results demonstrate its superior robustness of parameter estimation for switching dynamic system.