An ant stochastic decision based particle filter and its convergence

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.