Efficient in-flight transfer alignment using evolutionary strategy based particle filter algorithm

Large initial misalignment between mother and daughter munitions make transfer alignment system nonlinear, because small angle approximation applicable to the system dynamics does not hold. Further, when the parameters of state transition matrix are based on current measurements, the system becomes time varying. A conventional Kalman filter fails to estimate misalignment in such situations. A particle filter performs satisfactorily, but, the performance suffers when the knowledge about the system is not accurate. Out of particles that get propagated through such improper system dynamics, only a few are retained and used for estimation purpose, due to sample impoverishment problem. In this work, it is claimed that better result can be obtained by employing an evolutionary strategy. Set of support points are generated for each particle by propagating the particle through an array of perturbed system dynamics, and, then by choosing best weight support point as apriori estimate from that set. The current work considers design of such evolutionary strategy based particle filter. For the purpose of proving robustness of proposed algorithm, simulation is first carried out on target tracking problem. Then it is applied to in-flight transfer alignment problem and its performance is found to be satisfactory.