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

机译：使用基于进化策略的粒子滤波算法进行高效的空中转移对准

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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.
机译：母弹药和子弹药之间的初始偏差大，使传递对准系统呈非线性，因为不能适用于系统动力学的小角度近似值。此外，当状态转换矩阵的参数基于电流测量值时，系统将随时间变化。在这种情况下，传统的卡尔曼滤波器无法估计失准。粒子过滤器的性能令人满意，但是当有关系统的知识不准确时，性能会受到影响。由于样本贫乏问题，在通过这种不适当的系统动力学传播的粒子中，只有少数保留并用于估计目的。在这项工作中，声称可以通过采用进化策略来获得更好的结果。通过使粒子通过一系列扰动的系统动力学进行传播，然后通过从该集合中选择最佳权重支持点作为先验估计，来为每个粒子生成一组支持点。当前的工作考虑了这种基于进化策略的粒子滤波器的设计。为了证明所提算法的鲁棒性，首先对目标跟踪问题进行了仿真。然后将其应用于飞行中的飞行对准问题，发现其性能令人满意。

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