An exploit of the Sequential Importance Sampling (SIS) algorithm using Differential Algebra (DA) techniques is derived to develop an efficient particle filter. The filter creates an original kind of particles, called scout particles, that bring information from the measurement noise onto the state prior probability density function. Thanks to the creation of high-order polynomial maps and their inversions, the scouting of the measurements helps the SIS algorithm identify the region of the prior more affected by the likelihood distribution. The result of the technique is two different versions of the proposed Scout Particle Filter (SPF), which identifies and delimits the region where the true posterior probability has high density in the SIS algorithm. Four different numerical applications show the benefits of the methodology both in terms of accuracy and efficiency, where the SPF is compared to other particle filters, with a particular focus on target tracking and orbit determination problems.

翻译：本文推导了一种利用微分代数（DA）技术改进序贯重要性采样（SIS）算法的方法，以构建一种高效的粒子滤波器。该滤波器生成一种新型粒子，称为侦察粒子，其作用是将测量噪声的信息传递至状态先验概率密度函数。通过构建高阶多项式映射并对其进行反演，对测量的侦察过程有助于SIS算法识别先验分布中受似然分布影响更大的区域。该技术最终形成了所提出的侦察粒子滤波器（SPF）的两种不同版本，其能够在SIS算法中识别并界定真实后验概率具有高密度的区域。通过四个不同的数值应用，展示了该方法在精度与效率方面的优势，其中将SPF与其他粒子滤波器进行了比较，并特别关注了目标跟踪与轨道确定问题。