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Interacting multiple model unscented filter for tracking a ballistic missile during its boost phase

机译：相互作用的多模型无味过滤器，用于在弹道导弹发射阶段跟踪

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Boost-phase detection of a ballistic missile is an attractive option for defense systems because boosting rockets are easy to detect and, in this phase, countermeasures are less effective. Nevertheless, it raises technical criticalities due to the short burn-out times and to the unknown features and behavior of the missile. The boosting trajectory of a ballistic missile, in fact, can be divided in at least three phases, each one characterized by a different steering strategy: the vertical arc, the pitch maneuver, and the gravity turn. Furthermore, the overall trajectory is very sensitive to variations in the parameters of the model and in the kick angle chosen during the pitch maneuver. Therefore, the tracking of a boosting missile presents two major difficulties: the identification of the missile parameters (ballistic coefficient, mass rate, thrust over weight ratio, etc.), and the determination of the steering strategy. The multiple models filter approach has been adopted in this paper to cope with these issues. In particular, an improved version of the Interacting Multiple Model Unscented Filter (IM-MUF) for tracking a missile has been introduced. Essentially, a modified form of the filter Markov Transition Matrix (MTM), ad hoc designed for the problem of tracking a ballistic missile during its boost phase, is proposed. The probabilities of the state transitioning from one dynamic model to another are given by the MTM elements. In the traditional approach, these probabilities are supposed constant and known. In this work these conservative hypotheses have been relaxed and these probabilities have been considered to be functions of the estimated state vector, thanks to specific features of the missile trajectory. A numerical, three-dimensional Monte Carlo simulation has been implemented in order to reconstruct a state vector composed by the position, velocity and missile parameters vectors. The modified IMMUF runs several models, each one accounting for one of the phases of flight with a different value of thrust direction, and combines estimations in order to provide the final estimation. Noisy measurements are obtained from a radar. The results show that the proposed IMMUF with the variable MTM outperform the traditional form of the IMMUF, by giving more accurate estimation.
机译：在防御系统中，弹道导弹的助推阶段检测是一个有吸引力的选择，因为助推火箭很容易检测到，并且在此阶段，对策效果不佳。然而，由于短的烧尽时间以及导弹的未知特征和行为，它提高了技术上的关键性。实际上，弹道导弹的助推轨迹至少可以分为三个阶段，每个阶段都具有不同的操纵策略：垂直弧，俯仰操纵和重力转向。此外，总体轨迹对模型参数以及在俯仰操纵期间选择的脚踢角的变化非常敏感。因此，跟踪助推导弹存在两个主要困难：导弹参数的识别（弹道系数，质量比，推力重量比等），以及操纵策略的确定。本文采用了多种模型过滤方法来解决这些问题。特别地，已经引入了用于跟踪导弹的交互式多模型无味滤波器（IM-MUF）的改进版本。本质上，提出了一种改进形式的滤波器马尔可夫转换矩阵（MTM），该滤波器是专为在弹道导弹的助推阶段追踪弹道导弹的问题而设计的。状态从一个动态模型转换到另一个动态模型的概率由MTM元素给出。在传统方法中，假定这些概率是恒定的并且是已知的。在这项工作中，由于导弹弹道的特定特征，这些保守的假设被放宽了，并且这些概率被认为是估计状态向量的函数。为了重建由位置，速度和导弹参数向量组成的状态向量，已经进行了三维三维蒙特卡罗模拟。修改后的IMMUF运行多个模型，每个模型都考虑了具有不同推力方向值的飞行阶段之一，并组合了估计以提供最终估计。噪声测量是从雷达获得的。结果表明，通过给出更准确的估计，具有可变MTM的拟议IMMUF优于传统形式的IMMUF。

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