It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model (MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter, the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect (TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.

中文翻译：

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基于多模型高效粒子滤波器的跟踪前检测机动弱目标

联合检测和跟踪一个弱目标是一个棘手的问题，当目标处于机动时就变得更具挑战性。上述问题是通过使用贝叶斯理论制定的，并提出了一种基于多模型（MM）的滤波器。所呈现的滤波器使用 MM 方法，通过将表示运动模型的随机变量添加到目标状态，来适应机动目标可能经过的多种运动。为了增强滤波器的效率性能，将目标存在变量与目标状态分离，并以更有效的方式计算存在概率。为了检查基于 MM 的方法的性能，使用具有机动目标的典型跟踪前检测 (TBD) 场景进行模拟。