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Passive Multi-sensor Maneuvering Target Tracking based on UKF-IMM Algorithm

机译:UKF-IMM算法的被动多传感器机动目标跟踪

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摘要

For effectively improving the accuracy of tracking a maneuvering target by passive sensors, a novel passive multi-sensor maneuvering target tracking algorithm based on unscented Kalman filter-interacting multiple model(UKF-IMM) is proposed. In this algorithm IKK is used by all models. UKF can avoid linearization of the highly nonlinear equations, and achieve accuracy at least to the second order. This algorithm use Markov process to describe switching probability among the models, while weighting means of inputs and outputs of UKF. Simulation results in passive maneuvering target tracking using three infrared sensors show that the proposed algorithm is more stable and effective.
机译:为了有效提高无源传感器对机动目标的跟踪精度,提出了一种基于无味卡尔曼滤波-交互多模型(UKF-IMM)的无源多传感器机动目标跟踪算法。在该算法中,所有模型均使用IKK。 UKF可以避免高度非线性方程的线性化,并且至少可以达到二阶精度。该算法使用马尔可夫过程描述模型之间的切换概率,同时对UKF的输入和输出进行加权。使用三个红外传感器进行被动机动目标跟踪的仿真结果表明,该算法更加稳定,有效。

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