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High manoeuvre target tracking in coordinated turns

机译:协调转弯中的高机动目标跟踪

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摘要

In target tracking, most tracking algorithms are model based, and serious errors can arise when the models fail to fit the physical target motions, especially during coordinated turns. To address this problem, a two-step tracking algorithm is proposed. First, considering the diversity of the target manoeuvres, five elemental Singer models with carefully designed parameter interact with each other in an interacting multiple model (IMM) algorithm to accomplish the tracking and estimate the target's kinematic parameters. Second, if a turn motion occurs, then it discriminates the turn motion into a horizontal turn or a three-dimensional (3D) turn; and the real-time turn rate is calculated to refine the corresponding model for tracking. Since the filtering stage is important in estimating the kinematic parameters, sparse-grid quadrature Kalman filter is employed to improve the filtering capability. The proposed algorithm is exemplified by simulation tests and data tests in 3D, and is compared with that of an IMM algorithm utilising constant velocity, constant acceleration and 3D coordinated turn models. All of the test results demonstrate that the proposed algorithm is effective and has higher accuracy.
机译:在目标跟踪中,大多数跟踪算法都是基于模型的,当模型无法适应物理目标运动时,尤其是在协调转弯时,可能会出现严重错误。为了解决这个问题,提出了一种两步跟踪算法。首先,考虑到目标动作的多样性,五个具有精心设计参数的基本Singer模型在交互多模型(IMM)算法中相互交互,以完成跟踪和估算目标的运动学参数。第二,如果发生转弯运动,则将转弯运动区分为水平转弯或三维(3D)转弯;并计算实时转弯速率以完善相应的跟踪模型。由于滤波阶段在估计运动学参数方面很重要,因此采用稀疏网格正交卡尔曼滤波器来提高滤波能力。该算法以3D仿真测试和数据测试为例,并与采用恒定速度,恒定加速度和3D协调转弯模型的IMM算法进行了比较。所有测试结果表明,该算法是有效的,具有较高的精度。

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