Purpose

The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs.

Design/methodology/approach

In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model.

Findings

Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value.

Originality/value

This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

中文翻译：

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一种基于AIMM-UKF的小型无人机跟踪算法

目的

本研究的目的是建立一种基于交互多模型（IMM）的小型无人机（UAV）的有效跟踪算法，以对非法飞行的无人机及时采取对策。

设计/方法/方法

本文基于等速模型（CV）、机动自适应电流统计模型（CS）和角速度自适应三维（3D）定心恒速恒转向率模型，提出一种基于提出了自适应交互多重模型（AIMM-UKF）。此外，算法的每个模型都添加了一个自适应鲁棒滤波器。CV模型和CS模型附加线性卡尔曼滤波算法，CSCDR模型附加无迹卡尔曼滤波算法(UKF)，解决3D车削模型的非线性问题。

发现

Monte-Carlo仿真与其他两种IMM跟踪算法对比表明，在无人机运动模式和机动强度不同的情况下，AIMM-UKF算法在计算量和滤波精度之间做出了很好的权衡，可以跟踪更准确稳定，鲁棒性强。同时，经过对无人机实际观测数据的测试，结果表明AIMM-UKF算法状态估计轨迹在实际工程应用中可以看作是一条实际轨迹，具有很好的实用价值。

原创性/价值

本文提出了一种新的基于IMM的小型无人机跟踪算法，并通过实验证明了该算法与现有算法相比的优势和实用性。