Purpose

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown parameters; a more general nonlinear dynamical model for each UAV is considered to include two terms. Due to an unknown parameter corresponding to the normal or abnormal state for each UAV, the bias-compensated approach is proposed to obtain the unbiased parameter estimation. Meanwhile, the biased error and accuracy analysis are also given in case of strict statistical description of the uncertainty or white noise. To relax this strict statistical description on external noise, an analytic center approach is proposed to identify the unknown parameters in presence of bounded noise, such that two inner and outer ellipsoidal approximations are constructed to include the membership set. To be precise, this paper is regarded as one extension and summary for the author’s previous research on the anomaly detection in multi-UAV formation. Finally, one simulation example is given to confirm the theoretical results.

Design/methodology/approach

Firstly, one extended nonlinear relation is constructed to embody the mutual relationship of UAVs. Secondly, to obtain the unbiased parameter estimations, the bias-compensated approach is applied to achieve it under the condition of white noise. Thirdly, in case of unknown but bounded noise, an analytic center approach is proposed to deal with this special case. Without loss of generality, the author thinks this paper can be used as one extension and summary for research on multi-UAVs formation anomaly detection.

Findings

An anomaly detection problem in multi-UAVs formation can be transformed into a problem of nonlinear system identification, and in modeling the nonlinear dynamical model for each UAV, two terms are considered simultaneously to embody the mutual relationships with other nearest UAV.

Originality/value

To the best knowledge of the authors, this problem of the anomaly detection problem in multi-UAVs formation is proposed by the authors’ previous work, and the problem of multi-UAVs formation anomaly detection can be transferred into one problem of parameter identification. In case of unknown but bounded noise, an analytic center approach is proposed to identify the unknown parameters, which correspond to achieve the goal of the anomaly detection.

中文翻译：

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多种无人机形态异常检测的综合分析

目的

本文的目的是要解决多无人机飞行器编队中的异常检测问题，可以将其转换以识别一些未知参数。每个无人机的更通用的非线性动力学模型被认为包括两个项。由于对应于每个无人机的正常或异常状态的未知参数，提出了一种偏置补偿方法来获得无偏置参数估计。同时，在对不确定性或白噪声进行严格的统计描述的情况下，还会给出偏差误差和精度分析。为了放宽对外部噪声的严格统计描述，提出了一种分析中心方法来识别存在边界噪声的未知参数，这样就构造了两个内部和外部椭圆近似，以包括隶属集。确切地说，本文被认为是作者先前关于多UAV形成中异常检测的研究的延伸和总结。最后，给出了一个仿真实例来验证理论结果。

设计/方法/方法

首先，构造一个扩展的非线性关系来体现无人机的相互关系。其次，为了获得无偏参数估计，在白噪声条件下，采用偏补偿方法来实现。第三，在未知但有界噪声的情况下，提出了一种解析中心方法来处理这种特殊情况。在不失一般性的前提下，作者认为，本文可以作为多UAV形成异常检测研究的一个扩展和总结。

发现

多UAV形成中的异常检测问题可以转化为非线性系统识别的问题，并且在为每个UAV建模非线性动力学模型时，要同时考虑两个项以体现与其他最近的UAV的相互关系。

创意/价值

据作者所知，多UAV形成异常检测问题是作者先前的工作提出的，多UAV形成异常检测问题可以转化为参数识别问题。在未知但有界噪声的情况下，提出了一种解析中心方法来识别未知参数，这些参数对应于实现异常检测的目的。