Purpose

The purpose of this study is to address the limitations of existing target group distribution pattern analysis methods and identify subtle distribution differences within and between the groups with no pre-specified distribution features. Classical work generally concentrates on either the group distribution tendency or shape as a whole and simply ignores the subtle distribution differences within the group. Other work is constrained to pre-defined spatial distribution features.

Design/methodology/approach

This study proposes a novel algorithm for target group distribution pattern analysis. This study first transforms the group distribution data with uncertain measurements into a distributional image. Upon that, a bagged convolutional neural network model is constructed to discriminate the delicate group distribution patterns.

Findings

Experimental results indicate that our method is robust to target missing and location variance and scalable with dataset size. Our method has outperformed the benchmark machine learning methods significantly in pattern identification accuracy.

Originality/value

Our method is applicable for complex unmanned aerial vehicle distribution pattern identification.

中文翻译：

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用于无人机分布模式识别的袋装卷积神经网络目标群体分布模式分析

目的

本研究的目的是解决现有目标群体分布模式分析方法的局限性，并确定没有预先指定分布特征的群体内部和群体之间的细微分布差异。经典著作一般只关注群体分布趋势或整体形态，而忽略群体内部细微的分布差异。其他工作仅限于预定义的空间分布特征。

设计/方法/方法

本研究提出了一种新的目标群体分布模式分析算法。本研究首先将具有不确定测量的群分布数据转换为分布图像。在此基础上，构建了一个袋装卷积神经网络模型来区分精细的组分布模式。

发现

实验结果表明，我们的方法对目标缺失和位置差异具有鲁棒性，并且可随数据集大小扩展。我们的方法在模式识别精度方面明显优于基准机器学习方法。

原创性/价值

我们的方法适用于复杂的无人机分布模式识别。