论文标题
使用神经网络扩展目标跟踪和分类
Extended Target Tracking and Classification Using Neural Networks
论文作者
论文摘要
扩展目标/对象跟踪（ETT）问题涉及跟踪对象，该对象可能会在单个传感器扫描下生成多个测量值。最先进的ETT算法可以在这些测量结果中有效利用可用信息，从而可以跟踪对象的动态行为并同时学习其形状。一旦形成对象的形状估计值，就可以通过高级任务（例如对象类型的分类）来使用它。在这项工作中，我们建议使用一个天真的深神经网络，该网络由一个输入，两个隐藏和一个输出层组成，以对其形状估计进行分类。与贝叶斯分类器进行模拟实验相比，提出的方法显示出卓越的性能。
Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT algorithms can efficiently exploit the available information in these measurements such that they can track the dynamic behaviour of objects and learn their shapes simultaneously. Once the shape estimate of an object is formed, it can naturally be utilized by high-level tasks such as classification of the object type. In this work, we propose to use a naively deep neural network, which consists of one input, two hidden and one output layers, to classify dynamic objects regarding their shape estimates. The proposed method shows superior performance in comparison to a Bayesian classifier for simulation experiments.
