Ramirez-Paredes Juan-Pablo, Doucette Emily A, Curtis Jess W, Ayala-Ramirez Victor
Department of Electronics Engineering, University of Guanajuato, Salamanca, Gto. 36885, Mexico.
Munitions Directorate, Air Force Research Laboratory, Eglin AFB, 32542, FL, USA.
Sensors (Basel). 2018 Feb 21;18(2):638. doi: 10.3390/s18020638.
Tracking multiple targets using a single estimator is a problem that is commonly approached within a trusted framework. There are many weaknesses that an adversary can exploit if it gains control over the sensors. Because the number of targets that the estimator has to track is not known with anticipation, an adversary could cause a loss of information or a degradation in the tracking precision. Other concerns include the introduction of false targets, which would result in a waste of computational and material resources, depending on the application. In this work, we study the problem of detecting compromised or faulty sensors in a multiple-target tracker, starting with the single-sensor case and then considering the multiple-sensor scenario. We propose an algorithm to detect a variety of attacks in the multiple-sensor case, via the application of finite set statistics (FISST), one-class classifiers and hypothesis testing using nonparametric techniques.
使用单个估计器跟踪多个目标是一个通常在可信框架内解决的问题。如果对手控制了传感器，就会有许多弱点可供利用。由于估计器需要跟踪的目标数量无法预先得知，对手可能会导致信息丢失或跟踪精度下降。其他问题包括引入虚假目标，这将根据应用情况导致计算和物质资源的浪费。在这项工作中，我们研究了在多目标跟踪器中检测受损或故障传感器的问题，首先从单传感器情况开始，然后考虑多传感器场景。我们提出了一种算法，通过应用有限集统计（FISST）、单类分类器和使用非参数技术的假设检验来检测多传感器情况下的各种攻击。
