Using neural networks for localization of key fob within and surrounding a
car as a security feature for keyless entry is fast emerging. In this paper we
study: 1) the performance of pre-computed features of neural networks based UWB
(ultra wide band) localization classification forming the baseline of our
experiments. 2) Investigate the inherent robustness of various neural networks;
therefore, we include the study of robustness of the adversarial examples
without any adversarial training in this work. 3) Propose a multi-head
self-supervised neural network architecture which outperforms the baseline
neural networks without any adversarial training. The model's performance
improved by 67% at certain ranges of adversarial magnitude for fast gradient
sign method and 37% each for basic iterative method and projected gradient
descent method.

使用神经网络进行钥匙无线进入安全特性中钥匙卡的定位是一种快速发展的技术。我们研究了基于预计算功能的神经网络的定位分类性能，探究了各种神经网络的鲁棒性，并提出了一种优于基线神经网络的多头自监督神经网络架构。模型的性能在某些对抗性幅度范围内提高了 67%（对于快速梯度符号法），以及对于基本迭代法和投影梯度下降法分别提高了 37%。