In this paper, an analytical framework is presented for device detection in an impulse radio (IR) ultra-wide bandwidth (UWB) system and its performance analysis is carried out. The Neyman–Pearson (NP) criteria is employed for this device-free detection. Different from the frequency-based approaches, the proposed detection method utilizes time domain concepts. The characteristic function (CF) is utilized to measure the moments of the presence and absence of the device. Furthermore, this method is easily extendable to existing device-free and device-based techniques. This method can also be applied to different pulse-based UWB systems which use different modulation schemes compared to IR-UWB. In addition, the proposed method does not require training to measure or calibrate the system operating parameters. From the simulation results, it is observed that an optimal threshold can be chosen to improve the ROC for UWB system. It is shown that the probability of false alarm, $P_{FA}$, has an inverse relationship with the detection threshold and frame length. Particularly, to maintain $P_{FA}<{10}^{-5}$ for a frame length of 300 ns, it is required that the threshold should be greater than $2.2$. It is also shown that for a fix $P_{FA}$, the probability of detection $P_D$ increases with an increase in interference-to-noise ratio (INR). Furthermore, $P_D$ approaches 1 for INR $>-2$ dB even for a very low $P_{FA}$ i.e., $P_{FA}=1\times {10}^{-7}$. It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed $P_{FA}=0.1$ and $P_D=0.5$. Finally, the derived performance expressions are corroborated through simulation.

中文翻译：

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脉冲无线电超宽带系统中的无预警检测

本文提出了一种用于脉冲无线电（IR）超宽带（UWB）系统中设备检测的分析框架，并对其性能进行了分析。Neyman–Pearson（NP）标准用于此无设备检测。与基于频率的方法不同，所提出的检测方法利用时域概念。特征函数（CF）用于测量设备存在和不存在的时刻。此外，该方法可以容易地扩展到现有的无设备和基于设备的技术。该方法还可以应用于与IR-UWB相比使用不同调制方案的不同的基于脉冲的UWB系统。另外，所提出的方法不需要训练来测量或校准系统操作参数。根据仿真结果，可以观察到，可以选择最佳阈值来改善UWB系统的ROC。结果表明，误报的可能性，$P_{F\mathrm{一种}}$与检测阈值和帧长成反比关系。特别是要保持$P_{F\mathrm{一种}}<{10}^{-5}$ 对于300 ns的帧长，要求阈值应大于 $2.2$。还显示了修复$P_{F\mathrm{一种}}$，检测的可能性 $P_d$随干扰噪声比（INR）的增加而增加。此外，$P_d$ INR接近1 $>-\mathrm{2个}$ 即使是非常低的dB $P_{F\mathrm{一种}}$ IE， $P_{F\mathrm{一种}}=\mathrm{1个}\times {10}^{-7}$。还显示出，对于固定频率，干扰能量增加2倍会使INR改善3 dB。$P_{F\mathrm{一种}}=0.1$ 和 $P_d=0.5$。最后，通过仿真证实了导出的性能表达式。