Abstract
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, , has an inverse relationship with the detection threshold and frame length. Particularly, to maintain for a frame length of 300 ns, it is required that the threshold should be greater than . It is also shown that for a fix , the probability of detection increases with an increase in interference-to-noise ratio (INR). Furthermore, approaches 1 for INR dB even for a very low i.e., . It is also shown that a 2 times increase in the interference energy results in a 3 dB improvement in INR for a fixed and . Finally, the derived performance expressions are corroborated through simulation.
Highlights
COVID-19 has had a significant impact on our lifestyle [1]
We develop a novel device-free indoor detection method that can be applied to impulse radio (IR)-ultra-wide bandwidth (UWB) networks
N(0,0), n(0,1), · · ·, n( Ns −1,L−1), h respectively. s is the desired signal, i denotes the effect of the present devices, n is the additive white Gaussian noise (AWGN) added at the receiver, Ns denotes the repetition code length, L is the number of multipaths present in the system. ( j, l ) represents the signal of the j-th frame and l-th multipath
Summary
COVID-19 has had a significant impact on our lifestyle [1]. Physical distancing, wearing masks, avoiding crowds, and maintaining better hygiene are becoming the new norms [2,3]. For indoor environments, when trying to measure this distance, two different approaches have been proposed: wearable [6] and device-free [7]. UWB technology is capable of localizing within 10–30 cm depending on different environments, where the indoor industrial environment is the most challenging due to a large number of reflections By developing this UWB analytical framework, the device detection can be achieved with high accuracy followed by distance prediction, especially when operating in an LoS indoor environment. The development of such a framework at this moment is critical and will further lead to an accurate indoor localization system that is not present at the moment.
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