Signalling for covert passive sensing
Abstract
In this work, we consider the optimality of signalling for covert sensing, where a legitimate receiver intends to estimate unknown variables based on the received signals from a transmitter, while ensuring that the probability of these signals being detected by a warden Willie is negligible. Specifically, we consider additive white Gaussian noise (AWGN) for both the estimation channel and detection channel, based on which we first reveal that Gaussian signalling is not optimal in terms of maximizing the estimation accuracy (e.g., maximizing the Fisher information) subject to a covertness constraint, e.g., guaranteeing a Kullback-Leibler divergence being no large than a specific value. To this end, we explicitly show that a skew normal distribution with an optimized skew parameter can achieve a higher estimation accuracy than a normal distribution subject to the same covertness constraint. Furthermore, we develop a framework based on calculus of variations and the Runge-Kutta method to identify the optimal signalling for covert sensing. As expected and explicitly shown in our numerical results, the identified optimal signalling outperforms both the normal and skew normal distributed signalling, which demonstrates the necessity of optimizing signalling in the context of covert sensing.
Document Type
Conference Proceeding
Date of Publication
1-1-2023
Volume
2023-May
Publication Title
IEEE International Conference on Communications
Publisher
IEEE
School
School of Science / ECU Security Research Institute
RAS ID
62736
Copyright
subscription content
First Page
3902
Last Page
3907
Comments
Zhang, Q., Yan, S., Shu, F., Cong, Y., & Ng, D. W. K. (2023). Signalling for covert passive sensing. In IEEE International Conference on Communications (pp. 3902-3907). IEEE. https://doi.org/10.1109/ICC45041.2023.10279567