Accurate indoor radiation source localization is crucial for navigation, logistics, and electromagnetic security. Traditional methods struggle with multipath interference, environmental dynamics, and adaptability. To address these challenges, this paper proposes C2FGAN, a two-stage coarse-to-fine localization strategy based on Conditional GANs. The model learns electromagnetic wave propagation, enabling adaptive localization. A Fast Localization Algorithm integrates Optimal Dynamic Sampling and Iterative Location Estimation, leveraging equipotential attenuation surfaces and K-Dimensional Tree-based spatial density analysis for high-precision localization with minimal data. Experiments in diverse indoor environments show that C2FGAN achieves sub-meter accuracy, rapid convergence, and strong robustness, promising to provide an efficient and scalable solution for real-world applications.

In electromagnetic spectrum monitoring, Ultra-Wideband (UWB) signals, as a kind of low interception rate communication signal, are easy to bring great security risks. UWB signals are easy to be submerged in the background noise and difficult to detect. Therefore, this paper proposes an UWB signal detection and recovery algorithm based on power spectrum entropy and improved stochastic resonance for the above problems. Firstly, the power spectrum entropy algorithm was used to detect the existence of the signal. After confirming the existence of the signal, the adaptive piecewise stochastic resonance system was used to recover the detected signal waveform. On the basis of stochastic resonance processing, the Variational Mode Decomposition (VMD) algorithm combined with wavelet transform is used to further filter the signal noise and improve the recovery degree of the signal waveform. The simulation results show that the proposed algorithm can effectively recovery the UWB signal, and the recovery degree is better than the traditional bistable stochastic resonance model. The measured experiment verifies the effectiveness of the proposed algorithm.

Universal Serial Bus (USB) generates compromising emanations (CE) during data transmission, which is a weakness in the information security of information equipment. The radiated electromagnetic signal from the USB keyboard can be collected and processed to restore the key information. ‘USBee’, a malware, steals information from information equipment through the electromagnetic leakage signal generated by the data writing behavior of USB. This paper investigates the compromising emanations from USB to detect the behaviors of USB storage device including data reading, data writing and silence. We extract multiscale sample entropy (MSE) features and statistical features to quantify the characteristics of the radiated signal. The extracted features are fed into the k-Nearest-Neighbors (k-NN) classifier to achieve behaviors recognition. Finally, five USB flash disks and three computers are used to provide experimental examples for testing. The experimental results show that the proposed method can recognize the behaviors of USB storage devices effectively by analyzing the compromising emanations. Meanwhile, it is illustrated that the CE from USB contains the information of device individuals.

Compromising emanations (CE) is produced in-evitably by the electronic information equipment while processing and transmitting information. Malicious software and side-channel attack can achieve information interception and cause information leakage by enhancing the compromising radiation or intentionally generating controlled electromagnetic emanations. To detect and analyze compromising emanations is of great significance to maintain information security. In this paper, we present a method to detect and analyze the compromising electromagnetic emanations of USB storage devices. The method is composed of three parts: signal preprocessing, electromagnetic emanations detection and electromagnetic emanations analysis. Firstly, the collected signal is denoised by the empirical mode decomposition (EMD) algorithm. Then singular value decomposition (SVD) is utilized to process the denoised signal. And microframe parameter of the denoised signal is analyzed to determine whether the compromising radiation is from USB. Finally, the radiation signal from the USB device is analyzed by the correlation function to assess whether there is a potential threat about this device. Experiments are carried out in normal scenarios and practical attack scenarios with “USBee”, which shows that the proposed method is effective and has practical application value.

A well-done specific emitter identification (SEI) method via radio frequency (RF) fingerprint is a physical-layer-based approach for wireless communication network security. In this paper, we propose an efficient SEI method based on the spectrum trace skeleton and Bezier curve fitting. The method utilizes the original spectrum trace directly instead of collecting and processing time-domain data. The control points obtained by Bezier curve fitting with the spectrum trace skeleton are selected to act as the new features for identification. It can retain the information of the spectrum trace to the most extent while reducing feature dimension. It is proved that the proposed method has effective and reliable performance for identifying WiFi devices. The total identification accuracy can reach 98.4% even at a low signal-to-noise ratio (SNR). Furthermore, compared with time-domain methods, the proposed method has a lower feature dimension and saves running time and storage space.

Electromagnetic (EM) radiation is an inherent phenomenon in the operation of electronic information equipment. The side-channel attack, malicious hardware and software implantation attack by using the EM radiation are implemented to steal information. This form of attacks can be used in air-gap information equipment, which bring great danger for information security. The malicious implantation hidden in circuits are difficult to detect. How to detect the implantation is a challenging problem. In this paper, a malicious hardware implantation is analyzed. A method that leverages EM signals for Trojan-embedded computer video cable detection is proposed. The method neither needs activating the Trojan nor requires near-field probe approaching at close. It utilizes recognizable patterns in the spectrum of EM to predict potential risks. This paper focuses on the extraction of feature vectors via the empirical mode decomposition (EMD) algorithm. Intrinsic mode functions (IMFs) are analyzed and selected to be eigenvectors. Using a common classification technique, we can achieve both effective and reliable detection results.

This study attempts to characterize the electromagnetic compromising emanations from Laser Facsimile. Electromagnetic radiation is inevitable when electronic equipment works. The radiations can deteriorate the performance of a part of device itself or of another system. This phenomenon is a subject in the field of electromagnetic compatibility (EMC). But concerning people who process classified information, there is another threat that such electromagnetic radiation could be intercepted, deciphered and finally reveal the data being processed by the equipment. In this paper, electromagnetic side channel of laser facsimile and countermeasures are analyzed and discussed. It shows that the electromagnetic side channel of electronic information equipments is a real threat of information security.

As wireless platforms grow in popularity and store valuable information, their security becomes increasingly important. Specific emitter identification (SEI) is a novel means of enhancing the security of wireless networks. Thus, an automatic SEI system with good performance is meaningful. In this paper, a novel method for SEI based on singular spectrum analysis (SSA) is proposed. It uses SSA to analyze the transient signals and extract features for identification of mobile phones. A complete identification system is presented and its performance is evaluated by volume experiments, which show that the proposed method is efficient even at a reduced signal to noise ratio.

This letter presents a new method for localizing and identifying a target through walls with known wall parameters. A time-delay difference curve (TDDC) is utilized in this method for target localization and identification. The characteristic of the TDDC can be applied to estimate the incident angle and offset the influences in propagation path and propagation time delay brought from the wall. This letter analyzes the method and gives out the theoretical TDDC. Extensive simulations and experiments show the effectiveness of the proposed method.

Via analyzing the characteristics of life sign, a novel method is proposed to detect multiple trapped victims by ultra-wideband radar in this letter. The signal-to-noise and clutter ratio (SNCR) is greatly improved. The non-static clutter can be removed effectively. The number, range position and respiratory frequency of life signs can be detected automatically and accurately in low SNCR environment. By using experiment and numerical simulation, the good performance of the new method is proved.

The research on vital information extraction meets a lot challenges due to the narrow bandwidth and low signal-to-noise ratio of a vital sign in real environment. In this paper, a novel noncontract vital sign detection method based on multiple higher order cumulant is presented. According to the characteristic of vital sign for impulse ultrawideband radar, the quasi-periodic reflected echo in slow-time is analyzed. The novel method is theoretically deduced from fourth-order cumulant. It is proved to be better than the reference fast Fourier transform method by simulation and experiment. By using the new method, the range position and frequency information of life can be extracted accurately and automatically.

Via analyzing the characteristics of life sign, a novel method of life detection based on constant false alarm ratio and clustering is proposed in this paper. The signal to noise and clutter ratio (SNCR) is greatly improved. The nonstatic clutter can be removed effectively. The good performance of the new method is proved numerically and experimentally. The range location and frequency information of life sign can be estimated automatically and accurately in low SNCR environment.

This paper proposes a new algorithm to obtain the target shape imaging and track it synchronously using triangle localization method and the difference of time delay in the application of through wall imaging (TWI). The proposed algorithm based on a system model built in the near field can effectively estimate the velocity and track of moving target with the imaging result. Simulation results show that the effect of the wall can be removed visibly and the purpose of the imaging and tracking of moving target can be realized well.

The factors influencing on vital signal detection are discussed by introducing Cramer-Rao Low Bound (CRLB) for Ultra Wideband (UWB) in this paper. The CRLB is described on the basis of the impulse UWB sequential signal module and the Maximum Likelihood rule with Fisher Information Matrix (FIM). A novel detection method based on Multifold High Order Cumulant Fast Fourier Transform (MHOC-FFT) is used to process the simulated noisy signal and to estimate respiratory frequency. Simulated results verify the influence of the key parameters on vital detection. And the novel detection method has high Detectability with the capability to improve SNR and reduce higher harmonics.