Radio frequency fingerprint identification (RFFI) is a physical layer security methodology to recognize individual devices by leveraging hardware imperfections inevitably induced in the manufacturing process. However, the performance degradation caused by the time-varying channel impacts and interferences has severely restricted the development of RFFI. To this end, we present a channel-agnostic RFFI system, which consists of three modules, i.e., signal preprocessing module, feature extraction module, and classification module. In the signal preprocessing module, we first propose a novel approach, referred to as limiter-based spectral circular shift bidirectional division (LB-SCSBD), to generate two parallel spectral quotient (SQ) sequences. Then, we define the spectral quotient constellation (SQC) symbols according to different modulation formats, and thereby transform the SQ sequences into four magnitude-based sequences in terms of two channel-robust signal representations, i.e., the SQ magnitude (SQM) and SQC error vector magnitude (SQC-EVM). In the feature extraction module, we present a moment-based statistical feature extractor (MB-SFE) to extract the device-specific information from the above four sequences. In the classification module, the extracted statistics are fed into the multi-class support vector machine (SVM) for training and testing. We take WiFi as a case study and evaluate the performance of the proposed RFFI system by classifying eight simulated device models and six universal software radio peripheral (USRP) transmitter radios. Experimental results show that (i) the proposed method achieves the accuracies of 99.84% and 98.26% with eight devices in QPSK and 16QAM cases, as well as the accuracy of 92.42% with six USRP devices (ii) the proposed method exhibits superior classification performance in comparison to some existing RFFI methods, leading to a significant accuracy improvement of at least 38.33%.

Emitter localization is an active research topic in electronic reconnaissance. Recently, passive synthetic aperture (PSA) is used to determine the azimuth and range distance of emitter. However, the existing methods approximate the distance equation of the spaceborne model through the airborne model, resulting in a large positioning error. In this paper, an emitter localization algorithm based on PSA in spaceborne model is proposed. First, the phase of the received signal is accumulated by using the symmetry of the Doppler history of the received signal in long synthetic aperture time, improving the azimuth positioning accuracy. The effective velocity is then used to establish the nonlinear equations of range distance. A modulus function is used to solve the equations, correcting the range positioning error in spaceborne model. Finally, the influence of noise, satellite position error, and satellite speed error on the positioning results is analyzed. The error matrix of proposed method in wide swath is obtained. Simulation results show that the positioning accuracy of this method is one order of magnitude higher than that of traditional single satellite positioning methods. The effectiveness of the proposed method is verified through actual satellite experiments.

Passive localization is important because of its strong concealment and long detection distance. Space-borne passive radiation source location technologies have contradicting characteristics of wide coverage and high-precision position. Hence, accurate target identification is difficult to realize. A novel measurement that utilizes passive synthetic aperture is presented in this letter to measure the time of the satellite’s passing zenith precisely. In the beam coverage duration, the radiation carrier Doppler component is extracted and accumulated to synthesize an equivalent large virtual satellite antenna azimuth aperture. Taking advantage of the linear Doppler rate, a local matched filter is designed to implement a 2-D search. Doppler rate and zero Doppler point correspond to the target range and azimuth locations, respectively. The corresponding subastral point position radiation source can then be determined. Practical experiment results from an unmanned aerial vehicle platform demonstrated the effectiveness, wide coverage, and high-precision meter scale of the proposed method. Satellite data were also utilized to verify the feasibility of the system.

Respiration rate is an essential indicator of vital signs, which can demonstrate the physiological condition of the human body and provide clues to some diseases. Commercial Wi-Fi devices can provide a non-invasive, cost-effective and long-term respiration rate-monitoring scheme for home scenarios. However, previous studies show that the breathing depth and location may affect the detectability of respiratory signals. In this study, we leverage the variation of the Doppler spectral energy extracted from the channel state information (CSI) collected by Wi-Fi devices to track the chest displacement induced by respiration. First, the random phase is eliminated by phase-fitting method to obtain the complex CSI containing the Doppler shift. Then, the multipath decomposition of CSI is carried out to obtain the channel impulse response, which eliminates the interference phase of the time delay and retains the Doppler shift. The dynamic path units are also separate from the multipath, which overcomes the indoor multipath effect. Finally, we conduct a time–frequency analysis to dynamic units to accumulate Doppler spectral energy. Based on these ideas, we design a complete respiration rate-monitoring system to obtain the respiration rate by using the consistency between the Doppler energy change period and the respiratory cycle. We evaluate our system through extensive experiments in several typical home environments filled with multipath. Experimental results show that the errors of the three scenarios are approximate, the maximum error is less than 0.7 bpm, and the average errors are approximately 0.15 bpm. This result indicates that our scheme can achieve high precision respiration monitoring and has good anti-multipath ability compared with existing methods.

The utilization of Global Navigation Satellite System (GNSS) is becoming an attractive navigation approach for geostationary orbit (GEO) satellites. A high-sensitivity receiver compatible with Global Position System (GPS) developed by the United States and BeiDou Navigation Satellite System (BDS) developed by China has been used in a GEO satellite named TJS-5 to demonstrate feasibility of real-time navigation. According to inflight data, the GNSS signal characteristics including availability, position dilution of precision (PDOP), carrier-to-noise ratio (C/N₀), observations quantity and accuracy are analyzed. The mean number of GPS and GPS + BDS satellites tracked are 7.4 and 11.7 and the mean PDOP of GPS and GPS + BDS are 10.24 and 3.91, respectively. The use of BDS significantly increases the number of available navigation satellites and improves the PDOP. The number of observations with respect to C/N₀ is illustrated in detail. The standard deviation of the pseudorange noises are less than 4 m, and the corresponding carrier phase noises are mostly less than 8 mm. We present the navigation performance using only GPS observations and GPS + BDS observations combination at different weights through comparisons with the precision reference orbits. When GPS combined with BDS observations, the root mean square (RMS) of the single-epoch least square position accuracy can improve from 32.1 m to 16.5 m and the corresponding velocity accuracy can improve from 0.238 m/s to 0.165 m/s. The RMS of real-time orbit determination position accuracy is 5.55 m and the corresponding velocity accuracy is 0.697 mm/s when using GPS and BDS combinations. Especially, the position accuracy in x-axis direction reduced from 7.24 m to 4.09 m when combined GPS with BDS observations.