In passive localization, the synthetic aperture positioning (SAP) method can achieve high precision and high-resolution positioning. However, existing research neglects the issue of target adaptability. For radar emitter targets, receivers can only periodically capture signals when the emitter’s beam scans toward the receiving antenna, resulting in spectral aliasing of the received signals. This leads to multiple false targets in localization images and reduced accuracy. This study employs the Fractional Fourier Transform (FrFT) integrated with compressed sensing for continuous signal reconstruction, aiming to eliminate spurious targets and enhance positioning accuracy. Initially, spectral aliasing is suppressed through FrFT, capitalizing on the approximately linear frequency-modulated (LFM) characteristics inherent in Doppler signals. Subsequently, continuous signal is reconstructed using compressed sensing with FrFT basis vectors forming the sensing matrix. Finally, the SAP method is implemented to achieve precise positioning. The effectiveness of the proposed method has been validated through simulations and Unmanned Aerial Vehicle (UAV) experiments, demonstrating that it significantly enhances the adaptability of SAP methods to radar emitter targets.

In passive localization, the synthetic aperture positioning (SAP) method enables high-precision positioning under low signal-to-noise ratio (SNR) conditions. However, higher-order phase errors induced by platform self-localization errors degrade image focusing and reduce localization accuracy. In this paper, a polynomial fitting approach based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares (IRLS) is applied to the unwrapped phase to eliminate higher-order error components. Additionally, a multiple sub-aperture phase stitching method is proposed to mitigate phase susceptibility to noise interference and error accumulation during phase unwrapping. The effectiveness of the proposed method is validated through both simulations and UAV experiments. Results demonstrate that meter-level localization accuracy can be achieved for the emitter target.

In emitter localization, the synthetic aperture positioning technique can achieve high-precision positioning even at a low signal-to-noise ratio (SNR). However, existing methods overlook the impact of receiver motion errors on the phase history of the received signal, leading to a reduction in localization accuracy. In this study, we propose a motion compensation (MoCo) technique for synthetic aperture passive localization using weather radar signals. Weather radar stations are chosen as reference stations due to their widespread coverage, high transmission power, and continuous signal transmission. The proposed method involves estimating and compensating for phase errors in the received signal, enabling phase coherency accumulation and achieving high-precision emitter localization. First, pulse compression is applied to the received weather radar signals to extract the phase information containing motion error details. Subsequently, we estimate motion errors using the extracted radar signal phase and apply phase compensation to the emitter target signal. Finally, the existing synthetic aperture positioning method is used to estimate the position of the target. Simulation results demonstrate that the method proposed in this paper offers superior accuracy in emitter localization compared to relying solely on real-time kinematic (RTK) for MoCo. The effectiveness of our proposed method is validated through actual unmanned aerial vehicle (UAV) experiments.

In emitter localization, the synthetic aperture positioning (SAP) technique can achieve high-precision positioning even at a low signal-to-noise ratio (SNR). However, existing methods require 2-D search implementation, which produces a huge amount of computation. In this study, the synthetic aperture map drift (MD) positioning method aided by an interferometer is proposed. This method reduces the amount of computation while achieving high-precision positioning. First, the exact azimuth position and approximate range position can be determined by a double-interference antenna via least-squares estimation. Doppler history is then used to correct range positioning error via an MD algorithm, which avoids the search of range and reduces the computational complexity. Simulation results show that the positioning accuracy of this method is close to the Cramer–Rao lower bound (CRLB). The effectiveness of the proposed method is verified through actual unmanned aerial vehicle (UAV) experiments.

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 has been an active research subject in electronic reconnaissance, target tracking, emergency response, and satellite interference source localization. Recently, researchers applied passive synthetic aperture in the emitter localization to improve the positioning accuracy. Passive synthetic aperture uses Doppler rate and zero Doppler point to target range and azimuth locations, respectively. In spaceborne model, effective satellite velocity is used in the range equation to fit the passive synthetic aperture model. Therefore, this study proposed a convex optimization approach to calculate the effective satellite velocity. We established an objective function by calculating the relationship among beam footprint velocity, satellite velocity, and effective satellite velocity. The optimal effective satellite velocity and range distance are obtained through iterating different range distances. Finally, the experimental results show that the positioning accuracy of proposed method is one order of magnitude higher than that of traditional FOA and FDOA method.

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.

Transform domain communication systems (TDCSs) are cognitive anti‐interference multi‐carrier communication systems with dynamic spectrum access. The inherent high peak‐to‐average power ratio (PAPR) of TDCS reduces the efficiency of the power amplifier. Adaptive waveform generation of the TDCS also causes the PAPR to vary according to the spectral conditions on hand. In this study, a complementary peak reducing signal (CPRS) method is proposed and analysed. It uses all unoccupied frequency bins to transmit data and uses all interfered frequency bins to generate CPRSs. Every data signal and its corresponding CPRS are orthogonal and complementary, so as to fully occupy all frequency bins. Therefore, the PAPR reduction of the composite signal with all frequency bins is considered. Once the optimal pseudo‐random phase sequence is determined, the sequence can adapt to all spectral conditions without side information, and the computational complexity for diverse spectral conditions is greatly reduced. Moreover, the orthogonality between the data signal and its CPRS in the frequency domain eliminates distortions and spectral spreading. As a component of the transmitting signal, CPRS may cause bit error rate (BER) loss. This study also proposes a signal power adjustment mechanism to achieve a compromise between PAPR reduction and BER loss.

Fast acquisition in space telemetry and telecontrol communication system is challenged significantly by the high dynamic and low signal‐to‐noise ratio. Increasing the dwell time to obtain better acquisition sensitivity, based on two‐dimensional time‐frequency search, is impossible because of resource limitation in receiver. To tackle these problems, this study proposes a two‐stage differential acquisition (TSDA) algorithm. The differential coherent integration is employed by the first stage of TDSA to extend integration time, during which keystone transform and fast Fourier transform are used to compensate the linear code phase drift and obtain high accumulation gain. The carrier‐frequency offset is estimated in terms of the code phase difference between the two parts of received signal divided by the second stage of TSDA in which the two‐dimensional searching process is avoided. The proposed TSDA algorithm and the traditional acquisition methods are compared under the same acquisition time and typical parameters of S‐band space telemetry and telecontrol communication system. Simulation results demonstrate that the proposed TSDA algorithm attains a higher acquisition probability and better acquisition sensitivity than the traditional acquisition methods.

This study exploits wireless channel response and fractional Fourier transform (FrFT) to achieve anti‐eavesdropping downlink transmission over a multipath channel. The proposed system works in time‐division duplexing mode, and only the transmitter knows the main channel response through uplink services. With the use of the main channel information, a time‐varying spread‐spectrum orthogonal frequency division multiplexing (TS‐OFDM) subsystem is proposed to realise confidential transform order (TO) transmission, and a security‐enhanced FrFT‐OFDM (SE‐FrFT‐OFDM) subsystem is presented for high‐rate data transmission. The optimal power allocation algorithms that optimise the average secrecy outage capacity of the TS‐OFDM subsystem and the bit error rate performance of the SE‐FrFT‐OFDM subsystem are also provided. Given channel reciprocity and spatial decorrelation, the legitimate user can achieve optimal anti‐multipath fading performance, but the intercepted data symbols will be randomised by both channel fading and TO deviations. To avoid randomisation, the eavesdropper has to test all possible TO combinations of multiple SE‐FrFT‐OFDM symbols, which is usually an impractical task. Compared to other typical methods, the transmitting power of the proposed system can be used more efficiently because the data symbol is not transmitted along with additional interference. Computer simulations are carried out to validate this scheme.

In the next generation Ka-band telemetry, track, and command (TT&C) system, the received TT&C signal will suffer from more than 800 kHz of Doppler shift. Even with code phase-Doppler parallel search algorithm based on FFT, the mean acquisition time (MAT) will be more than several seconds. By exploiting the Doppler tolerance of chirp noise waveform (CNW), the authors propose a novel CNW aided spread spectrum signal fast acquisition algorithm for large Doppler shifted TT&C system. Through this article, the authors first describe the ambiguity function (AF) of CNW, and then design a simplified quadrature modulation model, in which the data symbol and the CNW are transmitted simultaneously. At the receiving end, a fast acquisition method based on dual match filters (MFs) is proposed. Due to the delay-Doppler coupling feature, the receiver can achieve both code synchronization and Doppler shift estimation through only 1-Dimensional (1D) delay search, and then MAT can be reduced to a few milliseconds. Simulation results demonstrate the effectiveness of the proposed method.