Qiushi Chen

The Global Navigation Satellite System (GNSS) can be utilized for long-distance and high-precision time transmission. With the ongoing development of low Earth orbit (LEO) satellites and the rapidly changing geometric relationships between them, the convergence rate of ambiguity parameters in Precise Point Positioning (PPP) algorithms has increased, enabling fast and reliable time transfer. In this paper, GPS is used as an experimental case, the LEO satellite constellation is designed, and simulated LEO observation data are generated. Then, using the GPS observation data provided by IGS, a LEO-enhanced PPP model is established. The LEO-augmented PPP model is employed to facilitate faster and more reliable high-precision time transfer. The application of the LEO-augmented PPP model to time transfer is examined and discussed through experimental examples. These examples show multiple types of time transfer links, and the experimental outcomes are uniform. GPS + LEO is compared with exclusive GPS time transfer schemes. The clock offset of the time transfer link for the GPS + LEO scheme converges more swiftly, meaning that the time required for the clock offset to reach a stable level is the briefest. In this paper, standard deviation is employed to assess stability, and Allan deviation is utilized to assess frequency stability. The results show that the clock offset stability and frequency stability achieved by the GPS + LEO scheme are superior within the convergence time range. Controlled experiments with different numbers of satellites for LEO enhancement indicate that time transfer performance can be improved by increasing the number of satellites. As a result, augmenting GPS tracking data with LEO observations enhances the time transfer service compared to GPS alone.

Future wireless communications have been envisaged to benefit from integrating drones and free space optical (FSO) communications, which would provide links with line-of-sight propagation and large communication capacity. The theoretical performance analysis for a drone-assisted downlink FSO system is investigated. Furthermore, this paper utilizes the Málaga distribution to characterize the effect of atmospheric turbulence on the optical signal for the drone–terrestrial user link, taking into account atmospheric attenuation, pointing errors, and angle-of-arrival fluctuations. The probability density function and cumulative distribution function are then expressed in closed-form using the heterodyne detection and indirect modulation/direct detection techniques, respectively. Thereafter, the analytical expressions including the average bit error rate (BER) and the ergodic capacity are given. Particularly, the asymptotic behavior of the average BER of the considered system is presented using heterodyne detection at high optical power. The Monte Carlo simulation results certify the theoretical analytical results. Correspondingly, the field-of-view of the receiver is analyzed for optimal communication performance.

In high-frequency (HF) radar systems, transient interference is a common phenomenon that dramatically degrades the performance of target detection and remote sensing. Up until now, various suppression algorithms of transient interference have been proposed. They mainly concentrate on the skywave over-the-horizon radar on the basis of the assumption that the interference is sparse in a coherent processing interval (CPI). However, HF surface wave radar (HFSWR) often faces more complex transient interference due to various extreme types of weather, such as thunderstorm and typhoon, etc. The above algorithms usually suffer dramatic performance loss when transient interference contaminates the enormously continuous parts of a CPI. Especially for the compact HFSWR, which suffers from severe beam broadening and fewer array degrees of freedom. In order to solve the above problem, this study developed a two-dimensional interference suppression algorithm based on space-time cascaded processing. First, according to the spatial correlation of the compact array, the statistical samples of the main-lobe transient interference are estimated using a rotating spatial beam method. Next, an adaptive selection strategy is developed to obtain the optimal secondary samples based on information geometry distance. Finally, based on a quadratic constraint approximation, a precise estimation method of the optimal weight is developed when the interference covariance matrix is singular. The experimental results of simulation and measured data demonstrate that the proposed approach provides far superior suppression performance.

Combined sky‐wave and surface‐wave monostatic high‐frequency (CSSM‐HF) radar is a new type of mono‐static high frequency (HF) radar that uses multiple combined propagation modes of skywave and surface wave to detect targets. However, the detection range of different propagation modes partially overlaps, which results in multiple mode echoes from one target. To track the target in the presence of multi‐mode echoes, a new multi‐mode target tracking algorithm is proposed in the study, which uses the detection range as a priori knowledge. In CSSM‐HF radar, the possible propagation mode of the target echo is related to the position of the target itself. Therefore, the study uses the detection range simulation results as a priori knowledge to construct a propagation mode judgment module to effectively improve the calculation efficiency and performance of the tracking algorithm. The simulation results show that the proposed algorithm can effectively track the target in the presence of multi‐mode echoes and the tracking performance of the target with lower abscissa speed is better. In addition, the mode judgment module directly affects the performance of the algorithm. The tracking performance will be reduced when the output of the mode judgment module has an error mode.

High‐frequency surface wave radar (HFSWR) is an effective method for detecting and monitoring the targets on the ocean surface and above. To improve the detection performance of HFSWR, a new detector is proposed by applying weighted distance iteration (WDI) processing to the joint domain localised (JDL) matrix constant false alarm rate (CFAR) detector. Based on information geometry theory and Riemannian manifold, the proposed detector uses multi‐dimensional information (azimuth, Doppler velocity and range information) of the signal to effectively find the target that has drowned in sea clutter and uses WDI‐processing technique to suppress the clutter. The experimental results are given to show the superiority of the proposed detector. Compared with cell average CFAR detector and JDL matrix CFAR detector, the proposed detector improves the detection performance effectively.

A performance bound—Cramér-Rao lower bound (CRLB) for target estimation and detection in sparse stepped frequency radars is presented. The vector formulation of this CRLB is used to obtain a lower bound on the estimation error. The estimation performance can be transformed into different types of CRLB structures. Therefore, the expressions of bounds under three equivalent models are derived separately: time delay and Doppler stretch estimator, joint multiple parameter estimator, and sparse-based estimator. The variables to be estimated include the variances of unknown noise, range, velocity, and the real and imaginary parts of the amplitude. A general performance expression is proposed by considering the echo of the target in the line-of-sight. When the relationship between CRLB and various parameters are discussed in detail, the specific effect of waveform parameters on a single CRLB is compared and analyzed. Numerical simulations demonstrated that the resulting CRLB exhibits considerable theoretical and practical significance for the selection of optimal waveform parameters.

In order to adapt to the crowed High Frequency (HF) electromagnetic environment, improve the utilization rate of bandwidth and further realize the higher range resolution, the sparse random stepped frequency (SRSF) signal is introduced into HF radar. Signal model based on compressive sensing (CS) theory is put forward to reconstruct the range spectrum of targets. However, redundancy is generated due to external factors or unreasonable parameters in the process of estimating. Therefore, in this paper, key external factors affecting the range estimation performance are analyzed in detail. These factors mainly include mismatched distance redundancy, oversample redundancy, and also distortion from unknown velocities of moving targets. Specific elimination methods are also provided. Comparison between before and after elimination shows the effectiveness of the proposed method.

The range resolution is limited by insufficient continuous bandwidth in High Frequency Radar (HFR), due to the working frequency band (3–30 MHz) is heavily congested. In order to minimize the mutual interference from other users and improve bandwidth utilization, the Discontinuous Spectra Synthetic Bandwidth (DSSB) signal is proposed. For collecting all target information from echoes, multiple snapshots are needed to synthesize the range spectrum of multiple targets in the long-range observation window. The Multi-Snapshots Compressed Sensing-Information Extraction (MCS-IE) method is proposed to accurately estimate range and amplitude from multiple snapshots. For MCS-IE method, a new measurement matrix according to random frequencies of compressed sensing is designed for samples in every snapshot, and then information extraction method is used for the same range window with multiple samples and estimations. A series of comparison experiments based on measured data from HFR Frequency Monitoring System (FMS) are tested to verify proposed methods.