With the large-scale deployment of low Earth orbit (LEO) mega-constellations, navigation augmentation services provided by LEO satellites have become key to improving the accuracy and reliability of global navigation satellite systems (GNSSs). Multicomposite code shift keying (MCSK) signals combine high-data-rate broadcasting and high-precision ranging, exhibiting unique advantages in navigation augmentation. However, whether the signal characteristics verified through ground testing remain consistent after launch and operation in a real-space environment is a critical issue that directly affects the effectiveness of ground verification and the efficiency of forming operational capability. In this paper, we propose a comprehensive evaluation framework to assess the consistency of MCSK signal characteristics between ground and in-orbit conditions. A multidimensional performance indicator system covering time-domain, frequency-domain, correlation-domain, measurement-domain and MCSK-specific structural features is established. Ground test data and in-orbit measurement data are jointly analysed using Pearson correlation, Spearman rank correlation and mutual information methods to construct a hierarchical consistency assessment framework that captures linear, monotonic and generalised statistical dependencies. Extensive experimental validation is conducted on the basis of ground precision testing and in-orbit high-gain antenna monitoring data from a representative LEO navigation augmentation constellation. The results show diverse consistency patterns ranging from strong linear correlations (e.g., digital distortion and operating bandwidth) to weak or complex statistical dependencies (e.g., partial S-curve biases), reflecting different sensitivities of physical mechanisms to space-ground environmental differences. The proposed methodology and findings provide strong support for optimising tests, predicting performance and formulating standards for LEO navigation augmentation payloads.
Airborne circular synthetic aperture radar (CSAR) enables multiangle observation and high-resolution imaging, which can extract the elevation information. However, the traditional elevation extraction methods are limited by the low elevation extraction accuracy and complicated processing flow. Thus, an elevation information extraction scheme based on the sub-aperture correlation using the airborne CSAR imaging is proposed. First, the full-aperture echo data are divided into multiple sub-aperture datasets, and then the back-projection (BP) is used for the sub-aperture imaging to generate high-precision sub-aperture images. Moreover, Otsu-based adaptive binarisation method is adapted to binarise sub-aperture images and extract the centroid position of sub-aperture images, and then the chain correlation calculation is implemented for the centroid position registration of sub-aperture images. Besides, the geometric relation between the imaging position offsets of targets in different sub-aperture images are utilised to calculate the line-of-sight direction offsets of targets at different viewing angles, and then the target elevation information is derived by solving corresponding relational equations. Finally, the elevation information obtained from all registered sub-aperture images is fused to generate the more accurate and complete elevation information. Experimental results based on the simulated and measured CSAR data verify the correctness and effectiveness of the proposed elevation information extraction scheme.
Deep-sea operations impose demanding requirements on the flexibility and autonomy of underwater vehicles. Unlike traditional torpedo-shaped AUVs, the disk-shaped, multi-thruster Autonomous Underwater Vehicle (AUV) demonstrates enhanced agility, which is ideal for navigating uncharted and complex underwater terrains. This paper proposes a dedicated obstacle avoidance strategy tailored to the multi-thruster AUV's exceptional horizontal manoeuverability and fixed-point multi-directional distance measurement capability. Upon obstacle detection, the vehicle adjusts the thrust difference of its horizontal thrusters to achieve in-place rotation across different headings, enabling continuous collection of obstacle distance data via single-beam acoustic ranging. The collected data is then input into an optimised Vector Field Histogram (VFH) algorithm to generate the optimal real-time heading for obstacle avoidance. This integration of fixed-point multi-directional sensing and algorithm-adaptive decision-making enhances the vehicle's obstacle avoidance robustness and adaptability. Comprehensive simulations are conducted in MATLAB and on the MOOS-IvP autonomous marine vehicle platform under diverse uncharted underwater environments to validate the strategy. Simulation results confirm the feasibility and reliability of the proposed obstacle avoidance strategy and algorithm.
Line spectra radiated by moving targets are important features for passive sonar detection and classification. Relative motion between a target and the receiver induces time-varying Doppler shifts in the received line spectra, causing phase misalignment during long-time Fourier integration and resulting in spectral broadening, energy dispersion and coherent integration loss. To address this problem, this paper proposes a matched frequency-trajectory estimation method for long-time coherent integration enhancement (MFT-LTCIE). The method transforms the received signal into its analytic representation, extracts the narrowband component around the target line spectrum and partitions the signal into short-time segments. For each segment, matching responses are computed over a candidate frequency grid, and a maximum-matching criterion is used to estimate the segment-wise frequency trajectory. The estimated trajectory is interpolated onto the sample-time axis and used to construct a phase-compensation function relative to a reference frequency. Trajectory-guided phase compensation restores phase coherence, and a full-duration FFT of the compensated signal yields an enhanced coherent integration spectrum. Simulations show that MFT-LTCIE suppresses Doppler-induced spectral broadening and maintains sharp spectral peaks under different signal-to-noise ratios and target speeds. Experimental ship-radiated noise data further demonstrate its ability to track line-spectrum variations and refocus dispersed energy.
The statistical characterisation of a target's radar cross section (RCS) is notoriously challenging due to its complex, angle-dependent nature, often manifesting as multimodal and heavy-tailed distributions contaminated by specular glints. Conventional statistical models, including Gamma, Weibull, Log-normal distribution (LND) and mixed LND (MLND) models, are often inadequate as they lack robustness to outliers and are based on a physically inconsistent assumption of static mixture weights. To address these deficiencies, this paper introduces a physics-informed dynamic Student's t-mixture model (PI-DS-tMM). Our framework makes a dual contribution: it employs the heavy-tailed Student's t-distribution for inherent robustness against outliers, and, critically, it replaces static weights with a dynamic, physics-informed weighting function that explicitly models the angle-dependent activation of scattering mechanisms. Extensive validation on both simulated and measured RCS data from diverse aerial targets demonstrates that the PI-DS-tMM achieves a superior goodness-of-fit over conventional models, as quantified by the Kolmogorov-Smirnov test. The proposed model provides a more accurate and physically consistent representation of RCS fluctuations, offering a principled framework poised to enhance the performance of advanced radar applications such as target detection, tracking and recognition.
