Doppler positioning, as an early form of positioning, has regained significant research interest in the context of Low Earth Orbit (LEO) satellites.Given the LEO mega-constellation scenario, the objective function of Doppler positioning manifests significant nonlinearity, leading to ill-conditioning challenges for prevalent algorithms like iterative least squares (LS) estimation, especially in cases where inappropriate initial values are selected. In this study, we investigate the causes of ill-posed problems from two perspectives. Firstly, we analyze the linearization errors of the Doppler observation equations in relation to satellite orbital altitude and initial value errors, revealing instances where traditional algorithms may fail to converge. Secondly, from an optimization theory perspective, we demonstrate the occurrence of convergence to locally non-unique solutions for Doppler positioning. Subsequently, to address these ill-conditioning issues, we introduce Tikhonov regularization terms in the objective function to constrain algorithm divergence, with a fitted model for the regularization coefficient. Finally, we conduct comprehensive simulation experiments in both dynamic and static scenarios to validate the performance of the proposed algorithm. On the one hand, when the initial values are set to 0, our algorithm achieves high-precision positioning, whereas the iterative LS fails to converge. On the other hand, in certain simulation scenarios, the iterative LS converges to locally non-unique solutions, resulting in positioning errors exceeding 50 km in the north and east directions, several hundred kilometers in the vertical direction, and velocity errors surpassing 120 m/s. In contrast, our algorithm demonstrates typical errors of a position error of 6.8462 m, velocity error of 0.0137 m/s, and clock drift error of 8.3746 × ${10}^{-6}$ s/s. This work provides an effective solution to the sensitivity issue of initial points in Doppler positioning and can serve as a reference for the algorithm design of Doppler positioning receivers with LEO mega-constellations.

Aiming at the problem that the existing federated filtering algorithms are not sensitive to the abnormal measurement and poor tolerance, an improved adaptive federated filtering algorithm based on innovation is proposed, and when the navigation source is abnormal, it can automatically adjust the allocation factor. The experimental results show that the algorithm can effectively suppress the error when the navigation source is abnormal and improve the fault-tolerance of the multi-source information fusion navigation system. The implementation is simple, and the calculation is small and easy to implement.

Satellite communications are pivotal to global Internet access, connectivity, and the advancement of information warfare. Despite these importance, the open nature of satellite channels makes them vulnerable to eavesdropping, making the enhancement of interception resistance in satellite communications a critical issue in both academic and industrial circles. Within the realm of satellite communications, polarization modulation and quadrature techniques are essential for information transmission and interference suppression. To boost electromagnetic countermeasures in complex battlefield scenarios, this paper integrates multi-parameter weighted-type fractional Fourier transform (MP-WFRFT) with directional modulation (DM) algorithms, building upon polarization techniques. Initially, the operational mechanisms of the polarization-amplitude-phase modulation (PAPM), MP-WFRFT, and DM algorithms are elucidated. Secondly, it introduces a novel variable polarization-amplitude-phase modulation (VPAPM) scheme that integrates variable polarization with amplitude-phase modulation. Subsequently, leveraging the VPAPM modulation scheme, an exploration of the anti-interception capabilities of MP-WFRFT through parameter adjustment is presented. Rooted in an in-depth analysis of simulation data, the anti-scanning capabilities of MP-WFRFT are assessed in terms of scale vectors in the horizontal and vertical direction. Finally, exploiting the potential of the robust anti-scanning capabilities of MP-WFRFT and the directional property of antenna arrays in DM, the paper proposes a secure transmission technique employing directional variable polarization modulation with MP-WFRFT. The performance simulation analysis demonstrates that the integration of MP-WFRFT and DM significantly outperforms individual secure transmission methods, improving anti-interception performance by at least an order of magnitude at signal-to-noise ratios above 10 dB. Consequently, this approach exhibits considerable potential and engineering significance for its application within satellite communication systems.

This paper tackles the spatiotemporal calibration problem of multi-sensor fusion by providing a calibration approach that fuses the motion-based and feature-based calibration methods, which does not need a calibration plate. This method aims to solve the performance drop of data fusion caused by the time delay between different sensors and the inaccurate extrinsic parameters in practical use. Methodology is divided into two stages. In stage 1, we apply the sensor-motion consistency and B-spline-based Lie Algebra on trajectories to perform the fitting and the alignment. Rotation extrinsic parameters and temporal offsets are robustly estimated with non-linear optimization. The second stage is to use the results of the first phase as the initial value and extract the road boundaries with a high degree of availability as the features from the natural environment. A KDTree is built to facilitate effective feature point matching among different sensors, using a staged optimization method to decrease the cost function and precisely calculate the translation extrinsic parameters. The proposed 2-stage calibration method decreases dependence on a particular vehicle motion excitation and gives a dependable first estimate for matching of features, which guarantees the accuracy of calibration. Experiments based on the KITTI public dataset show that the proposed method can achieve sub-degree rotation accuracy (error < 0.5°) and centimeter-level translation accuracy (error < 2cm) in spatial calibration. Meanwhile, the error of temporal calibration is controlled to be a little more than 10 milliseconds. This high precision spatiotemporal alignment greatly improves the fusing of the point clouds and images. It validates that this method is accurate, robust and practical for complicated real-world scenario.