In satellite-to-ground communication,during the process of P-band signal crossing the atmosphere to reach the ground array,its direction of arrival(DOA) and polarization angle are changed by ionosphere effects. Therefore,there is an error between the estimated values obtained by the existing DOA-polarization estimation algorithm and the real values. In order to solve the problem,corrections are made from the aspects of DOA and polarization angle. Firstly, considering the ionospheric refraction effect, a signal propagation model of ionosphere is established,based on which a correction method for pitch angle error is proposed. Secondly,regarding the Faraday rotation(FR) effect of ionosphere,the influence of Faraday rotation angle (FRA) on signal polarization is analyzed,and two estimation methods of FRA based on scattering matrix are proposed according to the process of ground target scattering. Simulation results demonstrate that the proposed methods can accurately calculate the correction value of pitch angle and FRA of P-band signal. Under certain conditions,the pitch angle estimation accuracy is improved by about 0.1°,and the FRA estimation accuracy is improved by nearly 1°,so the correction of DOA and polarization angle is realized.

To reduce the computations of parameters estimation in high-dynamic and long integration global navigation satellite system(GNSS) signal detection applications,the authors propose a low-computation GNSS acquisition method (LGAM) suitable for high-dynamic environment. The goal of LGAM is to apply the synthetic Doppler frequency hypothesis testing (SDHT) method to the acquisition of high dynamic GNSS signals with Doppler rate and bit flipping. Firstly,sparse Doppler frequency(SDF) process is implemented by coarse Doppler estimation,and post-correlation signal model is derived based on SDF structure. Then,in order to improve the detection efficiency of Doppler and Doppler rate, double-FFT based detection is proposed based on the post-correlation signal model for parameters estimation. The results demonstrate that in high dynamic environments, when the signal-to-noise ratio (SNR ) is higher than -43 dB, the computational complexity based on FFT method is 15 times that of LGAM1 and 780 times that of LGAM2.

To address the issue of synthetic aperture radar(SAR) images being susceptible to environmental noise,which leads to a reduction in the signal-to-noise ratio(SNR) at target locations,a method integrating singular value decomposition (SVD) and minimum entropy deconvolution (MED) to enhance the Back Projection(BP ) algorithm is proposed. Initially,the acquired echo signals undergo SVD, and a singular value matrix is obtained. By retaining only the first five singular values and reconstructing the echo signal matrix,an initial noise reduction is achieved. Subsequently,the signals are processed using MED filtering, where filter coefficients are iteratively updated to minimize the entropy of the signal,thereby reducing the kurtosis of the output signal and suppressing noise. A Zero-Phase Filter(ZPF) is then applied to restore any phase delays. Finally, the SAR image is generated using the BP algorithm. Experimental results demonstrate that this method significantly enhances the SNR at target locations,with improvements of 7.9 dB and 9.1 dB for large and small corner reflectors,respectively.

For the problem of target azimuth estimation under low signal-to-noise ratio (SNR) for active sonar in underwater environments,a direction of arrival(DOA) estimation multi-beamforming sonar imaging method based on fractional Fourier transform (FrFT )-enhanced iterative adaptive approach (IAA ) is proposed. Firstly, the echo signals received by hydrophones are subjected to FrFT preprocessing, transforming the wideband linear frequency modulation (LFM ) signals into narrowband signals in the fractional domain to avoid the influence of cross-interference terms. Then, focusing on LFM signals and suppressing noise in the FrFT domain is achieved. Finally,the iterative adaptive method is implemented in the FrFT domain,optimizing the power spectrum estimation method for accurate DOA estimation. Compared with traditional DOA estimation methods, the proposed method achieves better estimation accuracy and smaller root mean square error under low SNR conditions without increasing sensor array elements. It significantly improves imaging effectiveness, as indicated by simulation results showing sidelobe levels-13.364 dB for peak sidelobe ratio. in the range direction and -9.723 dB for integrated sidelobe ratio,-13.874 dB for peak sidelobe ratio in the azimuth direction and -10.034 dB for integrated sidelobe ratio.

For the problems of multi-person vital sign detection at the same distance and extraction of heartbeat signals under the interference of respiratory higher harmonics, an algorithm based on digital beamforming and orthogonal projection filter (OPF ) is proposed. After the radar measures multiple baseband signals, digital beamforming is first performed to obtain the vital sign signals of people by generating multiple beams pointing to the subjects in space. Then, using the OPF, the obtained vital sign signals are projected onto the null space of their respective respiratory higher harmonics to achieve the removal of respiratory higher harmonic interference and the real-time acquisition of heartbeat signals. The results of multi-person experiments show that the proposed algorithm can successfully separate the detection of multi-person vital sign signals and the extraction of heartbeat signals, and the accuracy rate of heartbeat frequency estimation is as high as 99.68%.

To solve the problem of signal aliasing after sampling in software defined radio (SDR ) , an improved phase-adjusted filtering algorithm is proposed. Based on the second-order RF bandpass sampling front-end with adjustable time delay,an anti-aliasing filter that supports multi-segment filtering is designed, and multiple frequency segments can be flexibly set according to the actual needs,so as to achieve accurate filtering for different frequency bands. Through the simulation verification in MATLAB SIMULINK, the method has better suppression performance than similar filters(38 dB or more) ,and can effectively filter out the required signal without affecting the integrity of other signals,which simplifies the receiving front-end,has strong flexibility and adaptability, and can better support future communication technology and high-density communication connections.

Vehicular edge computing(VEC) converges the computing resources of cloud servers to the edge of the network closer to the vehicle side, allowing vehicles to offload vehicular computing tasks to the network edge servers,thus providing vehicles with low latency,high bandwidth and high reliability services. However,the highly dynamic network topology,strict low-delay constraints,and massive data of vehicular tasks of VEC pose significant challenges for implementing efficient offloading. The digital twin(DT)-driven VEC model can enable real-time monitoring of the state of the VEC network,thus assisting in making sound offloading decisions in the physical world. Firstly, the research progress of edge computing, available vehicles and DT-driven VEC task offloading methods are introduced. Then,the scenario architecture of DT-driven task offloading for VEC is elaborated. Finally,the future research challenges and solutions of DT-driven VEC task offloading methods are discussed,in hope of providing certain guidance for solving the problem of DT-driven VEC task offloading.