Accurate marine wind detection under all-weather conditions is crucial for maritime activities. The joint detection of lidar and radar is supposed to be a potential way to carry out the all-weather sensing of wind. However, their performance analysis has not been well studied, particularly in the far sea area, where the wind-tracing particles are quite different from those inland. Based on the particle distributions above the sea surface under different weather conditions, this study investigated the scattering and attenuation effects of lidar and radar waves in open sea areas with the Mie theory and T-matrix method. Then, the maximum detection range and velocity accuracies of lidar/radar were comprehensively analyzed based on detection principles to optimize the combination of lidar and radar. According to the simulation results, it was difficult to maintain the detection capability of a single lidar/radar under all-weather conditions, and 1.55 $\mathsf{\mu }$m lidar and W-band radar presented a promising joint detection scheme, as they exhibited optimal weather adaptability in clear sky and precipitation conditions, respectively.

The generalized Radon–Fourier transform (GRFT) is a well-established coherent accumulation technique for high-speed and high-mobility target detection. However, this method tends to suffer from the difficulty of identifying the main lobe from multiple blind speed sidelobes (BSSLs) and the computational complexity is generally high. To address these challenges, we propose a new method, namely the BSSL Traction Particle Swarm Optimization (BTPSO), to robustly and accurately extract the main lobe. In the method, the relationship between the main lobe and the BSSLs is used to attract particles to potential positions of the main lobe in the group when trapped in local optimal, and a new termination criterion in which multiple particles should converge to the same optimal value is proposed to avoid local convergence. Simulation examples show that the proposed method can improve the probability of converging to the main lobe peak while reducing cost time, and its good adaptability to low signal-to-noise ratio (SNR) cases is well verified.

Typically, a monostatic radar can only measure the Doppler velocity, which is the projection of full velocity on the LOS (line of sight). To enable measurement of the tangential wind velocity, this paper proposes an interferometric detection method (IDM) based on spaced phase centers of digital phased array radar, which can measure the radial and tangential Doppler velocities simultaneṇously over wide angular ranges. The masscontinuity constraint is further employed to adjust the measured tangential velocity in a closed-form, and no iterative optimization is required. Moreover, since the measurement and adjustment are all computationally inexpensive, the proposed method can be operated in real-time. Simulation experiments for representative scenarios verify the higher temporal resolution and better performance of the proposed method in comparison with traditional methods.

Variational methods are widely used in the meteorological community to retrieve 3D-wind from single LiDAR observations, but the weights for loss function components are static and determined empirically, and iteratively minimizing the loss function is computationally expensive. In this paper, we propose a hybrid method, the Model Function-based Multi-Regularization parameter selection with Adaptive Weights (MFMR-AW) for the background field components to overcome these challenges. MFMR-AW employs the multi-parameter regularization method to combine the Lidar observations with the background field. The combined effect of MFMR and AW determines the weights of background field components. Then the variational principle is used to transform the function minimization process into a system of linear equations, which can be well handled with the truncated singular value decomposition method. Simulations and field observations are employed to validate the proposed method. Results show that the mean RMSEs of MFMR-AW can be reduced by up to 66.9% compared to traditional assumption-based background fields, and the proposed method can get comparable or even superior accuracy with only about 1% of the computation cost of the traditional variational method.

Coherent Doppler wind lidar serves as an essential technological approach for remote sensing of atmospheric wind fields. However, conventional single-frequency wind lidar (SFWL) architectures are severely constrained by atmospheric turbulence-induced phase perturbations, which cause rapid degradation of inter-pulse coherence. This study presents a systematic theoretical investigation of the physical mechanisms underlying atmospheric turbulence suppression of dual-frequency wind lidar (DFWL) systems, which can maintain inter-pulse coherence and extends effective coherent time from the milliseconds to seconds, due to good accumulated gain of inter-pulse coherent processing. Simulation results well verifies that DFWL can not only enhance SNR and extend detection range, but also preserve high range resolution and velocity estimation accuracy.