The electromagnetic (EM) vortex imaging has been found to have a great potential application prospect in the imaging radar field. However, current studies focus on the motionless target, which seriously limits its application in practice. Therefore, to achieve EM vortex imaging for the motion target, this study proposes a parametric sparse representation model for EM vortex imaging that takes into account a translational motion target and uses the stepped frequency signal. An iterative algorithm is developed based on the sparse Bayesian learning (SBL) algorithm to estimate the velocity, and accomplish the EM vortex imaging exploiting SBL algorithm. Simulation results demonstrate that the proposed algorithm can improve velocity estimate accuracy in terms of relative error and achieve EM vortex imaging for the motion target.

Visual navigation technology holds significant potential for applications involving unmanned aerial vehicles (UAVs). However, the inherent spectral limitations of optical-dependent navigation systems prove particularly inadequate for high-altitude long-endurance (HALE) UAV operations, as they are fundamentally constrained in maintaining reliable environment perception under conditions of fluctuating illumination and persistent cloud cover. To address this challenge, this paper introduces microwave vision to assist optical vision for environmental measurement and proposes a novel microwave vision-enhanced environmental perception method. In particular, the richness of perceived environmental information can be enhanced by SAR and optical image fusion processing in the case of sufficient light and clear weather. In order to simultaneously mitigate inherent SAR speckle noise and address existing fusion algorithms’ inadequate consideration of UAV navigation-specific environmental perception requirements, this paper designs a SAR Target-Augmented Fusion (STAF) algorithm based on the target detection of SAR images. On the basis of image preprocessing, this algorithm utilizes constant false alarm rate (CFAR) detection along with morphological operations to extract critical target information from SAR images. Subsequently, the intensity–hue–saturation (IHS) transform is employed to integrate this extracted information into the optical image. The experimental results show that the proposed microwave vision-enhanced environmental perception method effectively utilizes microwave vision to shape target information perception in the electromagnetic spectrum and enhance the information content of environmental measurement results. The unique information extracted by the STAF algorithm from SAR images can effectively enhance the optical images while retaining their main attributes. This method can effectively enhance the environmental measurement robustness and information acquisition ability of the visual navigation system.

Bistatic radar imaging can overcome limitations of monostatic radar imaging and obtain abundant target feature information; thus, it is followed with interest. Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique. In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance. Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed. Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image. Simulation results demonstrate the validity and the superiority of the proposed method.

Synthetic aperture radar (SAR) can acquire navigation data to correct inertial navigation system (INS) errors even under global navigation satellite system (GNSS)-denied conditions. However, when unmanned aerial vehicles (UAVs) may deactivate the SAR system to maintain radio silence, or the SAR sensor may be subjected to transient interference, the INS/SAR integrated navigation system transitions to degraded scenarios without SAR navigation data. Furthermore, the irregular sampling characteristics of SAR navigation data pose significant challenges to the localization performance of the INS/SAR integrated navigation system. In order to address the above challenges faced by UAVs, we propose a liquid time-constant (LTC) network-enhanced INS/SAR integrated localization method. The method adopts a loosely coupled integration strategy with training and prediction modes. During training, an LTC-assisted localization prediction network (LTC-ALPN) is designed to model input–output relationships using prior flight data while explicitly accounting for the non-uniform temporal sampling characteristics of SAR measurements. In prediction mode, the trained LTC-ALPN forecasts missing SAR navigation information, which is subsequently fused with INS outputs via a Kalman filter to maintain high-precision positioning during SAR outages. Experimental results demonstrate that, compared to pure INS localization in degraded scenarios, the proposed method reduces northward error MAE and RMSE by approximately 92.8% and 93.9% and eastward error MAE and RMSE by 54.1% and 67.1%. Against suboptimal network baselines, further improvements of 50.8%/38.1% (north) and 17.1%/16.7% (east) in MAE/RMSE were achieved.