External vibration, shock, unbalanced torque and other uncertain disturbances are mainly transmitted to the motor rotor through the bearing friction. To restrain the uncertain friction disturbances and improve the speed stability of a permanent magnet synchronous motor (PMSM), an optimized active disturbance rejection control (ADRC) algorithm is proposed in this study. Firstly, an auxiliary model of friction and a reduced-order processing method are introduced into extended state observation (ESO) to reduce the burden of single ESO and promote the compensation accuracy of disturbances. In addition, a supervisory radial basis function (SRBF) is employed to supervise and promote the error elimination efficiency of the nonlinear state error feedback rate (NLSEF). The hybrid control algorithm makes up for the deficiency of typical ADRC through the fusion of multiple control quantities. Simulation and experimental results show that the proposed algorithm has strong anti-disturbance performance and effectively solves the problem of low-speed crawling.

By enhancing transient fidelity before geometric inversion, this work revisits the classical LCT-based non line-of-sight (NLOS)imaging paradigm and establishes a unified Bayesian sparse-enhancement framework for reconstructing hidden objects under photon-starved and hardware-limited conditions. We introduce sparse Bayesian learning (SBL) as a dedicated front-end transient restoration module, leveraging adaptive sparsity modeling to suppress background fluctuations while preserving physically consistent multipath returns. This lightweight and geometry-agnostic design enables seamless integration into existing LCT processing pipelines, granting the framework strong compatibility with diverse acquisition configurations. Comprehensive simulations and experiments on complex reflective targets demonstrate significant improvements in spatial resolution, boundary sharpness, and robustness to IRF-induced temporal blurring compared with traditional LCT and f-k migration methods. The results validate that transient quality remains a critical bottleneck in practical NLOS deployment, and addressing it via probabilistic sparsity inference offers a scalable and computationally affordable pathway toward stable, high-fidelity NLOS reconstruction. This study provides an effective signal-domain enhancement solution that strengthens the practicality of NLOS imaging in real-world environments, paving the way for future extensions toward dynamic scenes, multi-view fusion, and high-throughput computational sensing.

The main function of the dynamic scan and stare imaging system is to quickly and continuously search a large area and perform high-resolution imaging. To eliminate image motion during the scanning process, this paper proposes an image motion compensation control method with dual channel control. Firstly, an improved model-assisted active disturbance rejection control is proposed, in which an auxiliary model is integrated into the algorithm to improve the control accuracy and response speed of the rotation rate of the platform. Secondly, a fast steering mirror (FSM) is introduced into the control system to compensate for the scanning speed and multiple disturbances. By adopting the amplitude frequency characteristics of the tracking differentiator, a parallel tracking differentiator filter is designed to suppress the interference of gyroscope noise on the FSM. When the system is disturbed by multiple frequency disturbances, the residual error of the image motion compensation is less than the spatial angular resolution of one pixel through the dual channel stable control of the platform and the FSM. In the scan imaging experiment results, the average value of the grayscale variance function for the compensated images is close to 90% that of the static reference images.

The main function of the dynamic scan and stare imaging system is to quickly and continuously search a large area and perform high-resolution imaging. To eliminate image motion during the scanning process, this paper proposes an image motion compensation control method with dual channel control. Firstly, an improved model-assisted active disturbance rejection control is proposed, in which an auxiliary model is integrated into the algorithm to improve the control accuracy and response speed of the rotation rate of the platform. Secondly, a fast steering mirror (FSM) is introduced into the control system to compensate for the scanning speed and multiple disturbances. By adopting the amplitude frequency characteristics of the tracking differentiator, a parallel tracking differentiator filter is designed to suppress the interference of gyroscope noise on the FSM. When the system is disturbed by multiple frequency disturbances, the residual error of the image motion compensation is less than the spatial angular resolution of one pixel through the dual channel stable control of the platform and the FSM. In the scan imaging experiment results, the average value of the grayscale variance function for the compensated images is close to 90% that of the static reference images.