Accurate recognition and parameter estimation of frequency hopping (FH) signals remain challenging in FH signal-based transmission systems. This study proposes a scheme combining time-frequency analysis (TFA) and deep learning (DL)-based image processing algorithms to alleviate the degradation of recognition accuracy and estimation performance caused by complex electromagnetic interference (EMI). A short-time Fourier transform (STFT) was used to obtain the signal spectrogram, which reflects the signal energy in a concentration-dependent manner. Then, a CenterNet-based deep network was employed to identify the shape and position of the FH signal, which reduces the computational burden via a lightweight neural network while maintaining high recognition accuracy. Inverse mapping from the coordinates to the spectrogram was used to perform parameter estimation in the time-frequency (TF) domain. The estimation error was reduced by precisely locating the centroid of the signal energy using CenterNet. The simulation results demonstrate that the proposed scheme can accurately estimate the FH signal at a low signal-to-noise ratio (SNR) with complex EMI. Furthermore, appropriately determining the optimal parameters of CenterNet to ensure the estimator performance provides a novel approach for integrating DL into signal detection in complex EMI environments.

This letter addresses the approach of enabling a UAV to autonomously land on an agile UGV that moves irregularly with sudden accelerations, decelerations or abrupt turns, where existing methods have limitations in handling nonlinear dynamics and visual recognition errors in high-dynamic situations. To overcome these limitations, we present a real-time UAV-UGV cooperative landing framework that integrates Model Predictive Control and Adaptive Bezier Curve Optimization (MPC-ABCO) to achieve UGV's position prediction and UAV's trajectory forecasting, ensuring robust performance in high-dynamic scenarios. MPC-ABCO utilizes an MPC model to predict the UGV's future states by leveraging odometry data to model UGV's kinematic dynamics. Concurrently, the UAV's landing trajectory is optimized by Bezier curves, with waypoints dynamically updated by considering UGV's relative future positions detected by AprilTag code. Experimental results demonstrate that the proposed framework achieves an average landing deviation below 5 cm for UGV's steering velocities up to 5 m/s under no-acceleration stations. When the UGV follows irregular paths with random accelerations, MPC-ABCO outperforms traditional strategies in both landing success rate and accuracy, maintaining reliable performance at maximum velocities of 5 m/s.

With the ability to manipulate objects contactlessly, acoustic levitation has excellent potential in the micro-assembly industry. Fixed ultrasonic phased arrays (UPA) widely used in acoustic levitation control objects with restricted space and inflexibility. Here, an acoustic array robot based on the dual-rotation mechatronic design is proposed, combining phase and orientation control of two UPAs. The robot model is established to calculate the coordinates, orientations, and phases of each element of two arrays. The simulation and experimental results show that the proposed robot is feasible and effective, and the robot's maximum controllable levitation areas grow by 183.2% and 43.2% over the fixed array levitator in the $yoz$ and $xoz$ planes of the $\lbrace \mathit{O}\rbrace$ , respectively. This robot will contribute to developing micro-manipulation technology in micro-assembly and other fields.