Synthetic aperture interferometric radiometers (SAIRs) have attracted increasing attention for target detection applications, owing to their high spatial resolution and passive sensing capability. In such applications, fast and wide-area imaging is essential for efficient target localization, with a wide instantaneous field of view (FOV) being critical for capturing more information per snapshot and reducing data acquisition time. However, the effective FOV and alias-free FOV (AF-FOV) of uniformly sampled arrays are limited by the minimum element spacing in interferometric arrays. While non-uniform sampling offers a potential solution to alleviate these limitations, a comprehensive framework for optimizing non-uniform arrays in SAIRs remains lacking. To address this gap, we propose a sidelobe suppression-assisted array design method to enlarge the effective FOV of SAIRs. We begin by defining a novel optimization objective named modified array factor (MAF), with consideration of the spatial-variant array factor (AF) for non-uniform sampling SAIR systems. Subsequently, a constrained multi-objective optimization model is formulated to improve the effective FOV while maintaining angular resolution and sensitivity, subject to constraints on array aperture size and minimum element spacing. Finally, a global optimization algorithm is employed to solve this model. Simulation results demonstrate that the optimized non-uniform arrays (ONAs) significantly extend the effective FOV while maintaining competitive angular resolution and sensitivity compared to conventional arrays, validating the effectiveness of the proposed approach.

This paper proposes a pixel-level grasp configuration prediction method based on deep neural network. The method utilizes RGB images as inputs, combines with a deep neural network model, and outputs the object's grasp configuration at the pixel level. This paper adopts a new region-level AGA model to model the grasping properties of objects. The model solves the angle conflict during training and simplifies the process of marking the real grasping posture. A pose estimation network based on Deeplabv3 is designed to predict the OAR model on RGB images. Pixel-level mapping avoids the loss of real grasping posture and overcomes the limitations of current deep learning technologies by avoiding discrete sampling of grasp candidates and long computation times. Finally, experiments are conducted on the Cornell Grasp Dataset to verify the proposed method. The results show that the proposed method can accurately predict the grasp configuration of objects at the pixel level and has good predictive and generalization abilities.

In order to solve the problems of slow convergence speed and poor global search ability in mobile robot path planning, an adaptive ant colony optimization algorithm (AACO) is proposed in this paper. First, in the early stage of the ant colony search, adaptive initial pheromone distribution is used to reduce the blindness of ant colony algorithm. Using adaptive pheromone factor and adaptive evaporation factor to improve the role of pheromone in different periods of convergence of ant colony algorithm. Improve the update mechanism of pheromones and use pheromone preferential limited update to reduce the redundancy of pheromones. A novel adaptive pheromone reconstruction mechanism is proposed to improve the global search capability of the ant colony algorithm. Finally, through two random environment experiments, the proposed algorithm has better path planning ability than some similar algorithms and classical ant colony algorithms.