研究目的
To develop an iterative adaptive imaging approach for phased-array imaging of an unknown area using narrowband RF signals and robotic arrays, improving imaging quality by optimally sensing uncertain regions.
研究成果
The proposed adaptive imaging framework effectively improves image quality by iteratively reducing uncertainty in regions of interest using optimized robotic arrays. Numerical simulations demonstrate suppression of ghost objects and better object detection compared to fixed-array methods. Future work could address real-world implementation and broader scenarios.
研究不足
The approach relies on numerical simulations and may have limitations in real-world applications due to factors like robot mobility constraints, environmental noise, and the assumption of narrowband signals. The optimization is computationally intensive for exhaustive search over array parameters.
1：Experimental Design and Method Selection:
The methodology involves an iterative adaptive imaging framework using near-field beamforming principles. It includes initial sensing with fixed arrays, identification of uncertain regions, optimization of array location and orientation, and image fusion. Theoretical models for point spread function (PSF) and optimization are employed.
2：Sample Selection and Data Sources:
Numerical simulations are conducted in MATLAB for scenarios with objects in an unknown area. Scattered signals are calculated based on electromagnetic field models.
3：List of Experimental Equipment and Materials:
Robots (unmanned vehicles) are used to synthesize antenna arrays. Specific equipment details are not provided in the paper.
4：Experimental Procedures and Operational Workflow:
The process starts with initial imaging using arrays on four sides of the workspace. Then, uncertain regions are identified using thresholding and superpixel methods. An optimization problem is solved to find the best array configuration, and new measurements are collected. Images are combined iteratively until stopping criteria are met.
5：Data Analysis Methods:
Image intensity analysis, thresholding, optimization via exhaustive search, and additive or weighted fusion of images are used. MATLAB is employed for simulations and data processing.
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