Advanced multidimensional factor models incorporating high-precision sensor arrays and optimization techniques for accurate 3-D near-field source parameter estimation

Abstract

Accurate estimation of 3-D near-field source parameters is essential for applications such as radar systems, underwater acoustics, and biomedical imaging. However, traditional methods often fall short due to limitations in handling noise, interference, and environmental variability. This study aimed to develop a multidimensional factor model that integrates high-precision sensor arrays with advanced optimization techniques to address these challenges. The proposed model utilizes compressive sensing for sparse signal recovery and hybrid optimization frameworks, including deep learning-based reconstruction algorithms and metaheuristic methods, to enhance accuracy and computational efficiency.The research employed high-precision sensor arrays equipped with adaptive beam forming capabilities to capture multidimensional data. A comprehensive test bed simulated diverse environmental conditions, including fluctuating noise levels and multi-source interference, to evaluate the model's robustness. Comparative analysis with traditional methods and benchmark models was conducted using performance metrics such as Mean Square Error (MSE), processing time, and robustness against accuracy degradation.Results showed that the proposed model achieved a significantly lower MSE (as low as 0.011±0.001) compared to traditional methods and benchmark models. The average processing time per estimation was 0.35 seconds, significantly faster than the alternatives. Additionally, the model demonstrated superior robustness, maintaining accuracy degradation below 7% under challenging conditions. Statistical analyses, including ANOVA and t-tests, validated these findings, underscoring the model’s effectiveness and reliability.In conclusion, the proposed model addresses key limitations in traditional approaches, offering a robust and efficient solution for 3-D near-field parameter estimation. Practical recommendations include optimizing hardware-software integration, validating with real-world datasets, and exploring modular designs for scalability. These advancements position the model as a transformative tool in signal processing, with broad applicability across defense, healthcare, and environmental monitoring.