Fuse accurate height measurement method based on parameterized model and deep learning

By combining parametric models with deep learning, the problems of low ranging accuracy and poor scene adaptability of radio fuses under complex terrain conditions were solved, achieving accurate altitude measurement of the fuse, improving ranging accuracy and enhancing the adaptability of the model.

CN122020371APending Publication Date: 2026-05-12BEIJING INST OF TECH
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Patent Information

Application Number
CN202610071557.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-20
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Existing empirical models have low ranging accuracy and poor scene adaptability under complex terrain conditions, making it difficult to achieve accurate altitude measurement.

Method used

A parametric model and deep learning-based approach is adopted to construct a parametric ground echo simulator for electromagnetic scattering. A deep fully connected network is used to extract echo signal features and estimate electromagnetic scattering parameters. Gradient boosting trees are combined to correct the error of height information, thereby achieving accurate height measurement of the fuze.

Benefits of technology

It improves the ranging accuracy of the fuze under complex terrain conditions, enhances scene adaptability, reduces altitude measurement error, and simplifies model deployment.

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Abstract

The invention belongs to the technical field of radio fuses, and particularly relates to an accurate fuze height measurement method based on a parameterized model and deep learning, and the method specifically comprises the steps: determining an electromagnetic scattering parameter combination in a ground echo model building process, and carrying out the sampling in a multi-dimensional prediction parameter space; a controllable electromagnetic scattering parameterization ground echo simulator is constructed based on the improved Ulaby model; performing echo modeling on the N groups of parameter combinations by using the simulator to obtain original echo signals, extracting original echo signal features, and combining the original echo signal features into a high-dimensional feature vector as the input of a deep learning model; constructing a deep full-connection network, and training a network model by taking the feature vector as input and the electromagnetic scattering parameter combination estimation value as a label; actual fuze echo data are collected, the trained network model is utilized to obtain an electromagnetic scattering parameter combination estimation value and optimize the electromagnetic scattering parameter combination estimation value, error correction is carried out on height information in optimized electromagnetic scattering parameters, and accurate fuze height measurement is achieved.
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