A radar target feature enhanced micro-doppler target discrimination method

By performing multi-dimensional processing and adaptive nonlinear correction on radar echo signals, the problem of obscuring limb micro-motion features was solved, and high-precision target recognition was achieved in complex environments.

CN122218643APending Publication Date: 2026-06-16NANTONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NANTONG UNIV
Filing Date
2026-03-27
Publication Date
2026-06-16

AI Technical Summary

⚠Technical Problem

When identifying non-rigid targets, existing radar technology is prone to masking the subtle limb movements by strong background signals from the torso, resulting in insufficient utilization of multi-channel information and an inability of the enhancement coefficient to adapt to changes in the signal-to-noise ratio, leading to low discrimination accuracy.

⚗Method used

By analyzing radar echo signals, reconstructing multidimensional matrices, performing Fourier transforms and cross-channel normalization, introducing adaptive nonlinear correction operators to stretch low-energy regions, dynamically enhancing limb end features, acquiring enhanced data, locking the target centroid, and inputting it into the discrimination model.

🎯Benefits of technology

It significantly enhances the contrast of limb end motion features, has strong algorithm adaptability, can improve recognition accuracy under different signal-to-noise ratio environments, and improves the radar's discrimination accuracy for micro-moving targets.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a radar target feature enhancement micro-Doppler target discrimination method, comprising the following steps of S1, data acquisition and reconstruction; S2, distance dimension processing; S3, speed dimension processing; S4, cross-channel global normalization; S5, adaptive gamma correction; and S6, target result discrimination. Through adaptive nonlinear correction, the application significantly enhances the contrast of limb movement details in the atlas, so that the feature saliency is strong. Through the global normalization and the dynamic coefficient determination mechanism, the application can automatically adapt to the enhancement requirements in different signal-to-noise ratio environments, and has high adaptability and robustness.
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