Insect echo self-adaptive filtering method based on micro-motion phase self-correlation matching

By using an insect echo adaptive filtering method based on micro-motion phase autocorrelation matching, the limitations of filtering the main Doppler frequency of insect targets are solved, and high-precision extraction of insect wing-beat micro-motion features is achieved.

CN121348320AActive Publication Date: 2026-01-16ADVANCED TECH RES INST OF BEIJING UNIV OF TECH +1
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Patent Information

Application Number
CN202511914296.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-18
Publication Date
2026-01-16
Estimated Expiration
2045-12-18

AI Technical Summary

Technical Problem

Existing methods for filtering out the main Doppler frequency of insect targets have limitations. Fixed window lengths make it difficult to adapt to all scenarios, while low-frequency filtering can prevent insect targets that fly with low-frequency vibrations from being effectively extracted and identified.

Method used

An adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching is adopted. By modeling the instantaneous Doppler frequency of the original insect echo signal, the optimal filtering window length is determined by using the autocorrelation function, and adaptive filtering is performed to filter out the main Doppler component and compensate for slow-time signal echoes.

Benefits of technology

It improves the filtering accuracy of complex main Doppler targets and enhances the ability to extract the micro-movement features of insect wing flapping on radar.

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Abstract

The invention discloses an insect body echo self-adaptive filtering method based on micro-motion phase self-correlation matching, belongs to the technical field of radars, and is used for solving the problems that an existing filtering mode of the main Doppler frequency of an insect target has certain limitation, full-scene adaptation is difficult to realize by a fixed window length, and the filtering efficiency is low. And low-frequency filtering causes that the insect target in low-frequency vibration flight cannot be effectively extracted and identified. The method comprises the following steps: carrying out instantaneous Doppler frequency model modeling processing on an original insect echo signal to obtain a target instantaneous Doppler frequency model; performing smoothing processing based on different sliding window lengths on the target echo phase to obtain a micro-motion phase after the main Doppler component is filtered; carrying out self-correlation function calculation under a related self-adaptive window length on the micro-motion phase after the main Doppler component is filtered out; determining an optimal filtering window length corresponding to the current insect phase signal; and performing slow-time signal echo compensation processing on the current insect phase signal to obtain a micro-motion signal of the current insect.
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