Estimation of Insect Wingbeat Frequency Based on Micro-Motion Phase Autocorrelation and Micro-Doppler Power Concentration

TY - JOUR

T1 - Estimation of Insect Wingbeat Frequency Based on Micro-Motion Phase Autocorrelation and Micro-Doppler Power Concentration

AU - Ye, Zihan

AU - Li, Weidong

AU - Wang, Rui

AU - Wang, Jiangtao

AU - Zhang, Fan

AU - Hu, Cheng

N1 - Publisher Copyright: © 1980-2012 IEEE.

PY - 2026

Y1 - 2026

N2 - Wingbeat frequency (WBF) is a key parameter for identifying airborne migratory insect species. Radar is one of the most effective tools for measuring the WBF, reliably operating day or night, and under various weather conditions. However, the scattering signal from the insect body is extremely weak, and the wingbeat echo intensity is an order of magnitude smaller than that of the insect body. This results in low signal-to-noise (SNR) wingbeat echoes, making WBF extraction challenging. Traditional amplitude methods achieve acceptable WBF extraction success rates under high SNR conditions, but exhibit significantly lower success rates when SNR drops. Subsequent studies have shown that WBF can be extracted by exploiting the micro-Doppler modulation of the echo phase induced by wing beat. However, performance degradation occurs in phase-based methods because airflow disturbances or maneuvering by the insect in flight introduce higher-order nonlinear phase fluctuations that intermix with wingbeat micro-Doppler phase components. To fully use both amplitude and phase information for improved WBF extraction under low SNR conditions, this article proposes a novel method for estimating insect WBF based on micro-motion phase autocorrelation and micro-Doppler power concentration. First, an adaptive filtering approach using micro-motion phase autocorrelation matching is proposed. It can effectively mitigate nonlinear Doppler interference. Secondly, the long-duration micro-Doppler power concentration method based on micro-motion paths and phase model matching is proposed. It greatly improves the performance of the WBF estimation method under low SNR. Validated through simulations and field radar data, the proposed method effectively extracts the WBF of naturally migrating insects. In a field test using echo signals from locusts, the WBF detection success rate of the proposed method was 22% higher than that of the traditional method.

AB - Wingbeat frequency (WBF) is a key parameter for identifying airborne migratory insect species. Radar is one of the most effective tools for measuring the WBF, reliably operating day or night, and under various weather conditions. However, the scattering signal from the insect body is extremely weak, and the wingbeat echo intensity is an order of magnitude smaller than that of the insect body. This results in low signal-to-noise (SNR) wingbeat echoes, making WBF extraction challenging. Traditional amplitude methods achieve acceptable WBF extraction success rates under high SNR conditions, but exhibit significantly lower success rates when SNR drops. Subsequent studies have shown that WBF can be extracted by exploiting the micro-Doppler modulation of the echo phase induced by wing beat. However, performance degradation occurs in phase-based methods because airflow disturbances or maneuvering by the insect in flight introduce higher-order nonlinear phase fluctuations that intermix with wingbeat micro-Doppler phase components. To fully use both amplitude and phase information for improved WBF extraction under low SNR conditions, this article proposes a novel method for estimating insect WBF based on micro-motion phase autocorrelation and micro-Doppler power concentration. First, an adaptive filtering approach using micro-motion phase autocorrelation matching is proposed. It can effectively mitigate nonlinear Doppler interference. Secondly, the long-duration micro-Doppler power concentration method based on micro-motion paths and phase model matching is proposed. It greatly improves the performance of the WBF estimation method under low SNR. Validated through simulations and field radar data, the proposed method effectively extracts the WBF of naturally migrating insects. In a field test using echo signals from locusts, the WBF detection success rate of the proposed method was 22% higher than that of the traditional method.

KW - Insect radar

KW - micro-Doppler

KW - nonlinear phase fluctuation

KW - power concentration

KW - wingbeat frequency (WBF)

UR - https://www.scopus.com/pages/publications/105033262745

U2 - 10.1109/TGRS.2026.3674165

DO - 10.1109/TGRS.2026.3674165

M3 - Article

AN - SCOPUS:105033262745

SN - 0196-2892

VL - 64

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

M1 - 5101714

ER -