An Indoor Moving Target Detection Method Based on Doppler Chirp Rate Profile and Range Gating Filter

The detection of indoor moving targets using autonomous-aerial-vehicle (AAV)-mounted through-the-wall radar (TWR) has been widely applied in both military and civilian fields. However, the imaging results of moving targets often suffer from defocusing and are prone to be submerged in strong stationary clutter, which reduces the detection rate of moving targets. To address this issue, this article proposes a detection method for indoor moving targets based on Doppler chirp rate profile and range gating filter using AAV-mounted TWR. In the proposed method, the characteristics of Doppler chirp rate for both clutter and moving targets are analyzed. The range migration of the target echo is corrected in the range-Doppler (RD) domain through sinc interpolation. Then, the fractional Fourier transform (FrFT) is applied to estimate the chirp rate at each range bin. Subsequently, the estimated chirp rate values are compared with the theoretical chirp rate values of stationary objects. Based on the differences of Doppler chirp rates, a gating filter in fast time domain is designed to suppress clutter originated from walls and stationary objects. Finally, an azimuth compression filter is constructed to achieve focused imaging of moving targets. Both simulation and field experiments demonstrate the feasibility and effectiveness of the proposed method. The proposed method shows strengths over other methods in terms of improvement factor, image entropy, and peak-sidelobe ratio.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-02-12
DOI
https://doi.org/10.1109/jiot.2025.3540887
Primary Topic
Advanced Measurement and Detection Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

An Indoor Moving Target Detection Method Based on Doppler Chirp Rate Profile and Range Gating Filter

Fengming Liu, Hao Zhang, Xiaodong Qu, Xiaolong Sun
IEEE Internet of Things Journal
Advanced Measurement and Detection Methods
article

An Indoor Moving Target Detection Method Based on Doppler Chirp Rate Profile and Range Gating Filter

Fengming Liu, Hao Zhang, Xiaodong Qu, Xiaolong Sun
article en

Abstract

The detection of indoor moving targets using autonomous-aerial-vehicle (AAV)-mounted through-the-wall radar (TWR) has been widely applied in both military and civilian fields. However, the imaging results of moving targets often suffer from defocusing and are prone to be submerged in strong stationary clutter, which reduces the detection rate of moving targets. To address this issue, this article proposes a detection method for indoor moving targets based on Doppler chirp rate profile and range gating filter using AAV-mounted TWR. In the proposed method, the characteristics of Doppler chirp rate for both clutter and moving targets are analyzed. The range migration of the target echo is corrected in the range-Doppler (RD) domain through sinc interpolation. Then, the fractional Fourier transform (FrFT) is applied to estimate the chirp rate at each range bin. Subsequently, the estimated chirp rate values are compared with the theoretical chirp rate values of stationary objects. Based on the differences of Doppler chirp rates, a gating filter in fast time domain is designed to suppress clutter originated from walls and stationary objects. Finally, an azimuth compression filter is constructed to achieve focused imaging of moving targets. Both simulation and field experiments demonstrate the feasibility and effectiveness of the proposed method. The proposed method shows strengths over other methods in terms of improvement factor, image entropy, and peak-sidelobe ratio.

IEEE Internet of Things JournalVol. 12(11)
Beijing Institute of Technology (CN)
Natural Science Foundation of Shandong Province, Beijing Nova Program
Affordable and clean energy
Openalex Percentile: Top 98%
Advanced Measurement and Detection Methods
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An Indoor Moving Target Detection Method Based on Doppler Chirp Rate Profile and Range Gating Filter — Fengming Liu, Hao Zhang, et al. · IEEE Internet of Things Journal (2025) | TGRS Research Map | TGRS