Indoor target positioning and trajectory tracking method and system based on millimeter wave radar

By using 77GHz millimeter-wave FMCW radar and fusion algorithms to process signals, the problems of high accuracy and privacy protection in indoor positioning are solved, achieving high-precision target positioning and trajectory tracking, adapting to complex environments and unaffected by obstructions.

CN117907963BActive Publication Date: 2026-06-19SHANDONG NORMAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANDONG NORMAL UNIV
Filing Date
2024-01-22
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve high-precision identification and trajectory tracking in indoor positioning while ensuring user privacy, especially in complex indoor environments. Traditional methods are heavily reliant on user location information and pose a risk of privacy breaches.

Method used

Indoor human body signals are acquired using a 77GHz millimeter-wave FMCW radar, and signal processing is performed through a fusion algorithm, including preliminary noise reduction, data segmentation, environmental noise filtering, clustering, and constant false alarm rate detection, to achieve high-precision target positioning and trajectory tracking.

Benefits of technology

It achieves high-precision indoor target positioning and trajectory tracking, effectively filters out noise, avoids privacy leaks, adapts to complex indoor environments, is unaffected by obstructions, and provides a convenient and reliable user experience.

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Abstract

This invention discloses an indoor target localization and trajectory tracking method and system based on millimeter-wave radar. The method includes: acquiring the frequency-modulated continuous wave emitted by the millimeter-wave radar towards the target area and receiving the echo signal; demodulating and transcoding the echo signal; performing preliminary noise reduction on the demodulated and transcoded data; segmenting and extracting the data after preliminary noise reduction to obtain a dataset; filtering environmental noise from the dataset to obtain a first-optimized dataset; clustering the first-optimized dataset to filter out noise generated by the target during movement, obtaining a second-optimized dataset; and using a constant false alarm rate (CFAR) detection algorithm on the second-optimized dataset, using peak points in the clustering results to replace the entire clustering interval to complete target localization.
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