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Improved Linear Chirplet Transform and Singular Value Decomposition Joint Algorithm for Motion Target Tracking
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AI
DOI：10.1109/JIOT.2023.3336839.png)
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Abstract
En 中文
Through-wall radar (TWR)-based target localization algorithm has great promise in the Internet of Things (IoT), such as indoor positioning, health monitoring, and surveillance. However, when locating multiple targets, time-frequency aliasing occurs in the echo's time-frequency distribution (TFD), which makes it challenging to accurately extract the target instantaneous frequency (IF) curve from the TFD, ultimately hindering the achievement of high-precision positioning. In this article, we propose a joint algorithm based on the improved linear chirplet transform (ILCT) and singular value decomposition (SVD) for target tracking, aiming to improve the localization precision of TWR. We design an ILCT algorithm to increase the time-frequency energy concentration of the target component of interest. Then, we use the SVD algorithm, based on the ILCT results, to accurately separate the target signal of interest from the echo signal. Finally, the motion path of the tracked target is synthesized based on the estimated target IF curves. Experimental results of target tracking demonstrated that the proposed method not only improves the target localization precision but also effectively suppresses the time-frequency aliasing phenomenon.
Keywords:
Doppler through-wall radar (TWR)
improved linear chirplet transform (ILCT)
instantaneous frequency (IF)
Internet of Things (IoT)
singular value decomposition (SVD)
target tracking
time-frequency distribution (TFD)
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