Local Maximum Reassignment Chirplet Basis Transform With Doppler Through-Wall Radar Target Localization

In recent years, Doppler through-wall radar (TWR) have shown its potential in Internet of Things (IoT) applications like target positioning, smart security, and health monitoring. However, traditional time-frequency analysis (TFA) struggles with close-range or crossed instantaneous frequency (IF) from multiple targets, reducing positioning accuracy. To address this, we propose a novel algorithm, local maximum reassignment chirplet basis transform (LMRCBT). Firstly, the time-frequency characteristics of the target signal are analyzed using Short-Time Fourier Transform (STFT) and the time-frequency distribution (TFD) is chunked to ensure local consistency. Next, we introduce the chirplet basis function combined with a kurtosis-based local optimal Chirp domain theory to capture the optimal TFD and enhance signal energy concentration. Finally, we design a timefrequency reassignment operator (RO) to optimize the redistribution of time frequency coefficients, improving resolution and boosting noise suppression. The main contribution of LMRCBT is its effective handling of non-stationary signals, especially those with close-range, non-proportional, or crossed IF, improving target localization accuracy and suppressing timefrequency aliasing, as shown in experiments.