The magnetotelluric (MT) data collected in an ore-concentration area are extremely vulnerable to all kinds of noise pollution. However, separating real MT signals from strong noise is still a difficult problem, and the noise in MT data is quite distinct from clean data in morphological features. By performing the signal-noise identification and data prediction, we develop a deep learning method to denoise MT data containing strong noise. First, we use the convolutional neural network (CNN) to learn the feature differences between the samples of massive noise and clean data and use the learned features to realize signal-noise identification of the measured data. Second, we use the measured clean data obtained by CNN identification to train the long short-term memory (LSTM) neural network and perform the prediction denoising of the noisy data. The simulation results clearly demonstrate the following two facts: (1) the predicted data output from LSTM basically matches the time-frequency domain features of the real data and (2) our CNN method performs significantly better than the features parameter classification method in dealing with signal-noise identification. In addition, the validity of our method is verified by the processing results of the measured data.

Doppler through-wall radar (TWR) is one of the mainstream target positioning equipment, acquiring the target instantaneous frequency (IF) to achieve trajectory tracking. However, severe time-frequency interference leads to a significant deviation of the estimated trajectory from the actual position. Hence, in this work, we propose a target trajectory estimation algorithm based on time-frequency enhancement. Firstly, the time-frequency analysis (TFA) of the echo signal is carried out by the short-time Fourier transform (STFT). By analyzing the time-frequency distribution (TFD) of the echo signal, an over-complete time-frequency dictionary is obtained which can reflect the characteristics of the target. Then, the Wigner-Ville distribution (WVD) is employed to process the radar echo signal, and the predefined dictionary is used to suppress the time-frequency interference in the WVD to achieve time-frequency enhancement. Lastly, the target IF curve is obtained from the enhanced TFD to synthesize the target motion trajectory. The experiment results show that the proposed method can suppress time-frequency interference and improve the estimation accuracy of IF values. Compared with the existing methods, the proposed method can effectively improve the accuracy of TWR localization.

In target tracking applications by doppler through-wall radar (TWR), the frequency ambiguity issue is a notable drawback, which can severely degrade the target localization performance. To overcome this drawback, a convolutional neural networks (CNN) based target tracking algorithm is proposed in this letter. Firstly, the short-time Fourier transform (STFT) is used to acquire an echo spectrogram. Then the spectrogram resolution is enhanced by introducing a modified CNN module. Compared with the traditional CNN module, an additional convolutional weight block with a residual structure is designed to help extract deeper time-frequency features. Finally, the target instantaneous frequency (IF) curves are extracted from the enhanced spectrogram and the target positions are obtained. Experimental results prove that the proposed method can effectively enhance the spectrogram resolution and suppress the frequency ambiguity issue, which would lead to higher target localization accuracy, and better robustness than traditional target tracking approaches.

The micro-Doppler (m-D) modulations to radar backscattering introduced by the flexible body articulations and complicated movement patterns of human movers can provide valuable information for activity classification and help to identify the interested targets. In particular, the m-D signal of limbs, as a highly distinctive feature of human activities, can be used as an effective clue to discriminate between the armed and unarmed persons, as well as the humans against other small animals. In this article, a novel theoretical method is proposed to extract the target m-D trajectories through an integrated application of modified Viterbi algorithm and Hough transform. Through this method, multiple components corresponding to various target scattering parts and their respective m-D trajectories can be accurately extracted and estimated, even in the overlapping regions of different scattering parts in the time–frequency (TF) distribution. The employed search method enhances upon the traditional ones and improves the efficiency of finding the optimal paths considerably. Finally, a series of experiments is conducted to illustrate the validity and performance of the proposed techniques. Compared to short-time Fourier transform (STFT) peak detection and traditional Viterbi algorithm, the average error of m-D frequency estimated by the proposed algorithm is reduced by 82.1% and 71.8%, respectively. Besides, the processing time is reduced by 36.4% compared to the traditional Viterbi algorithm.

Through-wall radar-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.

Doppler Through-Wall Radar (TWR) is a promising device for the Internet of Things (IoT), effective for indoor tracking, health monitoring, and smart homes. However, employing it to estimate the trajectories of multiple targets presents challenges associated with time-frequency analysis (TFA). In this paper, a multimodal network called MWVD is proposed, which eliminates the crossterm problem of Wigner-Ville distribution (WVD) and improves the accuracy of (instantaneous frequency) IF extraction to obtain accurate localization. In the MWVD, both the WVD spectrogram and the 1D complex signals are used as inputs to the network. The complex signals are passed through the proposed multi-window short-time filtering (MWSTF) module followed by an adaptive wavelet attention fusion (AWAF) module to simulate the wavelet transform. Subsequently, the enhanced WVD spectrogram is obtained by the energy compression module. As a result, comprehensive experiments, including simulated signal tests, module ablation studies, fusion mode ablation analyses, and real TWR target tracking, are conducted to demonstrate the proposed algorithm’s excellence, which will be combined with more IoT applications in the future.