Showing papers on "Time–frequency analysis published in 2020"

A Concentrated Time–Frequency Analysis Tool for Bearing Fault Diagnosis

Abstract: In industrial rotating machinery, the transient signal usually corresponds to the failure of a primary element, such as a bearing or gear. However, faced with the complexity and diversity of practical engineering, extracting the transient signal is a highly challenging task. In this paper, we propose a novel time–frequency analysis method termed the transient-extracting transform, which can effectively characterize and extract the transient components in the fault signals. This method is based on the short-time Fourier transform and does not require extended parameters or a priori information. Quantized indicators, such as Renyi entropy and kurtosis, are employed to compare the performance of the proposed method with other classical and advanced methods. The comparisons show that the proposed method can provide a much more energy-concentrated time–frequency representation, and the transient components can be extracted with a significantly larger kurtosis. The numerical and experimental signals are used to show the effectiveness of our method.

A Combined Polynomial Chirplet Transform and Synchroextracting Technique for Analyzing Nonstationary Signals of Rotating Machinery

Abstract: Time–frequency analysis (TFA) technique is an effective approach to capture the changing dynamic in a nonstationary signal. However, the commonly adopted TFA techniques are inadequate in dealing with signals having a strong nonstationary characteristic or multicomponent signals having close frequency components. To overcome this shortcoming, a new TFA technique applying a polynomial chirplet transform (PCT) in association with a synchroextracting transform (SET) is proposed in this paper. It is shown that the energy concentration of the time–frequency representation (TFR) of a strong frequency-modulated signal from a PCT transform can be further enhanced by an SET transform. The technique can also be employed to accurately extract the signal components of a multicomponent nonstationary signal with close frequency components by adopting an iterative process. It is found that the TFR calculated from the proposed technique matches well with the ideal TFR, which demonstrates the superiority of the current technique in dealing with nonstationary signals having rapidly changing dynamics. Results from the analysis of the experimental data under varying speed conditions confirm the validity of the proposed technique in dealing with nonstationary signals from practical sources.

Cooperative Specific Emitter Identification via Multiple Distorted Receivers

Abstract: Specific emitter identification (SEI) is a technique that identifies the unique emitter from its received signal by using the specific characteristics of an emitter In this paper, we consider an SEI problem with unknown receiver distortion Two groups of SEI schemes based on signal decomposition are proposed In the proposed schemes, the received signal is pre-processed by either of the following decomposition, ie, empirical mode decomposition (EMD), intrinsic time-scale decomposition (ITD), or variational mode decomposition (VMD) In the first group of the proposed schemes, the skewness and the kurtosis are extracted from the decomposed signal, which characterize the non-Gaussian features of the signal The support vector machine (SVM) or the back-propagation (BP) neural network is applied to fuse the features extracted from the multiple distorted receivers respectively and then determine the unknown emitter In the second group of the proposed schemes, an approach based on the long short term memory (LSTM) is proposed The LSTM model learns the deep features rather than the specific non-Gaussian features from the pre-processed signal In contrast to the first group, the features used to identify the unknown emitter are extracted directly from the pre-processed signal by the trained LSTM model Simulation results show that the proposed multi-receiver cooperative schemes can achieve the diversity gain in the identification performance Moreover, we evaluate the identification performance of the proposed schemes in various channels, including the Gaussian channel and the fading channel Compared to the existing methods based on different time-frequency representations, the proposed schemes possess the merits of high identification accuracy and low complexity The significance of this paper is that the receive diversity can be achieved by the proposed schemes by using multiple distorted receivers even without compensating the receiver distortion prior to the identification

Adaptive Tunable Q Wavelet Transform-Based Emotion Identification

Abstract: Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition from electroencephalogram (EEG) signals plays a vital role in the development of a brain–computer interface (BCI). Extracting the important information from raw EEG signals is difficult due to its nonstationary nature. Fixing a factual predefined basis function for efficient decomposition using a tunable $Q$ wavelet transform is an arduous task. In this article, an adaptive tunable $Q$ wavelet transform is proposed for the automatic selection of tuning parameters. Optimum tuning parameters are obtained using gray wolf optimization (GWO). Tuning parameters obtained by GWO are used to decompose the EEG signals into subbands (SBs). The set of time-domain features elicited from the SBs are used as an input to multiclass least-squares support vector machine. Classification accuracy of four basic emotions, namely, happy, fear, sad, and relax, is tested and compared with existing methods. An accuracy of 95.70% is achieved with a radial basis function kernel that is about 5% more than the existing methods using the same data set. This article proposes the development of a nonparameterized decomposition method for efficient decomposition of EEG signals. This method can be used with machine learning algorithms to take a step forward in the development of BCI systems.

Novel Short-Time Fractional Fourier Transform: Theory, Implementation, and Applications

Abstract: As a generalization of the classical Fourier transform (FT), the fractional Fourier transform (FRFT) has proven to be a powerful tool for signal processing and analysis. However, it is not suitable for processing signals whose fractional frequencies vary with time due to a lack of time localization information. A simple method to overcome this limitation is the short-time FRFT (STFRFT). There exist several different definitions of the STFRFT in the literature. Unfortunately, these existing definitions do not well generalize the classical result of the conventional short-time FT (STFT), which can be interpreted as a bank of FT-domain filters. The objective of this paper is to propose a novel STFRFT that preserves the properties of the conventional STFT and can be implemented easily in terms of FRFT-domain filter banks. We first present the novel STFRFT and then derive its inverse transform and basic properties. The time-fractional-frequency analysis of this transform is also presented. Moreover, the implementation of the proposed STFRFT is discussed. Finally, we provide several applications for the proposed STFRFT.

Convolutional neural network based emotion classification using electrodermal activity signals and time-frequency features

Abstract: In this work, an attempt has been made to classify emotional states using Electrodermal Activity (EDA) signals and Convolutional Neural Network (CNN) learned features. The EDA signals are obtained from the publicly available DEAP database and are decomposed into tonic and phasic components. The phasic component is subjected to the short-time Fourier transform. Thirty-eight features of time, frequency, and time–frequency domain are extracted from the phasic signal. These extracted features are applied to CNN to learn robust and prominent features. Five machine learning algorithms, namely linear discriminant analysis, multilayer perceptron, support vector machine, decision tree, and extreme learning machine are used for the classification. The results show that the proposed approach is able to classify the emotional states using arousal-valence dimensions. Classification using CNN learned features are found to be better than the conventional features. The trained end-to-end CNN model is found to be accurate (F-measure = 79.30% and 71.41% for arousal and valence dimensions) in classifying various emotional states. The proposed method is found to be robust in handling the dynamic variation of EDA signals for different emotional states. The results show that the proposed approach outperformed most of the state-of-the-art methods. Thus, it appears that the proposed method could be beneficial in analyzing various emotional states in both normal and clinical conditions.

Moving Ground Target Detection With Seismic Signal Using Smooth Pseudo Wigner–Ville Distribution

Abstract: The main challenging task in moving ground target detection with seismic signals is the extraction of robust feature vectors for accurate localization of a seismic event. Toward this goal, a new feature extraction technique is proposed for seismic event detection based on time-frequency analysis implemented with the Smooth pseudo Wigner–Ville distribution (SPWVD). The time-frequency coefficients are utilized to constitute a Renyi entropy vector exploiting a probability distribution function. This entropy vector gives information on the localized measurement of a possible seismic event resulting from moving ground vehicles. The likelihood of an actual seismic event is then ensured by using a constant false alarm rate (CFAR) detector which dynamically adjusts the threshold to minimize false alarms. The proposed algorithm is tested on the benchmarked data set SITEX02 consisting of seismic signals generated by moving tracked and wheeled vehicles. Additionally, the algorithm is also applied on a data set generated by collecting the seismic signature of a moving wheeled vehicle, i.e., bus within the campus. The results are also compared with pseudo Wigner–Ville distribution (PWVD) and short-time Fourier transform (STFT)-based seismic event detection technique. Significantly improved detection results are observed for SPWVD technique. An F-score improvement of ~24% as well as lead time enhancement of twofold is observed in comparison with the PWVD for relatively weak seismic signals generated by wheeled Dragon Wagon (DW) vehicles. Similarly, the F-score improvement is of ~13% and the lead enhancement is approximately 10 s in comparison with the STFT method.

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

Abstract: Time-frequency analysis always plays an important role in machine health monitoring owing to its advantage in extracting the fault information contained in non-stationary signal. In this paper, we present a novel technique to detect and diagnose the rolling bearing faults based on high-order synchrosqueezing transform (FSSTH) and detrended fluctuation analysis (DFA). With this method, the high-order synchrosqueezing transform is first utilized to decompose the vibration signal into an ensemble of oscillatory components termed as intrinsic mode functions (IMFs). Meanwhile, an empirical equation, which is based on the DFA, is introduced to adaptively determine the number of IMFs from FSSTH. Then, a time-frequency representation originated from the decomposed modes or corresponding envelopes is exhibited in order to identify the fault characteristic frequencies related to rolling bearing. Experiments are carried out using both simulated signal and real ones from Case Western Reserve University. Results show that the proposed method is more effective for the detection of fault characteristic frequencies compared with the traditional synchrosqueezing transform (SST) based fault diagnosis algorithm, which renders this technique is promising for machine fault diagnosis.

A scale independent flexible bearing health monitoring index based on time frequency manifold energy & entropy

Abstract: Shannon entropy measure applied to a time frequency distribution of a signal is a reliable indicator to quantitatively analyze a rolling element bearing health status. However, usually conventional time frequency representations rely on selection of the best scale of a base function in order to deal with noise components present in a vibration signal. In this context, time frequency manifold (TFM) is a relatively new time frequency analysis method, which has capability of cancelling out or suppressing most of noise components by utilizing correlation of the deterministic information of a faulty signal in its multidimensional phase space representation rather than depending on scale selection a certain base function. Targeting the merit of time frequency manifold, in this research, the concepts of time frequency manifold and Shannon entropy are integrated to construct an accurate bearing health monitoring index. Effects of embedding dimension and time delay on the calculation of proposed health index have been studied. Simulated signals have been used to study the characteristic properties of the proposed index. Two experimental datasets have been used to valiadate the effectiveness of the proposed method in compare to some conventional and hybrid bearing health monitoring indexes. It has been shown that proposed index shows a consistent good performance for both inner and outer race failure in case of run to failure bearing testing.

Online Estimation of ESR for DC-Link Capacitor of Boost PFC Converter Using Wavelet Transform Based Time–Frequency Analysis Method

Abstract: Aluminum electrolytic capacitor (AEC) is one of the most age-affected components in ac–dc conversion, and its equivalent series resistance ( ESR ) is an important index for reflecting the healthy condition of AEC. In AEC-used boost power factor correction (PFC) converters, ESR of AEC causes a small jump in the switching ripple of output voltage at switching moments, especially at turn- off moments. This small jump is hardly observed at line-frequency scale, either using time-domain analysis or frequency-domain analysis. However using time–frequency analysis this jump is very prominent due to its singularity. In this article, an online ESR estimation method of AEC is proposed by using the wavelet transform (WT) based time–frequency analysis. The relationship between ESR and the jump amount of output voltage at turn- off moments is analyzed first, and then the ESR calculation model is derived using WT with the wavelet basis of the first derivative of Gaussian function. An appropriate sampling interval for the output voltage and the inductor current is determined. Besides, the online ESR estimation scheme is implemented including the hardware and software designs. Furthermore, a prototype of boost PFC converter with 220 V ac input and 360 V dc output is built, where an average current mode control chip UC3854 is used. Four factors are discussed for estimation accuracy in the experiment, and the estimated results are consistent with the results measured by LCR meter with a relative error less than 10%.

EEG Based Brain State Classification Technique Using Support Vector Machine -A Design Approach

Abstract: Brain Computer Interface is a good beneficial route for severely physically challenged person who is underprivileged to communicate in conventional way or have lost their ability to speak. The cause of the work carried in the paper is to solve the problem of patients suffering from neurological disorder and its disabilities that give rise to this research. In the proposed work Electroencephalogram (EEG) based brain state signal measurement method is use to record the brain activity which is source of communication system between patient and outside world. Electroencephalogram is a non-muscular channel between the human brain and a computer system is provided by brain-computer interface (BCI) in which electrical activity are recorded for perusal of EEG signals by the brain. This signals are then decomposed into smaller segments of signal by Time frequency approaches (T-F) like fast Fourier transform & short time Fourier transform. Both these techniques acts as a feature extraction method followed by training of the data and the classification is done by using support vector machine. The performance parameters like accuracy, precision, sensitivity, specificity are calculated based on the values of evaluation metrics and overall system accuracy comes out to be 92%. The four classified signals can be used as Communication messages by the patients which will help to solve the speech impairment problem of disabled person.

Time-Synchroextracting General Chirplet Transform for Seismic Time–Frequency Analysis

Abstract: Synchrosqueezing transform (SST) is an effective time–frequency analysis (TFA) approach for the processing of nonstationary signals. The SST shows a satisfactory ability of the TF localization of the nonlinear signal with a slowly time-varying instantaneous frequency (IF). However, for the signal of which ridge curves in the TF domain are fast varying, or even almost parallel to the frequency axis, the SST will provide a blurred TF representation (TFR). To solve this issue, the transient-extracting transform (TET) was recently put forward. The TET can effectively characterize and extract transient features in the much concentrated TFR for the strongly frequency-modulated (FM) signal, especially the impulse-like signal. However, contrary to the SST, it is not suitable for weak FM modes. In this study, we propose a TFA method called the time-synchroextracting general chirplet transform (TEGCT). The TEGCT can achieve a highly concentrated TFR for strong FM signals as well as weak FM ones. Quantized indicators, the concentration measurement and the peak signal-to-noise ratio, are used to analyze the performances of the proposed method compared with those of other methods. The comparisons show that the TEGCT can provide a result with better TF localization. Then, the proposed method was applied to the spectrum analysis of the seismic data for oil reservoir characteristics. The horizontal slices of the offshore 3-D seismic data show that the TEGCT delineates more distinct and continuous subsurface channels in a fluvial-delta deposition system. All the results illustrate that our proposed method is a good potential tool for seismic processing and interpretation in the geoscience.

Horizontal Synchrosqueezing Transform: Algorithm and Applications

Abstract: Advancements in the synchrosqueezing transform as a postprocessing time–frequency method have received considerable attention in the past few decades for the analysis of nonstationary signals. Many studies have focused on improving the accuracy of the estimated instantaneous frequency (IF). In some fields, the IF spectra of signals exhibit fast varying behavior or even some times parallel to the frequency axis. Thus, a majority of the existing methods may fail to adequately handle the transient signals such as guided waves and vibration waves. To address this problem, a new method called the horizontal synchrosqueezing transform (HST) is introduced in the present research study. By applying a local estimation of the group delay (GD), the proposed technique can yield a compact time–frequency representation for a transient signal while retaining its invertibility. For the purpose of validation, the proposed approach is implemented in both synthetic and real signals. The results are observed in a convincing manner in terms of the smaller value of the Renyi entropy and better reconstruction accuracy. The HST is supposed to be a reliable and accurate method for the analysis of transient signals with extensive usefulness.

Seismic Time–Frequency Analysis via Adaptive Mode Separation-Based Wavelet Transform

Abstract: To better reveal time-varying spectral components of nonstationary seismic signals, time–frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral mode separation and an adaptive wavelet bank design. The proposed adaptive mode separation-based wavelet transform (AMSWT) generates a superior time–frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

Direction Finding of Linear Frequency Modulation Signal in Time Modulated Array With Pulse Compression

Abstract: A novel direction-finding method with time-modulated array (TMA) is proposed for modulating the echo linear frequency modulation (LFM) signal and analyzing its harmonic characteristics. Unlike conventional analysis method where each coefficient of harmonic components is analyzed by discrete Fourier transform, in the proposed method, the harmonic coefficients of modulated signals are calculated by using the pulse compression technology. After the modulated LFM signal is compressed by the matched filter, a closed-form expression of the corresponding output signal is derived and the harmonic coefficients of the input modulated signals can also be obtained. Meanwhile, in order to avoid being affected by other harmonic components in the process of analyzing each harmonic coefficient, the constraints to acquire independent harmonic coefficients are highlighted. Numerical simulations are provided to verify the performance of the proposed method, and a simple S-band eight-element TMA is constructed to experimentally verify its feasibility.

A Comparison of Time-Frequency Methods for Real-Time Application to High-Rate Dynamic Systems

Abstract: High-rate dynamic systems are defined as engineering systems experiencing dynamic events of typical amplitudes higher than 100 gn for a duration of less than 100 ms. The implementation of feedback decision mechanisms in high-rate systems could improve their operations and safety, and even be critical to their deployment. However, these systems are characterized by large uncertainties, high non-stationarities, and unmodeled dynamics, and it follows that the design of real-time state-estimators for such purpose is difficult. In this paper, we compare the promise of five time-frequency representation (TFR) methods at conducting real-time state estimation for high-rate systems, with the objective of providing a path to designing implementable algorithms. In particular, we examine the performance of the short-time Fourier transform (STFT), wavelet transformation (WT), Wigner–Ville distribution (WVD), synchrosqueezed transform (SST), and multi-synchrosqueezed transform (MSST) methods. This study is conducted using experimental data from the DROPBEAR (Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research) testbed, consisting of a rapidly moving cart on a cantilever beam that acts as a moving boundary condition. The capability of each method at extracting the beam’s fundamental frequency is evaluated in terms of precision, spectral energy concentration, computation speed, and convergence speed. It is found that both the STFT and WT methods are promising methods due to their fast computation speed, with the WT showing particular promise due to its faster convergence, but at the cost of lower precision on the estimation depending on circumstances.

Performance of Chirp Parameter Estimation in the Fractional Fourier Domains and an Algorithm for Fast Chirp-Rate Estimation

Abstract: This article addresses the problem of fast and accurate chirp signal parameter estimation in fractional Fourier domains. By employing a perturbation analysis, it is shown that the fractional Fourier transform can be used as an effective tool to yield an asymptotically minimum-variance unbiased estimator of the chirp parameters. Furthermore, it is shown that the asymptotic performance of the fractional-Fourier-transform-based chirp-rate estimator depends only on the actual chirp rate, not the initial frequency. Consequently, the chirp-rate estimation can be done in only one-dimensional search space, which greatly reduces the computational cost. In order to validate theoretical outcomes, we propose a fast and powerful method for the estimation of chirp rates in the fractional Fourier domains based on the golden section search. Extensive computer simulations confirm the theoretical results by demonstrating that the estimation performance of the chirp rate achieves the Cramer–Rao lower bound for both single- and multicomponent chirps. Consequently, we assert that the proposed method of chirp parameter estimation in the fractional Fourier domains is the minimum-variance unbiased estimator, requiring minimal computational cost.

Time–Frequency Feature Fusion for Noise Robust Audio Event Classification

Abstract: This paper explores the use of three different two-dimensional time–frequency features for audio event classification with deep neural network back-end classifiers. The evaluations use spectrogram, cochleogram and constant-Q transform-based images for classification of 50 classes of audio events in varying levels of acoustic background noise, revealing interesting performance patterns with respect to noise level, feature image type and classifier. Evidence is obtained that two well-performing features, the spectrogram and cochleogram, make use of information that is potentially complementary in the input features. Feature fusion is thus explored for each pair of features, as well as for all tested features. Results indicate that a fusion of spectrogram and cochleogram information is particularly beneficial, yielding an impressive 50-class accuracy of over $$96\%$$ in 0 dB SNR and exceeding $$99\%$$ accuracy in 10 dB SNR and above. Meanwhile, the cochleogram image feature is found to perform well in extreme noise cases of $$-\,5$$ dB and $$-\,10$$ dB SNR.

Time-Frequency Maximal Information Coefficient Method and its Application to Functional Corticomuscular Coupling

Abstract: An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time–frequency domain, named time–frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14–30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8–13 Hz) and beta-band (14–30 Hz) than in the gamma-band (31–45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.

A machine learning–based approach for adaptive sparse time–frequency analysis used in structural health monitoring:

Abstract: Time–frequency analysis is an essential subject in nonlinear and non-stationary signal processing in structural health monitoring, which can give a clear illustration of the variation trend of time...

Low-Rank Hankel Matrix Completion for Robust Time-Frequency Analysis

Abstract: In this paper, we develop a novel method to enable robust sparsity-based time-frequency representation of multi-component frequency modulated signals in the presence of burst missing samples, where the amplitudes of the different signal components are generally different. Unlike existing methods which require cross-term presence to be sparse in the time-frequency domain, the proposed method permits effective time-frequency representation reconstruction even when undesired cross-terms take a high occupancy. A key enabling procedure is the high-fidelity missing entry recovery of the instantaneous autocorrelation function that is insensitive to cross-terms. By designing instantaneous autocorrelation function patches such that their Doppler-frequency domain representation is sparse, we formulate the instantaneous autocorrelation function recovery problem as a patch-based low-rank block Hankel matrix completion problem. This approach effectively suppresses the effects of burst missing data samples and is robust to the amplitude differences. A data-adaptive time-frequency kernel is then applied to further mitigate the undesired cross-terms and the residual artifacts due to the burst missing samples. We prove the superiority of the proposed method over the state of the art for both multi-component linear and nonlinear frequency modulated signals. Simulation results confirm that the proposed method outperforms the state of the art for different types of frequency modulated signals with varying signal-to-noise ratios and missing sample rates.

An Online Bearing Fault Diagnosis Technique via Improved Demodulation Spectrum Analysis Under Variable Speed Conditions

Abstract: Online bearing fault diagnosis is still a challenge in real applications because of the complex modulation features and the nonstationary conditions. To realize the online fault diagnosis, a method robust to speed fluctuation and background noise is necessary. In this paper, an iterative generalized demodulation with tunable energy factor (IGDTEF) is proposed, which can map the time-varying trajectories of interest components to their corresponding energy factors. To exploit it in the online fault diagnosis, a phase function estimation strategy is further developed. First, the optimal frequency band of the raw signal is identified by fast kurtogram and then a bandpass filter is designed to separate the impulsive component. Second, Hilbert transform and short-time Fourier transform are applied to the filtered signal jointly obtaining the envelope time–frequency representation (TFR). Then, the instantaneous fault characteristic frequency (IFCF) is estimated roughly by applying an amplitude-sum-based peak search to the TFR. Next, the phase functions of the IFCF, the potential modulation rotating frequency, and their harmonics are calculated. Finally, the IGDTEF is performed to the filtered signal and then fast Fourier transform is applied to the demodulated signal generating the demodulation spectrum. The effectiveness of the method is evaluated by simulated and experimental data.

Quantitative Evaluation of Channel Micro-Doppler Capacity for MIMO UWB Radar Human Activity Signals Based on Time–Frequency Signatures

Abstract: A novel quantitative method to evaluate channel micro-Doppler capacity of multiple-input and multiple-output (MIMO) system is proposed here. The method is valid for ultrawideband (UWB) MIMO radar human activity systems based on time–frequency signatures, and the quality measure will be noted as a relative signal to noise ratio (RSNR). The method quantifies these signatures and evaluates the relative superiority or inferiority of these MIMO channels. Examples of micro-Doppler signature ( $\mu $ Ds) characteristics of human activities in a channel will be considered and compared to that of all other channels. First, the MIMO UWB radar human activity signal is modeled, and its corresponding time–frequency ( $T$ – $F$ ) characteristics are analyzed to justify the rationality of using the new RSNR metric. Second, the method is evaluated using experimental data and the capability of distinguishing the $\mu \text{D}$ capacity differences among channels is demonstrated. This new method clearly and accurately shows much better visible $\mu \text{D}$ evaluation performance than that of the conventional signal to noise ratio in time domain ( ${\mathrm {SNR}}_{t}$ ). Moreover, this evaluation method can still work well, even for signals with low signal to noise ratio (SNR) down to −4-dB level. Therefore, it can be successfully used to select the superior channels and eliminate any inferior channels or provide confidence coefficients for the collected multiple channel data of human activities. This method should lead to a significant reduction of the inferior channels’ influence on further MIMO-based classification or imaging of human activities.