scholarly journals Time-Frequency Energy Sensing of Communication Signals and Its Application in Co-Channel Interference Suppression

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2378 ◽  
Author(s):  
Yue Li ◽  
Liang Ye ◽  
Xuejun Sha
Keyword(s):  
Pulse Shaping ◽  
Interference Suppression ◽  
Frequency Resolution ◽  
Energy Concentration ◽  
Interference Signal ◽  
Time Varying ◽  
Communication Signals ◽  
Time Frequency ◽  
Channel Interference ◽  
Energy Sensing

As the number of mobile users and video traffics grow explosively, the data rate demands increase tremendously. To improve the spectral efficiency, the spectrum are reused inter cell or intra cell, such as the ultra dense network with multi-cell or the cellular network with Device-to-Device communications, where the co-channel interferences are brought and needs to be suppressed. According to the time-frequency energy sensing to the communication signals, the desired signal and the interference signal have different energy concentration areas on the time frequency plane, which provide opportunities to suppress the co-channel interference with time varying filter. This paper analyzes the time-frequency distributions of the Gaussian pulse shaping signals, discusses the effect of the analyzing window length on the time-frequency resolution, exploits the equivalence between the time frequency analysis at the baseband and at the radio front end, and finally reveals the advantages of the proposed masking threshold constrained time varying filter based co-channel interference mitigation method. The pass region for the linear time varying filter is generated according to the time-varying energy characteristics of the I/Q separated 4-QAM pulse shaping signals, where the optimum masking threshold is obtained by the optimum-BER criterion. The proposed co-channel interference suppression method is evaluated in aspect of BER performance, and simulation results show that the proposed method outperforms existing methods with low-pass or band-pass filters.

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

Nonstationary Vibration Signal Analysis Using Wavelet-Based Time–Frequency Filter and Wigner–Ville Distribution

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Chang Xu ◽  
Cong Wang ◽  
Wei Liu
Keyword(s):  
Method Comparison ◽  
Frequency Resolution ◽  
Time Varying ◽  
Frequency Filter ◽  
Time Frequency ◽  
Nonstationary Vibration ◽  
Comparison Results ◽  
Cross Terms ◽  
Vibration Signal Analysis ◽  
Wheel Vibration

Vibration responses of nonlinear or time-varying dynamical systems are always nonstationary. Time–frequency representation becomes a necessary approach to analysis such signals. In this paper, a nonstationary vibration analysis method based on continuous wavelet transform (CWT) and Wigner–Ville distribution (WVD) is presented. In order to avoid the cross-terms in the original WVD, a time–frequency filter created by wavelet spectrum is employed to filter the time–frequency distribution (TFD). This process eliminates cross-terms and maintains high time–frequency resolution. The improved WVD is applied to both simulated and practical time-varying systems. Bat echolocation signal, train wheel vibration, and bridge vibration under a moving train are used to assess the proposed method. Comparison results show that the improved WVD is free of cross-terms, effective in identifying time-varying frequencies and is more accurate than the wavelet time–frequency spectrum.

Visualization of EEG Using Time-Frequency Distributions

1997 ◽  
Vol 36 (04/05) ◽  
pp. 298-301 ◽  
Author(s):  
B. Stiber ◽  
S. Sato
Keyword(s):  
Brain Activity ◽  
Temporal Structure ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Time Varying ◽  
Eeg Analysis ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Temporal Features ◽  
Time Varying Frequency

Abstract:The EEG is a time-varying or nonstationary signal. Frequency and amplitude are two of its significant characteristics, and are valuable clues to different states of brain activity. Detection of these temporal features is important in understanding EEGs. Commonly, spectrograms and AR models are used for EEG analysis. However, their accuracy is limited by their inherent assumption of stationarity and their trade-off between time and frequency resolution. We investigate EEG signal processing using existing compound kernel time-frequency distributions (TFDs). By providing a joint distribution of signal intensity at any frequency along time, TFDs preserve details of the temporal structure of the EEG waveform, and can extract its time-varying frequency and amplitude features. We expect that this will have significant implications for EEG analysis and medical diagnosis.

scholarly journals Instantaneous Modal Parameter Identification of Linear Time-Varying Systems Based on Chirplet Adaptive Decomposition

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jie Zhang ◽  
Zhiyu Shi
Keyword(s):  
Parameter Identification ◽  
Energy Analysis ◽  
Linear Time ◽  
Damping Ratio ◽  
Energy Concentration ◽  
Time Varying ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Time Frequency ◽  
Time Varying Systems

Instantaneous modal parameter identification of time-varying dynamic systems is a useful but challenging task, especially in the identification of damping ratio. This paper presents a method for modal parameter identification of linear time-varying systems by combining adaptive time-frequency decomposition and signal energy analysis. In this framework, the adaptive linear chirplet transform is applied in time-frequency analysis of acceleration response for its higher energy concentration, and the response of each mode can be adaptively decomposed via an adaptive Kalman filter. Then, the damping ratio of the time-varying systems is identified based on energy analysis of component response signal. The proposed method can not only improve the accuracy of instantaneous frequency extraction but also ensure the antinoise ability in identifying the damping ratio. The efficiency of the method is first verified through a numerical simulation of a three-degree-of-freedom time-varying structure. Then, the method is demonstrated by comparing with the traditional wavelet and time-domain peak method. The identified results illustrate that the proposed method can obtain more accurate modal parameters in low signal-to-noise ratio (SNR) scenarios.

scholarly journals Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2598 ◽  
Author(s):  
Xiaolin Ma ◽  
Running Zhao ◽  
Xinhua Liu ◽  
Hailan Kuang ◽  
Mohammed A. A. Al-qaness
Keyword(s):  
Software Radio ◽  
Human Motion ◽  
Frequency Resolution ◽  
Interference Signal ◽  
Motion Classification ◽  
Time Frequency ◽  
Cross Terms ◽  
Micro Motion ◽  
Human Motions ◽  
Human Motion Classification

Human motion classification based on micro-Doppler effect has been widely used in various fields. However, the motion classification performance would be greatly degraded if the wireless environment has non-target micro-motion interference. In this case, the interference signal aliases with the signal of target human motions and then generates cross-terms, making the signals hard to be used to identify target human motions. Existing methods do not consider this non-target micro-motion interference and have poor resistance to such interference. In this paper, we propose a target human motion classification system that can work in the scenarios with non-target micro-motion interference. Specifically, we build a continuous wave radar transceiver working in a low-frequency radar band using the software defined radio equipment Universal Software Radio Peripheral (USRP) N210 to collect signals. Moreover, we use Empirical Mode Decomposition and S-transform successively to remove non-target micro-motion interference and improve the time-frequency resolution of the raw signal. Then, an Energy Aggregation method based on S-method is proposed, which can suppress cross-terms and background noise. Furthermore, we extract a set of features and classify four human motions by adopting Bagged Trees. Extensive experiments using the test-bed show that under the scenarios with non-target micro-motion interference, 97.3% classification accuracy can be achieved.

scholarly journals Strain Sensitivity Enhancement of Broadband Ultrasonic Signals in Plates Using Spectral Phase Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2582
Author(s):  
Lucas M. Martinho ◽  
Alan C. Kubrusly ◽  
Nicolás Pérez ◽  
Jean Pierre von der Weid
Keyword(s):  
Fourier Transform ◽  
Guided Waves ◽  
Strain Level ◽  
Energy Concentration ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Fourier Transform ◽  
Short Time ◽  
Sensitivity Gain

The focused signal obtained by the time-reversal or the cross-correlation techniques of ultrasonic guided waves in plates changes when the medium is subject to strain, which can be used to monitor the medium strain level. In this paper, the sensitivity to strain of cross-correlated signals is enhanced by a post-processing filtering procedure aiming to preserve only strain-sensitive spectrum components. Two different strategies were adopted, based on the phase of either the Fourier transform or the short-time Fourier transform. Both use prior knowledge of the system impulse response at some strain level. The technique was evaluated in an aluminum plate, effectively providing up to twice higher sensitivity to strain. The sensitivity increase depends on a phase threshold parameter used in the filtering process. Its performance was assessed based on the sensitivity gain, the loss of energy concentration capability, and the value of the foreknown strain. Signals synthesized with the time–frequency representation, through the short-time Fourier transform, provided a better tradeoff between sensitivity gain and loss of energy concentration.

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