Time-frequency Distribution Of Signal Research Articles

Sparse Time-Frequency Distribution Reconstruction Using the Adaptive Compressed Sensed Area Optimized with the Multi-Objective Approach.

Compressive sensing (CS) of the signal ambiguity function (AF) and enforcing the sparsity constraint on the resulting signal time-frequency distribution (TFD) has been shown to be an efficient method for time-frequency signal processing. This paper proposes a method for adaptive CS-AF area selection, which extracts the magnitude-significant AF samples through a clustering approach using the density-based spatial clustering algorithm. Moreover, an appropriate criterion for the performance of the method is formalized, i.e., component concentration and preservation, as well as interference suppression, are measured utilizing the information obtained from the short-term and the narrow-band Rényi entropies, while component connectivity is evaluated using the number of regions with continuously-connected samples. The CS-AF area selection and reconstruction algorithm parameters are optimized using an automatic multi-objective meta-heuristic optimization method, minimizing the here-proposed combination of measures as objective functions. Consistent improvement in CS-AF area selection and TFD reconstruction performance has been achieved without requiring a priori knowledge of the input signal for multiple reconstruction algorithms. This was demonstrated for both noisy synthetic and real-life signals.

Method of Automatic Parameters Estimation of Radio Signals with Frequency-Hopping Spread Spectrum

Method of automatically estimation of frequency elements duration, first hop beginning time, last hop ending time, the number and nominals of target group frequencies and the bandwidth of radio signal with frequency-hopping spread spectrum in the presence of durable extraneous emissions in the frequency range of receiver is proposed in the article. The method is based on the analysis of the time-frequency distribution obtained using the Welch window transformation and consists of three main stages: extraneous emissions detection, time parameters estimation and frequency parameters estimation of frequency-hopping spread spectrum. The detection of extraneous emissions is carried out according to the time criterion using an iterative approach. On the basis of estimated emissions frequency parameters band-rejection filters are formed, which central frequencies are equal to the central interferences frequencies and stopband are equal to emissions spectrum width. The estimation of time and frequency parameters of the radio signal is carried out by analyzing the signal time-frequency distribution, taking into account the characteristics of the band-rejection filters.To exclude from the calculation of the parts of the signal containing harmonics associated with the change in the values of frequency elements, the time windows of the time-frequency distribution are limited and aligned with the centers of hops. An approach to the formation of operating frequencies grid using the statistical criterion and base frequencies, calculated by minimizing the sum of the differences between the current nominal frequency elements and the frequency grid step is proposed. An algorithm for calculation the last hop ending time based on a joint analysis of the current values of the central frequencies and the energies of the frequency elements is developed. The results of testing the operability and efficiency of the developed method by modeling in the MATLAB software environment with a signal-to-noise ratio of -25 dB are given.

Automatic Data-Driven Frequency-Warped Cepstral Feature Design for Micro-Doppler Classification

Micro-Doppler signature analysis and speech processing share a common approach as both rely on the extraction of features from the signal's time-frequency distribution for classification. As a result, features, such as the mel-frequency cepstrum coefficients (MFCCs), which have shown success in speech processing, have been proposed for use in micro-Doppler classification. MFCCs were originally designed to take into account the auditory properties of the human ear by filtering the signal using a filter bank spaced according to the mel-frequency scale. However, the physics underlying radar micro-Doppler is unrelated to that of human hearing or speech. This work shows that the mel-scale filter bank results in the loss of frequency components significant to the classification of radar micro-Doppler. A novel method for frequency-warped cepstral feature design is proposed as a means for optimizing the efficacy of features in a data-driven fashion specifically for micro-Doppler analysis. It is shown that the performance of the proposed frequency warped cepstral coefficients outperforms MFCC based on both simulated and measured data sets for four-class and eight-class human activity classification problems.

A new synthetic detection technique for trackside acoustic identification of railroad roller bearing defects

The detection of railroad roller bearing defects is of great importance for railway traffic. The acoustic defective bearing detector (ADBD) via a trackside acoustic detection system (TADS) is a popular and reliable tool for trackside identification of railroad bearing incipient defects. However, the defective trackside acoustic signal of bearing is confronted with three challenges: demodulation of acoustic signal, removal of Doppler effect and enhancement of defect frequency. This paper proposes a synthetic detection technique to overcome these difficulties successively. In the technique, the signal envelope is first extracted by a new variable-resolution ridge demodulation (VRRD) method. Then the Doppler effect is removed in the envelope via a dynamic signal resampling method. Finally, the rectified defect frequency is enhanced through the means of logarithmic transformation and piecewise linear detrending. The VRRD technique extracts the envelope from the signal time–frequency distribution (TFD) along the Doppler shift curve of resonance frequency traced by an improved ridge extraction algorithm. The resampling method is carried out on the envelope according to the Doppler shift curve of defect frequency obtained by the same ridge extraction algorithm. The sidebands around defect frequency can be suppressed by the logarithmic transformation and the low-frequency non-linear trend can be removed by the detrending method. The signal amplitude is unified at the same time. Two practical bearing signals with inner-race and outer-race defects separately verify the effectiveness of proposed technique.

Single-channel source separation of radar fuze mixed signal using advanced adaptive decomposition

An advanced method of signal decomposition-advanced adaptive chirplet decomposition(AACD) is presented to separate the radar fuze mixed signal in single-channel. The method adds curvature factor to the adaptive Gauss chirplet decomposition (AGCD), thus it can be used to decompose nonlinear time varying components. It uses the fast algorithm and trust region method to optimize arithmetic process, then all the parameters can be solved accurately and quickly, and the mixed signal's time-frequency distribution without cross terms is obtained. In the simulation, the advanced method is used to improve the time-frequency distribution of complex mixed signals, then it can separate all the components and calculate the mixing coefficients. Results indicate that the proposed method has higher effectiveness and strong performance of anti-noise

The Time-Frequency Analysis Method Based on EMD White Noise Energy Density Distribution Characteristics of the Internal Combustion Engine Vibration

For the limitations of HHT of the internal combustion engine vibration signal analysis, and the problem of WVD cross-term suppression methods existing aggregation and cross-term component suppression conflicting, the time-frequency analysis method based on EMD white noise energy density distribution characteristics of the internal combustion engine vibration is proposed. First, the internal combustion engine vibration signal was decomposed into the independent series intrinsic mode function (IMF) with different characteristic time scales by using EMD decomposition method. Then, based on the energy density distribution characteristics of the white noise in EMD decomposition, used the distribution interval estimation curve of the IMFs energy density logarithm of white noise with the same length of the original signal as cordon for false pattern component, identified and eliminated false mode component of vibration signal IMFs component, analysised of each IMF with Wigner-Ville. Finally, the Wigner-Ville analysis results of each IMF were linear superposed in order to reconstruct the original signal time-frequency distribution. Simulation and engine vibration time-frequency analysis results show that this method has an excellent time-frequency characteristics, and can successfully extract feature information of the internal combustion engine cylinder head vibration signal.

Reservoir information extraction using a fractional fourier transform and a smooth pseudo Wigner-Ville distribution

Currently, it is difficult for people to express signal information simultaneously in the time and frequency domains when analyzing acoustic logging signals using a simple-time or frequency-domain method. It is difficult to use a single type of time-frequency analysis method, which affects the feasibility of acoustic logging signal analysis. In order to solve these problems, in this paper, a fractional Fourier transform and smooth pseudo Wigner-Ville distribution (SPWD) were combined and used to analyze array acoustic logging signals. The time-frequency distribution of signals with the variation of orders of fractional Fourier transform was obtained, and the characteristics of the time-frequency distribution of different reservoirs under different orders were summarized. Because of the rotational characteristics of the fractional Fourier transform, the rotation speed of the cross terms was faster than those of primary waves, shear waves, Stoneley waves, and pseudo Rayleigh waves. By choosing different orders for different reservoirs according to the actual circumstances, the cross terms were separated from the four kinds of waves. In this manner, we could extract reservoir information by studying the characteristics of partial waves. Actual logging data showed that the method outlined in this paper greatly weakened cross-term interference and enhanced the ability to identify partial wave signals.

Modernized method of estimating the parameters of LFM signals based on the time-frequency distribution correction and the use of the Hough transformation

The method has been modernized for obtaining the parameter estimation of the fine structure of LFM signals (linear-frequency-modulated signals) at small values of the signal-to-noise ratio. The development of this method was based on the analysis of the signal time-frequency distribution (TFD) and the Hough transform. The specific feature of this method is correction of the time-frequency parameters in the TFD image considered and the use of the principle of detecting the straight line by the Hough transform that allows us to obtain estimates of parameters of LFM signals from the radio signal received within a shorter time interval at small signal-to-noise ratios. The results of simulation modeling demonstrate the capabilities of the method proposed.

Efficient computation of joint fractional Fourier domain signal representation

A joint fractional domain signal representation is proposed based on an intuitive understanding from a time-frequency distribution of signals that designates the joint time and frequency energy content. The joint fractional signal representation (JFSR) of a signal is so designed that its projections onto the defining joint fractional Fourier domains give the modulus square of the fractional Fourier transform of the signal at the corresponding orders. We derive properties of the JFSR, including its relations to quadratic time-frequency representations and fractional Fourier transformations, which include the oblique projections of the JFSR. We present a fast algorithm to compute radial slices of the JFSR and the results are shown for various signals at different fractionally ordered domains.

Internal Combustion Engine Noise Analysis With Time-Frequency Distribution

An analysis procedure, using the time-frequency distribution, has been developed for the analysis of internal combustion engine noise signals. It provides an approach making use of advantages of both the linear time-frequency distribution and the bilinear time-frequency distribution but avoiding their disadvantages. In order to identify requirements on the time-frequency analysis and also correlate a time-frequency analysis result with noise sources, the composition of the noise signal is discussed first. With this discussion, a mathematical model has been suggested for the noise signal. An example of identifying noise sources and detecting the abnormal condition of an injector with the noise signal time-frequency distribution for a diesel engine is also provided.