Autocorrelation Domain Research Articles

Homoepitaxial growth rate measurement and surface morphology monitoring of MOVPE-grown Si-doped (1 0 0) β-Ga2O3 thin films using in-situ reflectance spectroscopy

We investigate Si-doped β-Ga2O3 layers grown by metalorganic vapor phase epitaxy (MOVPE) on (100) β-Ga2O3 semi-insulating substrates with the reflection spectrum incident normally onto the substrate surface. The reflectance spectrum transformed into the autocorrelation domain shows a more pronounced Fabry-Pérot oscillation than the raw spectrum, from which one can easily estimate the growth rate and the growth mode based on the period, and the damping behavior of the spectrum, respectively. The observed oscillation contributes to the refractive index difference between the bulk substrate and the grown film caused by the incorporated impurities and different preparation techniques. The high sensitivity of the reflectance spectroscopy is demonstrated to be an advantage as a powerful growth process monitoring tool.

A Fast and Accurate Method for Parameter Estimation of Multi-Component LFM Signals

This letter presents a fast and accurate method for parameter estimation of multi-component linear frequency modulated (MC-LFM) signals. We first show that the auto-terms of signal in fractional auto-correlation (FrAc) domain are concentrated along lines passing through the origin with slopes equal to chirp rates. The connection between the FrAc and fractional Fourier transform (FrFT) is exploited to find the chirp rates. We then easily obtain central frequencies from the same FrFT coefficients which are used for the estimation of chirp rates. We finally propose our efficient algorithm to accelerate the precise estimate of the MC-LFM signal parameters. Simulation results validate performance of the approach.

Single-shot imaging through scattering media under strong ambient light interference.

Speckle correlation imaging (SCI) has found tremendous versatility compared with other scattering imaging approaches due to its single-shot data acquisition strategy, relatively simple optical setup, and high-fidelity reconstruction performance. However, this simplicity requires SCI experiments to be performed strictly in a darkroom condition. As background noise increases, the speckle contrast rapidly decreases, making precise interpretation of the data extremely difficult. Here, we demonstrate a method by refining the speckle in the autocorrelation domain to achieve high-performance single-shot imaging. Experiment results prove that our method is adapted to estimate objects in a low signal-to-background ratio (SBR) circumstance even if the SBR is about -23dB. Laboratory and outdoor SCI experiments are performed.

Deep-learning-based ciphertext-only attack on optical double random phase encryption

Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems. Learning-based attack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be attacked. Here, we propose a two-step deep learning strategy for ciphertext-only attack (COA) on the classical double random phase encryption (DRPE). Specifically, we construct a virtual DRPE system to gather the training data. Besides, we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks (DNNs) to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image. With these two trained DNNs at hand, we show that the plaintext can be predicted in real-time from an unknown ciphertext alone. The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system. Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

The Mkurtogram: A Novel Method to Select the Optimal Frequency Band in the AC Domain for Railway Wheelset Bearings Fault Diagnosis

A wheelset bearing is one of the main components of the train bogie frame. The early fault detection of the wheelset bearing is quite important to ensure the safety of the train. Among numerous diagnostic methods, envelope analysis is one of the most effective approaches in the detection of bearing faults which has been amply applied, but its validity greatly depends on the informative frequency band (IFB) determined. For the wheelset bearing faulty signal, it is often difficult to identify the IFB and extract fault characteristics due to the influence of complex operating conditions. To address this problem, a novel method to select optimal IFB, called the Mkurtogram, is proposed for railway wheelset bearings fault diagnosis. It takes the multipoint kurtosis (Mkurt) of unbiased autocorrelation (AC) of the squared envelope signal generated from sub-bands as assessment indicator for the first time. The fundamental concept which inspires this proposed method is to make full use of regular periodicity of AC of squared envelope signal. In the AC domain, the impulsiveness and periodicity, two distinctive signatures of the repetitive transients, have achieved a united representation by Mkurt. A simulated signal with multiple interferences and two experimental signals collected from wheelset bearings are applied to verify its performances and advantages. The results indicate that the proposed method is more effective to extract the wheelset bearings fault feature under complex interferences. It can not only decrease the influence of large impulse interference and the discrete harmonics interference, but also effectively overcome the influence of amplitude fluctuation caused by variable working conditions. Moreover, based on the periodic directivity of Mkurt, the proposed method also can be applied to the compound faults diagnosis of the wheelset bearing.

DOA estimation in the autocorrelation domain for coprime array

In this paper a new algorithm for direction of arrival (DOA) estimation in coprime arrays based on the autocorrelation domain (AD) method is proposed. The spatial autocorrelation sequence of the virtual uniform linear array (ULA) associated with the coprime array is built by averaging the diagonals of the spatially smoothed covariance matrix. The AD method estimates the DOAs by solving a generalized eigenvalue problem and refining the obtained solution. The main advantage of the introduced method is a spatial spectrum without false peaks. Simulation results indicate that the proposed approach is better than existing methods, especially for small number of snapshots.

ACSE Networks and Autocorrelation Features for PRI Modulation Recognition

Pulse repetition interval (PRI) modulation recognition plays an important role in electronic warfare. Conventional recognition methods based on handcrafted features and elaborate threshold values suffer from the accuracy for multiple PRI modulations at low signal-to-noise ratio (SNR) with high percentages of missing pulses. In this letter, a method based on Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks and features in autocorrelation domain is proposed to recognize six PRI modulation modes automatically. First, features in the time domain, frequency domain and autocorrelation domain are converted to images. Then the images are input into ACSE networks which can extract and learn deep features without complex data pre-processing. Finally, a linear layer will output modulation modes directly. Via simulations, robustness of autocorrelation features is proved. The simulation results also demonstrate that the proposed recognition method can achieve higher than 91% accuracy at -12 dB under normal conditions for six modulations and higher than 95% at -4 dB under extreme conditions. Compared with the conventional SVM and three CNN methods, ACSE networks outperform at low SNRs under extreme conditions.

An underwater bistatic positioning system based on an acoustic vector sensor and experimental investigation

The active detection of underwater targets is an important area of research and has numerous applications. In this paper, a bistatic detection and positioning system using an Acoustic Vector Sensor (AVS) is proposed which employs an improved Matched Filter (MF). The target position is determined using time delay and azimuth estimation. Frequency domain autocorrelation is employed to remove noise and improve detection performance. A direct path signal spatial suppression technique is proposed to decrease the effects of the dominant interference. Numerical simulation and experimental data obtained on a reservoir are used to evaluate the performance of the proposed system and methods. The results obtained show that this system is effective in determining the position of an underwater target.

Intra‐pulse modulation radar signal recognition based on CLDN network

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short-term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal-to-noise ratio (SNR) levels from -14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at -6 dB and close to 100% when SNR > -2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.

PRI Modulation Recognition Based on Squeeze-and-Excitation Networks

Pulse repetition interval (PRI) modulation recognition is a significant means to analyze radar working statuses and missions in Electronic Support system. Traditional methods may be insufficient to accurately recognize complex PRI at low SNR with high percentages of missing pulses. In this letter, an approach based on Squeeze-and-Excitation networks (SE-Net) and the autocorrelation functions for PRI modulation recognition automatically is proposed. Firstly, the features of six PRI modulation types in the autocorrelation domain are converted into images by calculating instantaneous autocorrelation functions. Then, the images will be the input of SE-Net which will automatically learn about deep features of different PRI modulation modes. Finally, SE-Net will output PRI modulation modes directly. Simulation results show that SE-Net is robust to the noise and missing pulses. The accuracies for all PRI modulation modes are more than 95% at −10dB with 30% missing pulses and more than 96% at −2dB with 50% missing pulses. Compared with traditional method and other networks, SE-Net can achieve better recognition performance at low SNR and high percentages of missing pulses.

Frequency Domain Method for Scattering Damping Time Extraction of a Reverberation Chamber Based on Autocorrelation Functions

The scattering damping time (τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> ) of a reverberation chamber is a very important parameter but hard to obtain accurately. A novel method for extracting this parameter is presented in this article. By utilizing the frequency domain autocorrelation function and the unstirred frequency domain autocorrelation function, τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> can be directly calculated from measured S-parameters without the need of performing the inverse Fourier transform (IFT). Mathematical derivation is given based on the Wiener-Khinchin theorem, and experimental measurements are conducted for different frequency bands as well as different stirrer configurations to verify the proposed approach. Compared with the conventional IFT-based method, the proposed method does not involve the procedure of manually selecting the fitting range, while still maintains advantages in terms of the independence of the radiation efficiency and input impedance of the measurement antennas.

Automatic Modulation Recognition for Radar Signals via Multi-Branch ACSE Networks

Automatic modulation recognition (AMR) for radar signals plays a significant role in electronic warfare. Conventional recognition methods may suffer from the recognition accuracy and the computation complexity under low signal-to-noise ratio (SNR) conditions. In this paper, a novel multi-branch Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks using multi-domain features and fusion strategy based on a support vector machine is proposed to recognize eight kinds of radar signals. First, features of radar signals in the frequency domain, the autocorrelation domain, and the time-frequency domain are extracted. Then the obtained multi-domain features are converted as the input of the proposed networks which owns the representational power and learning ability. Finally, the outputs of multi-branch ACSE networks are fused via the fusion strategy to obtain the final results. Via simulations, the robustness and effectiveness of the fusion strategy are verified. The results on the simulation dataset prove that the proposed method can achieve more than 93% accuracy at -10dB for all modulations. Compared with four newly proposed networks, the multi-branch ACSE networks achieves better performance under low SNR conditions. And the results on measured signals show that the proposed method outperforms other comparison methods, especially for binary frequency-shift keying (BFSK) signals.

A SINS‐aided two‐step fast acquisition method for GNSS signal based on compressive sensing

SummaryThe Global navigation satellite system (GNSS) multi‐mode compatible and multi‐frequency information not only improves information redundancy but also eliminates errors such as ionospheric delays to improve positioning performance. However, multi‐mode and multi‐frequency observation information increases the hardware overhead and computational complexity, especially the signal acquisition. In this contribution, according to the strong complementarity between GNSS and Strapdown inertial navigation system (SINS) and the sparsity of the GNSS signal spreading code in the autocorrelation domain, a SINS‐aided fast acquisition method for GNSS signals based on Compressive sensing (CS) is proposed. First, the inertia information of SINS narrows the Doppler frequency search range; then, the CS is applied to measure compressively the GNSS signal in two steps, ie, the first step is to narrow down the code phase search range, and the second step is to determine the code phase of the half chip accuracy. Finally, the proposed method and the other two typical acquisition methods are compared by the detection probability and the Mean acquisition time (MAT). Theoretical analysis and experimental results show that the proposed method can not only reduce the number of correlators and computational complexity but also improve the detection probability and reduce the MAT.

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms.

Improved generalized cross correlation-phase transform based time delay estimation by frequency domain autocorrelation

Improved generalized cross correlation-phase transform based time delay estimation by frequency domain autocorrelation

Primary Synchronization Signal Detection Algorithm in LTE-A

The time domain autocorrelation algorithm requires a long estimated time and has a poor detection timing performance in the case of low signal to noise ratio. The drawback of the sliding correlation algorithm is that the computation is large and the complexity is high. In order to solve the problems in the existing methods, a frequency domain convolution correlation algorithm is proposed. In order to simplify the received signal and the local signal, so that cross-correlation can be carried out in the frequency domain, this method makes full use of the relationship between crosscorrelation and convolution, and the characteristics of Fourier transform. Compared with the time domain sliding correlation algorithm, the frequency multiplication algorithm reduces the number of multiplication times 92.6%, the number of complex additions decreased by 95.3%. The simulation results show that the frequency domain convolution cross correlation algorithm is efficient, robust and feasible, can reduce the computational complexity, shorten the synchronization time, improve the detection efficiency and meet the synchronization performance requirements of the LTE system.

Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

Autocorrelation domain is a proper domain for clean speech signal and noise separation. In this paper, a method is proposed to decrease effects of noise on the clean speech signal, autocorrelation-based noise subtraction (ANS). Then to deal with the error introduced by assumption that noise and clean speech signal are uncorrelated, two methods are proposed. Also to improve recognition rate of speech recognition system, overestimation parameter is used. Finally, with the addition of energy and cepstral mean and variance normalization to features of speech, recognition rate has improved considerably in comparison to standard features and other correlation-based methods. The proposed methods are tested on the Aurora 2 database. Between different proposed methods, autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization (ANSSOEMV) method has a best recognition rate improvement in average than MFCC features which is 64.91% on the Aurora 2 database.

Bayesian Lattice Filters for Time-Varying Autoregression and Time–Frequency Analysis

Modeling nonstationary processes is of paramount importance to many scientific disciplines including environmental science, ecology, and finance, among others. Consequently, flexible methodology that provides accurate estimation across a wide range of processes is a subject of ongoing interest. We propose a novel approach to model-based time–frequency estimation using time-varying autoregressive models. In this context, we take a fully Bayesian approach and allow both the autoregressive coefficients and innovation variance to vary over time. Importantly, our estimation method uses the lattice filter and is cast within the partial autocorrelation domain. The marginal posterior distributions are of standard form and, as a convenient by-product of our estimation method, our approach avoids undesirable matrix inversions. As such, estimation is extremely computationally efficient and stable. To illustrate the effectiveness of our approach, we conduct a comprehensive simulation study that compares our method with other competing methods and find that, in most cases, our approach performs superior in terms of average squared error between the estimated and true time-varying spectral density. Lastly, we demonstrate our methodology through three modeling applications; namely, insect communication signals, environmental data (wind components), and macroeconomic data (US gross domestic product (GDP) and consumption).

Eigen Domain Interference Rejection Combining Algorithm for MIMO Systems

In this letter, we investigate an eigen domain interference rejection combining (E-IRC) algorithm for multiple-input–multiple-output (MIMO) systems suffering from narrowband interference (NBI). The received signals are transformed into the eigen domain, which is obtained by eigenvalue decomposition of the time domain autocorrelation matrix of the NBI, and the interference is rejected in the eigen domain via multiple antennas. V-BLAST and Alamouti schemes are considered in this letter. For the V-BLAST scheme, the E-IRC algorithm is an extension of the one for SIMO system. For the Alamouti scheme, we redesign the transmission codeword, which can preserve the orthogonality in the eigen domain, and propose an NBI rejection algorithm based on the spatial whitening filter.

Rapid and stable measurement of respiratory rate from Doppler radar signals using time domain autocorrelation model.

Noncontact measurement of respiratory rate using Doppler radar will play a vital role in future clinical practice. Doppler radar remotely monitors the tiny chest wall movements induced by respiration activity. The most competitive advantage of this technique is to allow users fully unconstrained with no biological electrode attachments. However, the Doppler radar, unlike other contact-type sensors, is easily affected by the random body movements. In this paper, we proposed a time domain autocorrelation model to process the radar signals for rapid and stable estimation of the respiratory rate. We tested the autocorrelation model on 8 subjects in laboratory, and compared the respiratory rates detected by noncontact radar with reference contact-type respiratory effort belt. Autocorrelation model showed the effects of reducing the random body movement noise added to Doppler radar's respiration signals. Moreover, the respiratory rate can be rapidly calculated from the first main peak in the autocorrelation waveform within 10 s.