Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

Parameter estimation of LFM signals based on time reversal

In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

this paper develops a real-time traffic signal timing model which is to be integrated into a single intersection for urban road, thereby solving the problem of traffic congestion. We analyze the current situation of the traffic flow with release matrix firstly, and then put forward the basic models to minimize total delay time of vehicles at the intersection. The optimal real-time signal timing model (non-fixed cycle and non-fixed split) is built with the Webster split optimal model. At last, the simulated results, which are compared with conventional model, manifest the promising properties of proposed model.

Cyclic-Auto-Correlation Based Timing Estimation Algorithm for Time-Frequency Overlapping Multi-Carrier Signals

In recent years,the time-frequency overlapping multi-carrier signal has been a novel and valuable topic in blind signal processing,especially in the non-cooperative receiving field.But there is little related research in public published papers.This paper proposes two timing estimation algorithms,which are non-data-aided and based on the cyclic auto-correlation function.In order to evaluate the performance of the proposed algorithms,the theoretical bound of the timing estimation is derived.According to the analyses and simulation results,the effectiveness of the proposed algorithms has been demonstrated.It shows that MethodⅠhas better performance than MethodⅡ.However,MethodⅡdoes not need prior information,so it has a wider range of applications.

Research on Instantaneous Angular Speed Signal Separation Method for Planetary Gear Fault Diagnosis

Planetary gear train is a critical transmission component in large equipment such as helicopters and wind turbines. Conducting damage perception of planetary gear trains is of great significance for the safe operation of equipment. Existing methods for damage perception of planetary gear trains mainly rely on linear vibration analysis. However, these methods based on linear vibration signal analysis face challenges such as rich vibration sources, complex signal coupling and modulation mechanisms, significant influence of transmission paths, and difficulties in separating damage information. This paper proposes a method for separating instantaneous angular speed (IAS) signals for planetary gear fault diagnosis. Firstly, this method obtains encoder pulse signals through a built-in encoder. Based on this, it calculates the IAS signals using the Hilbert transform, and obtains the time-domain synchronous average signal of the IAS of the planetary gear through time-domain synchronous averaging technology, thus realizing the fault diagnosis of the planetary gear train. Experimental results validate the effectiveness of the calculated IAS signals, demonstrating that the time-domain synchronous averaging technology can highlight impact characteristics, effectively separate and extract fault impacts, greatly reduce the testing cost of experiments, and provide an effective tool for the fault diagnosis of planetary gear trains.

Travel time function for basic link considering signal control in network traffic model

In order to describe the travel time of signalcontrolled roads, a travel time model for urban basic roads based on the cumulative curve is proposed. First, the traffic wave method is used to analyze the formation and dispersion of the vehicle queue. Cumulative curves for road entrances and exits are established. Based on the cumulative curves, the travel time of the one-lane road under stable flow input is derived. And then, the multi-lane road is decomposed into a series of single-lane links based on its topological characteristics. Hence, the travel time function for the basic road is obtained. The travel time is a function of road length, flow and control parameters. Numerical analyses show that the travel time depends on the supply-demand condition, and it has high sensitivity during peak hours.

Method for pedestrian signal timing at signalized intersections

The pedestrian timing at signalized intersections is studied aiming at the problems of the inconsistency of the vehicular and pedestrian timing requirements and the insufficiency of pedestrian clearance. Based on the formulae of WALK and flashing DON＇T WALK （FDW） in the highway capacity manual （HCM）, the relationship between pedestrian signal indications and vehicular signal indications is discussed using the theory of traffic flow. Then, methods of pedestrian timing for different cases are established, particularly the methods of the pedestrian green adjustment. Ways of pedestrian crossing are analyzed for roadways with different forms and widths of the median island. The sampling values of calculation parameters are studied, and the recommended formulae of pedestrian timing for different conditions are presented.

Research on High-Accuracy Programmable Timer Used in Acquiring UWB Signals

UWB signal digitization depends, to a large extent, on the accuracy of sampling time. A highaccuracy programmable timer is therefore the key to implementing UWB signal data acquisition. A high-accuracy programmable timer based on the principle of ramp generators is described in this paper. The counting range of the timer is up to 16 bits, the timing precision is 8 ps, and the equivalent sampling rate is up to 50G Hz. No other identical product has been reported so far. This timer was successfully used in the data acquisition system for geological radar signals developed by us.

SA SIGNAL TIME SERIES ANALYSIS

分析了进行GPS SA研究的必要性,选择Trimble公司的Model 4000RL GPS接收机于定点采集数据,通过定位计算和时钟偏移滤波相结合的方法分离SA信号,经检验指出SA信号基本是零均值的平稳随机过程,并具有ARMA(3,2)的结构,对200个模型参数的频域和时域的分析,认为所有模型都是稳定的,进行预测和滤波将是收敛的。

Traffic Signal Timing via Deep Reinforcement Learning

In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed. © 2014 Chinese Association of Automation.

Anti-aliasing nonstationary signals detecion algorithm based on interpolation in the frequency domain using the short time Fourier transform

To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform （STFT） is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.

Signal timing optimization model based on dual-ring phase scheme for roundabout

A signal optimization model for roundabout was control concept were used to eliminate the conflict points proposed based on dual-ring scheme and two stop lines for left turns and weaving sections at a roundabout. A cycle length minimization problem was considered to generate optimal signal timings for roundabout, and a set of constraints to ensure feasibility and safety of the resulting optimal signal settings were proposed. Extensive experimental analyses in comparison with signalized intersection reveal that the proposed model is quite promising for application in design of roundabout signals, and the minimum cycle length can decrease from 186 s to 79 s while the capacity increases from 8 682 pcu/h to 9 011 pcu/h under high demand scenario. Sensitivity analysis with respect to the system performance show that the lane assignment plan, number of circulatory lanes and left turn ratio are three critical factors which have dominate impacts on performance of signalized roundabout

RECONSTRUCTION OF ONE DIMENSIONAL MULTI-LAYERED MEDIA BY USING A TIME DOMAIN SIGNAL FLOW GRAPH TECHNIQUE

A novel inverse scattering method to reconstruct the permittivity profile of one-dimensional multi-layered media is proposed in this paper.Based on the equivalent network ofthe medium,a concept of time domain signal flow graph and its basic principles are introduced,from which the reflection coefficient of the medium in time domain can be shown to be a series ofDirac δ-functions(pulse responses).In terms of the pulse responses,we will reconstruct both thepermittivity and the thickness of each layer will accurately be reconstructed.Numerical examplesverify the applicability of this

Impact of GPS Almanac Broadcast Strategy on the Signal Acquisition Time

This paper investigates the problem of almanac affecting the signal acquisition time with two constraints: different age of data and multi-sets of almanac. The contributions made in this paper include: 1) the exploiting of signal acquisition concept to extend well-known almanac function of predicting visible satellite and initializing signal acquisition to minimizing the signal acquisition time; 2) a model based on code phase and Doppler frequency to reflect the impact of multi-sets of almanac on the signal acquisition time; 3) the evaluation of the existing GPS almanac with different broadcast strategy. The theoretical analyses and simulations conducted on three sets of almanac show that the model proposed in this paper is general and efficient for almanac design and application.

Extracting useful high-frequency information from wide-field electromagnetic data using time-domain signal reconstruction

The wide-field electromagnetic method is widely used in hydrocarbon exploration,mineral deposit detection,and geological disaster prediction.However,apparent resistivity and normalized field amplitude exceeding 2048 Hz often exhibit upward warping in data,making geophysical inversion and interpretation challenging.The cumulative error of the crystal oscillator in signal transmission and acquisition contributes to an upturned apparent resistivity curve.To address this,a high-frequency information extraction method is proposed based on time-domain signal reconstruction,which helps to record a complete current data sequence;moreover,it helps estimate the crystal oscillator error for the transmitted signal.Considering the recorded error,a received signal was corrected using a set of reconstruction algorithms.After processing,the high-frequency component of the wide-field electromagnetic data was not upturned,while accurate high-frequency information was extracted from the signal.Therefore,the proposed method helped effectively extract high-frequency components of all wide-field electromagnetic data.

High Accuracy Time of Flight Measurement Using Digital Signal Processing Techniques for Subsea Applications

The techniques widely used in ultrasonic measurements are based on the determination of the time of flight (T.o.F). A short train of waves is transmitted and same transducer is used for reception of the reflected signal for the pulse-echo measurement applications. The amplitude of the received waveform is an envelope which starts from zero reaches to a peak and then dies out. The echoes are mostly detected by simple threshold crossing technique, which is also cause of error. In this paper digital signal processing is used to calculate the time delay in reception i.e. T.o.F, for which a maximum similarity between the reference and the delayed echo signals is obtained. To observe the effect of phase uncertainties and frequency shifts (Doppler), this processing is carried out, both directly on the actual wave shape and after extracting the envelopes of the reference and delayed echo signals. Several digital signal processing algorithms are considered and the effects of different factors such as sampling rate, resolution of digitization and S/N ratio are analyzed. Result show accuracy, computing time and cost for different techniques.

The application of breast DCE-MRI combined with time signal curve in diagnosing early breast cancer

Objective： The aim of this study was to investigate the application value of breast dynamic contrast-enhanced magnetic resonance imaging combined with time signal curve in diagnosis of early breast cancer. Methods： Conducted dynamic contrast-enhanced MRI and drew the time signal curves of breast lesions in 60 patients with breast disease （malignant 46, benign 14）. Results： Morphological features of malignant tumors mostly showed blurred or thin spiculate outlines, irregular shape or Iobular signs, signal heterogeneity or peripheral enhancement in dynamic contrast-enhanced MRI. Time signal curve showed type III or II. Morphologic features of benign tumors mostly showed clear edge, regular shape and homogeneous signal, or diffuse spot enhancement. Time signal curve showed type I or II. Conclusion： breast dynamic contrast enhanced scan in MRI can provide morphology and functional diagnosis information of the breast tissues. Dynamic contrast-enhanced MRI combined with time signal curve can further improve the accuracy of diagnosis of early breast cancer.

Cell transmission model based traffic signal timing in oversaturated conditions

In order to investigate enhancements to cell transmission model (CTM) as a tool for traffic signal timing in oversaturated conditions, randomly distributed saturation flow rates and arrival rates were used instead of constant values to simulate traffic flow movement, estimate the average delay of the network and search for an optimal traffic signal timing plan. A case study was given to demonstrate that the proposed methodology can capture unique phenomena in oversaturated conditions such as forward wave, spillback and lane entrance blockage. The results show that CTM underestimates travel time by 25% when compared to Simtraffic, while the enhanced CTM underestimates by only 3%. A second case study shows that a dynamic signal timing plan is superior to a fixed signal timing plan in the term of average delay.

Effect on Intensity Correlation Time by Input Signal in a Single-Mode Laser with Bias Signal Modulation

The effect on intensity correlation time T by input signal is studied for gain-noise model of a single-mode laser driven by colored pump noise and colored quantum noise with colored cross-correlation with a bias signal modulation in this paper. By using the linear approximation method, we detect that there exists maximum （i.e., resonance） in the curve of the intensity correlation time T upon bias current io when the noise correlation coefficient λ is positive; and there exists minimum （i.e., suppression） in the T-io curve when λ is negative. And whenλ is zero, T increases monotonously with increasing io. Furthermore, the curve of T upon the signal frequency Ω is also studied. Our study shows that no matter what the value of λ is, there exists minimum （i.e., suppression） in the T-Ω curve.