AoA-Based Channel Estimation for Massive MIMO OFDM Communication Systems on High Speed Rails

Channel estimation is a well-known challenge for wireless orthogonal frequency division multiplexing(OFDM)communication systems with massive antennas on high speed rails(HSRs).This paper investigates this problem and design two practicable uplink and downlink channel estimators for orthogonal frequency division multiplexing(OFDM)communication systems with massive antenna arrays at base station on HSRs.Specifically,we first use pilots to estimate the initial angle of arrival(AoA)and channel gain information of each uplink path through discrete Fourier transform(DFT),and then refine the estimates via the angle rotation technique and suggested pilot design.Based on the uplink angel estimation,we design a new downlink channel estimator for frequency division duplexing(FDD)systems.Additionally,we derive the Cramér-Rao lower bounds(CRLBs)of the AoA and channel gain estimates.Finally,numerical results are provided to corroborate our proposed studies.

Estimation of Vehicle Speed Based on Wheel Speeds from ASR System in Four-Wheel Drive Vehicles

Three major methods currently in the use of determining vehicle speed based on wheel speeds, the minimum wheel speed, minimum wheel speed corrected by slope method and the Kalman filter method, are analyzed, with merits and defects of each approach stated. Through simulations, the Kalman filter method based on minimum wheel speed shows improved accuracy, in addition to better adaptivity to vehicle reference speed. It also can be used to acceleration ship regulation （ASR） in part-time four-wheel drive vehicles.

DC Motor Speed Regulator via Active Damping Injection and Angular Acceleration Estimation Techniques

This paper suggests a novel model-based nonlinear DC motor speed regulator without the use of a current sensor.The current dynamics,machine parameters and mismatched load variations are considered.The proposed controller is designed to include an active damping term that regulates the motor speed in accordance with the first-order low-pass filter dynamics through the pole-zero cancellation.Meanwhile,the angular acceleration and its reference are obtained from simple first-order estimators using only the speed information.The effectiveness is experimentally verified using hardware comprising the QUBEServo2,myRIO-1900,and LabVIEW.

Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm installation.In general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric conditions.The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower heights.The first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno algorithm.Secondly,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m heights.The estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m height.This procedure is repeated continuously until theWS is estimated at a height of 180 m.The RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS vertically.The estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively.

Speed Estimation Using Commercial Wi-Fi Device in Smart Home

With the development of Internet of Things(IoT),the speed estimation technology has attracted significant attention in the field of indoor security,intelligent home and personalized service.Due to the indoor multipath propagation,the speed information is implicit in the motion-induced reflected signal.Thus,the wireless signal can be leveraged to measure the speed of moving target.Among existing speed estimation approaches,users need to either carry a specialized device or walk in a predefined route.Wi-Fi based approaches provide an alternative solution in a device-free way.In this paper,we propose a direction independent indoor speed estimation system in terms of Electromagnetic(EM)wave statistical theory.Based on the statistical characteristics of EM waves,we establish the deterministic relationship between the Autocorrelation Function(ACF)of Channel State Information(CSI)and the speed of a moving target.Extensive experiments show that the system achieves a median error of 0.18 m/s for device-free single target walking speed estimation.

Real-Time Traffic State and Boundary Flux Estimation with Distributed Speed Detecting Networks

The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.

Application of Neuro-Fuzzy Estimation for Rotor Flux in Speed Vector Control of Bearingless Induction Motor

The main requirement of a vector controller is knowing the magnitude and position of the rotating flow in the rotor. This feature permits to use either flow sensors or flow estimators. The solution chosen was the estimation of rotor flux with the hybrid neuro-fuzzy system. The motor characteristics are： 3.75 kW （5 HP）, two pole-pair, operate at 60 Hz and air-gap length 0.2 mm. The ANFIS （adaptive neuro-fuzzy inference system） was used to tune the membership functions in fuzzy system. The hybrid estimator aims at compensating possible parametric variations of the machine caused by agents, such as temperature or nucleus saturation. The simulated results have shown good performance.

A novel speed sensor-less direct torque control system for mining locomotive haulage

A novel speed sensor-less direct torque control induction motor drive system for the mining locomotive haulage is presented in the paper. Rotor speed identification is based on the model reference adaptive control theory with neural network using back propagation algorithm. The system is implemented using a real-time TMS320F240 digital signal processor. The simulation study and experiment results indicate that the suggested system has good performance.

A Sensor-less Surface Mounted PMSM for Electronic Speed Control in Multilevel Inverter

Recent advancements in power electronics technology evolves inverter fed electric motors.Speed signals and rotor position are essential for controlling an electric motor accurately.In this paper,the sensorless speed control of surface-mounted permanent magnet synchronous motor(SPMSM)has been attempted.SPMSM wants a digital inverter for its precise working.Hence,this study incor-poratesfifteen level inverter to the SPMSM.A sliding mode observer(SMO)based sensorless speed control scheme is projected to determine rotor spot and speed of the multilevel inverter(MLI)fed SPMSM.MLI has been operated using a multi carrier pulse width modulation(MCPWM)strategy for generation offif-teen level voltages.The simulation works are executed with MATLAB/SIMU-LINK software.The steadiness and the heftiness of the projected model have been investigated under no loaded and loaded situations of SPMSM.Furthermore,the projected method can be adapted for electric vehicles.

Electromagnetic Torque Estimation of a Three-Phase Induction Motor for Setting the Feedrate of a Milling Machine

In this paper, it presents a project of a fuzzy controller and a neural estimator to control a coordinate table powered by three-phase induction motor, aiming to implement an intelligent milling system. The position/speed control is performed using vector techniques of three-phase induction machines. The estimation of the motor electromagnetic torque is used for setting the feedrate of the table. The speed control is developed using TS （Takagi-Sugeno） fuzzy logic model and electromagnetic torque estimation using neural network type LMS （least mean square） algorithm. The induction motor is powered by a frequency inverter driven by a DSP （digital signal processor）. Control strategies are implemented in DSP. Simulation results are presented for evaluating the performance of the system.

Motion visualization and estimation for flapping wing systems

Studies of fluid-structure interactions associated with flexible structures such as flapping wings require the capture and quantification of large motions of bodies that may be opaque. As a case study, motion capture of a free flying Manduca sexta, also known as hawkmoth, is considered by using three synchronized high-speed cameras. A solid finite element (FE) representation is used as a reference body and successive snapshots in time of the displacement fields are reconstructed via an optimization procedure. One of the original aspects of this work is the formulation of an objective function and the use of shadow matching and strain-energy regularization. With this objective function, the authors penalize the projection differences between silhouettes of the captured images and the FE representation of the deformed body. The process and procedures undertaken to go from high-speed videography to motion estimation are discussed, and snapshots of representative results are presented. Finally, the captured free-flight motion is also characterized and quantified.

Performance and Improvement of MCS Selection with Channel Quality Estimation Errors for HSDPA

The throughput performance of modulation and coding schemes （MCS） selection with channel quality estimation errors （CQEE） is analyzed for high-speed downlink packet access （HSDPA）. To reduce the loss of throughput caused by CQEE, the robust MCS selection method and adaptive MCS switching scheme are proposed. In addition, automatic repeat request （ARQ） scheme is used to improve the block error rate （BLER） performance. Simulation results show that the proposed methods decrease the throughput loss resulted from CQEE efficiently and BLER performance gets better with ARQ scheme.

Speed Sensorless Vector Control of Induction Motor Based on Reduced Order Extended Kalman Filter

A speed sensorless vector control system of induction motor with estimated rotor speed and rotor flux using a new reduced order extended Kalman filter is proposed. With this method, two rotor flux components are selected as the state variables, and the rotor speed as an estimated parameter is regarded as an augmented state variable. The algorithm with reduced order decreases the computational complexity and makes the proposed estimator feasible to be implemented in real time. The simulation results show high accuracy of the estimation algorithm and good performance of speed control, and verify the usefulness of the proposed algorithm.

Adaptive Gain Tuning Rule for Nonlinear Sliding-mode Speed Control of Encoderless Three-phase Permanent Magnet Assisted Synchronous Motor

In this paper, an adaptive gain tuning rule is designed for the nonlinear sliding mode speed control(NSMSC) in order to enhance the dynamic performance and the robustness of the permanent magnet assisted synchronous reluctance motor(PMa-Syn RM) with considering the parameter uncertainties. A nonlinear sliding surface whose parameters are altering with time is designed at first. The proposed NSMSC can minimize the settling time without any overshoot via utilizing a low damping ratio at starting along with a high damping ratio as the output approaches the target set-point. In addition, it eliminates the problem of the singularity with the upper bound of an uncertain term that is hard to be measured practically as well as ensures a rapid convergence in finite time, through employing a simple adaptation law. Moreover, for enhancing the system efficiency throughout the constant torque region, the control system utilizes the maximum torque per ampere technique. The nonlinear sliding surface stability is assured via employing Lyapunov stability theory. Furthermore, a simple sliding mode estimator is employed for estimating the system uncertainties. The stability analysis and the experimental results indicate the effectiveness along with feasibility of the proposed speed estimation and the NSMSC approach for a 1.1-k W PMa-Syn RM under different speed references, electrical and mechanical parameters disparities, and load disturbance conditions.

The Joint Control of Variable Speed and On-Ramp Metering for Urban Expressway

The traffic performance of urban expressway is subject to non-recurring and recurring events, which may cause heavy congestion and vehicles long queuing on ramps. The low performance may bring more traffic delay to the whole network of urban road. This paper presents a new method, the joint control of variable speed control and on-ramp metering, which attempts to improve the level of traffic operations on urban expressway. By analyzing traffic flow on urban expressway, an optimum control strategy of variable speed and on-ramp metering is established in the paper.

TWO-DIMENSIONAL VISUALIZATION OF THE PROPAGATION SPEED OF CORTICAL SPREADING DEPRESSION IN RAT CORTEX

Cortical spreading depression(CSD),which is a significant pathological phenomenon that correlates with migraines and cerebral ischemia,has been characterized by a wave of depolarization among neuronal cells and propagates across the cortex at a rate of 2–5mm/min.Although the propagation pattern of CSD was well-investigated using high-resolution optical imaging technique,the variation of propagation speed of CSD across different regions of cortex was not well-concerned,partially because of the lack of ideal approach to visualize two-dimensional distribution of propagation speed of CSD over the whole imaged cortex.Here,we have presented a method to compute automatically the propagation speed of CSD throughout every spots in the imaged cortex.In this method,temporal clustering analysis(TCA)and least square estimation(LSE)were first used to detect origin site where CSD was induced.Taking the origin site of CSD as the origin of coordinates,the data matrix of each image was transformed into the corresponding points based on the polar-coordinate representation.Then,two fixed-distance regions of interest(ROIs)are sliding along with the radial coordinate at each polar angle within the image for calculating the time lag with correlating algorithm.Finally,we could draw a twodimensional image,in which the value of each pixel represented the velocity of CSD when it spread through the corresponding area of the imaged cortex.The results demonstrated that the method can reveal the heterogeneity of propagation speed of CSD in the imaged cortex with high fidelity and intuition.

基于多通道数据的高铁司机警觉评估实验设计

高铁司机警觉与行车安全密切相关。为实现司机警觉状态评估,该研究设计了一种基于多通道数据的高铁司机警觉评估实验方案。依托高铁驾驶模拟器,以连续驾驶的方式诱发高铁司机警觉衰退,实验过程中同步采集其脑电、心电、眼动以及简单反应时间和疲劳程度主观量表。基于神经网络、支持向量机等机器学习技术构建司机警觉评估模型技术方案。以司机神经、生理等融合特征为输入,以司机疲劳状态为输出,构建司机警觉评估模型,对提出的实验方案进行探讨,验证提出的实验方案的合理性与可应用性。

基于地震P波初至2 s功率信息的高速铁路地震预警系统快速震级估算方法

震级估算是高速铁路地震预警系统的关键技术参数,更加快速、准确的震级估算模型能够更好地保障人民的生命财产安全。利用日本K-net强震动数据,建立基于地震P波到达后携带功率信息的地震动衰减模型并用于震级估算,并将本文算法与传统算法对比。结果表明,在1~5 s的时间窗下,模型的震级估算误差均明显小于传统的方法,且小震高估现象也明显改善;随着时窗长度的增加,模型的震级估算误差及标准差也逐渐降低,实现率也逐步提升,在1 s的时窗长度下,模型的单台震级估算实现率满足规范要求;对7级以下地震,模型的震级估算实现率可在地震P波到达1 s后满足规范要求;对7级以上地震,模型的震级估算实现率可以在地震P波到达2 s后满足规范要求。

一种自适应扩展卡尔曼滤波的永磁同步电机无位置传感器矢量控制

在粗差干扰或噪声统计偏差情况下,扩展卡尔曼滤波(EKF)对永磁同步电机(PMSM)的速度和转子位置估计存在精度下降问题,为此提出一种基于新息序列的自适应扩展卡尔曼滤波算法(AEKF)。首先,将粗差干扰加入系统观测方程中,分析粗差干扰对系统观测精度的影响。其次,为增强算法的抗扰性能,在新息协方差计算中设置加权系数,通过调整临近时刻的新息协方差阵权重,计算出新息协方差值,并更新到卡尔曼增益的计算。最后,建立AEKF数学模型,并对比粗差干扰与噪声统计出现偏差情况下,AEKF与EKF两种策略的观测性能。仿真和实验结果表明,在粗差干扰或噪声统计信息出现偏差情况下,AEKF算法对永磁同步电机转速的观测具备更强的鲁棒性及更高的预测精度。