FM interference suppression for PRC-CW radar based on adaptive STFT and time-varying filtering

The influence of frequency modulation （FM） interfer- ence on correlation detection performance of the pseudo random code continuous wave （PRC-CW） radar is analyzed. It is found that the correlation output deteriorates greatly when the FM inter- ference power exceeds the anti-jamming limit of the radar. Accord- ing to the fact that the PRC-CW radar echo is a wideband pseudo random signal occupying the whole TF plane, while the FM in- terference only concentrates in a small portion, a new method is proposed based on adaptive short-time Fourier transform （STFT） and time-varying filtering for FM interference suppression. This method filters the received signal by using a binary mask to excise only the portion of the TF plane corrupted by the interference. Two types of interference, linear FM （LFM） and sinusoidal FM （SFM）, under different signal-to-jamming ratio （S JR） are studied. It is shown that the proposed method can effectively suppress the FM interference and improve the performance of target detection.

Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Fault detection filter design for linear discrete time-varying systems with multiplicative noise

The problem of fault detection for linear discrete timevarying systems with multiplicative noise is dealt with.By using an observer-based robust fault detection filter（FDF） as a residual generator,the design of the FDF is formulated in the framework of H ∞ filtering for a class of stochastic time-varying systems.A sufficient condition for the existence of the FDF is derived in terms of a Riccati equation.The determination of the parameter matrices of the filter is converted into a quadratic optimization problem,and an analytical solution of the parameter matrices is obtained by solving the Riccati equation.Numerical examples are given to illustrate the effectiveness of the proposed method.

H_-/H_∞ fault detection filter design for interval time-varying delays switched systems

The problem of the robust fault detection filter design for time-varying delays switched systems is considered in the framework of mixed H-/H∞. Firstly, the weighted H∞ performance index is utilized as the robustness performance, and the H- index is used as the sensitivity performance for obtaining the robust fault detection filter. Then a novel multiple Lyapunov-Krasovskii function is proposed for deriving sufficient existence conditions of the robust fault detection filter based on the average dwell time technique. By introducing slack matrix variable, the coupling between the Lyapunov matrix and system matrix is removed, and the conservatism of results is reduced. Based on the robust fault detection filter, residual is generated and evaluated for detecting faults. In addition, the results of this paper are dependent on time delays,and represented in the form of linear matrix inequalities. Finally,the simulation example verifies the effectiveness of the proposed method.

Network-Based H_∞ Filtering for Linear Systems with Randomly Varying Sensor Delay

An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying Bernoulli random binary distribution is introduced and a new system model is established by employing the measurements with random delay.By using the linear matrix inequality(LMI) technique,sufficient conditions are derived for ensuring the mean-square stochastic stability of the filtering error systems and guaranteeing a prescribed H∞ filtering performance.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approach.

Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit

In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a “soft-cutter” that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.

TWO APPROACHES TO THE DESIGN OF TIME-VARYING CASCADED FILTERS

Two approaches to the design of time-varying cascaded filters used in radar clutter rejection are presented. In the first approach, by fitting the cascaded filter to the noncascaded filter, the time-varying cascaded filter can be designed, which makes it possible that the time-varying cascaded filter behaves just like an optimum clutter filter. The second approach can be used to design the second-stage filter in a time-varying cascaded one by setting zeros in its equivalent overall frequency response. It has been shown that it is difficult to express the frequency response of the second-stage filter in the time-varying cascaded one, however, it is convenient to be involved in the overall response.

TIME-VARYING AR MODELING AND ADAPTIVE IIR NOTCH FILTER FOR ANTI-JAMMING DSSS RECEIVER

Using Time-Varying AR (TVAR) model and adaptive notch filter is a new method for the non-stationary jammer suppression in Direct Sequence Spread Spectrum (DSSS). The performance of TVAR model for Instantaneous Frequency (IF) estimation will be affected by some factors such as basis functions. Focusing on this problem, the optimal basis function of TVAR model for the IF estimation of the LFM signal is obtained in this paper. Besides the depth and width of notching, the phase properties of notch filter affect the Signal-to-Interference plus-Noise Ratio (SINR) of correlation output to the narrowband jammer suppression in DSSS, in response to the problem the closed solution of correlation output SINR improvement has been derived when a single frequency jammer passes through direct IIR notch filter, and its performance has been compared with those of five coefficient FIR filters. Later, a novel method for LFM jammer suppression based on Fourier basis TVAR model and direct IIR notch filter is proposed. The simulation results show the effectiveness of the proposed method.

基于LSTM-CAPF框架的岸桥起升减速箱轴承寿命预测方法

岸桥起升减速箱轴承的健康状况对港口生产安全具有重要意义.针对岸桥变工况的工作条件,提出一种起升减速箱轴承的剩余使用寿命(RUL)预测框架.首先,对工作载荷进行离散化,并确定工况边界.然后,利用长短时记忆(LSTM)网络模型预测载荷和相应的运行工况.其次,以维纳过程为基础,建立了考虑不同工况下退化率和跳变系数的状态退化函数.最后,利用工况激活粒子滤波(CAPF)方法预测轴承退化状态和RUL.采用NetCMAS系统采集的上海某港口起升减速箱轴承全寿命数据验证了所提出的预测框架.与其他3种预测模式比较表明,所提出的框架能够在变工况条件下获得更准确的退化状态和RUL预测.

基于TVFEMD-IMF能量熵增量的桥梁监测数据降噪方法

针对桥梁监测数据受多重噪声干扰、影响结构真实响应获取的问题,提出了一种基于时变滤波经验模态分解(time-varying filtering empirical mode decomposition,简称TVFEMD)和本征模函数(intrinsic mode function,简称IMF)能量熵增量的桥梁监测数据降噪方法。首先,利用TVFEMD分解桥梁原始监测数据,得到多个子序列;其次,采用IMF能量熵增量确定多个子序列中的有效子序列;然后,划分子序列中的结构响应分量和噪声分量,对结构响应分量重组实现监测数据降噪;最后,利用平均绝对误差(mean absolute error,简称MAE)、均方根误差(root mean squared error,简称RMSE)和信噪比(signal-noise ratio,简称SNR)对不同方法的降噪效果进行评价。仿真算例和工程实例结果表明:TVFEMD相比经验模态分解(empirical mode decomposition,简称EMD),有效解决了模态混叠问题;TVFEMD结合IMF能量熵增量方法,有效抑制了多重噪声影响,对结果精度有较大提升;与EMD-IMF能量熵增量和Kalman滤波降噪法相比,TVFEMD-IMF能量熵增量法所得到降噪信号的MAE和RMSE值分别提升了23%和21%以上,降噪效果更好,信噪比提升38%以上,抗噪性能更佳。

基于VMDT-POA-DELM-GPR的两阶段短期负荷预测

针对传统负荷预测方法精度不高的问题,为准确捕捉到负荷数据波动的规律,提出了一种两阶段负荷预测方法。第1阶段首先用变分模态分解(VMD)对原始负荷序列进行分解,得到分解处理后的残差分量,再采用时变滤波经验模态分解(TVF-EMD)方法进行特征提取;然后对全部子序列分别建立深度极限学习机(DELM)模型,同时利用鹈鹕优化算法(POA)进行参数寻优,叠加各子序列的预测值得到初始负荷预测值。第2阶段采用POA-DELM模型对误差分量进行预测;然后将第一阶段中所有子序列预测值和误差预测值作为特征输入到高斯过程回归(GPR)模型中,得到负荷最终的预测结果。结果表明,两阶段模型的均方根误差(RMSE)、平均绝对误差(MAE)分别为对比模型的4%~77%、4%~76%,而平均百分比误差(MAPE)仅为0.0678%,可有效提高电力负荷的预测精度。

A Robust Incremental Algorithm for Predicting the Motion of Rigid Body in a Time-Varying Environment

A configuration point consists of the position and orientation of a rigid body which are fully described by the position of the frame’s origin and the orientation of its axes, relative to the reference frame. We describe an algorithm to robustly predict futuristic configurations of a moving target in a time-varying environment. We use the Kalman filter for tracking and motion prediction purposes because it is a very effective and useful estimator. It implements a predictor-corrector type estimator that is optimal in the sense that it minimizes the estimated error covariance. The target motion is unconstrained. The proposed algorithm may be viewed as a seed for a range of applications, one of which is robot motion planning in a time-changing environment. A significant feature of the proposed algorithm (when compared to similar ones) is its ability to embark the prediction process from the first time step;no need to wait for few time steps as in the autoregressive-based systems. Simulation results supports our claims and demonstrate the superiority of the proposed model.

A Receiver Structure for Frequency-Flat Time-Varying Rayleigh Channels and Performance Analysis

This paper proposes a wavelet based receiver structure for frequency-flat time-varying Rayleigh channels, consisting of a receiver front-end followed by a Maximum A-Posteriori (MAP) detector. Discretization of the received continuous time signal using filter banks is an essential stage in the front-end part, where the Fast Haar Transform (FHT) is used to reduce complexity. Analysis of our receiver over slow-fading channels shows that it is optimal for certain modulation schemes. By comparison with literature, it is shown that over such channels our receiver can achieve optimal performance for Time-Orthogonal modulation. Computed and Monte-Carlo simulated performance results over fast time-varying Rayleigh fading channels show that with Minimum Shift Keying (MSK), our receiver using four basis functions (filters) lowers the error floor by more than one order of magnitude with respect to other techniques of comparable complexity. Orthogonal Frequency Shift Keying (FSK) can achieve the same performance as Time-Orthogonal modulation for the slow-fading case, but suffers some degradation over fast-fading channels where it exhibits an error floor. Compared to MSK, however, Orthogonal FSK provides better performance.

Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

CWT-ETVF与SWT结合的齿轮无转速计阶次跟踪及其应用

变转速齿轮故障振动信号特别微弱时,同步压缩小波变换(synchrosqueezing wavelet transform,SWT)无转速计阶次分析方法的提取效果不佳。基于此,提出一种连续小波变换的椭圆时变滤波(continuous wavelet transform-elliptic time-varying filtering,CWT-ETVF)与SWT相结合的无转速计阶次跟踪方法,用以提取齿轮时变低频故障特征。将CWT-ETVF与SWT结合对振动故障信号进行瞬时频率估计,以获得参考轴相位;再对原信号进行等角度重采样得到角域平稳信号,并作其阶次谱分析和SWT分解;最后,选取SWT重构分量进行阶次谱分析与阶次包络谱分析,以提取齿轮断齿的时变故障特征。仿真及实验结果验证了该方法在齿轮变转速工况下低频微弱故障特征提取的有效性。

基于TVF-EMD-ELM的超短期光伏功率预测

针对光伏功率预测方法精度不高和时效性低的问题,提出了一种基于时变滤波经验模态分解(TVF-EMD)和极限学习机(ELM)相结合的超短期光伏功率预测方法。首先应用TVF-EMD方法对光伏功率数据进行分解,以便得到一组相对稳定的分量,降低不同功率影响因素之间的交互影响。然后采用ELM神经网络模型,根据各分量的特点构建不同的预测模型,来预测各个分量的值。将ELM预测的各分量值相加,从而获得最终的预测结果。算例结果表明该方法的有效性,相比传统模型其归一化均方根误差值降低了25.8%,标准平均绝对误差下降了17.97%,相关系数提高了8.3%。

基于时变滤波经验模态分解和SSA-LSSVM的变压器内部机械故障诊断方法

为了准确有效地识别变压器内部的潜伏性机械故障,提出了一种基于时变滤波经验模态分解(TVFEMD)和麻雀搜索算法优化最小二乘支持向量机(SSA-LSSVM)的变压器内部机械故障诊断方法。首先,对铁心处于不同松动状态的变压器进行振动信号采集;其次,利用时变滤波改进的经验模态分解(EMD)对所得振动信号进行分解,以获取多个本征模态函数(IMF)即模态分量;然后,采用相关系数法计算IMF分量与原始振动信号的相关性,并计算相关性最大的IMF分量的样本熵,以此构建特征向量集;最后,以诊断准确率最高为目标函数,利用SSA对LSSVM的正则化参数和核函数参数进行优化,搭建SSA-LSSVM诊断模型,并利用诊断模型对特征向量集进行诊断识别,实现变压器铁心内部潜伏性机械故障的诊断。试验结果表明,所提方法能够有效识别变压器内部潜伏性机械故障,识别准确率达到了98%以上,比对比算法的识别准确率高出5%以上,达到了高识别准确率的诊断效果。

不确定电液伺服系统的时变输出约束自适应滤波控制

针对电液伺服系统位置跟踪控制中存在的输出约束和不确定性问题,提出一种基于正切型时变障碍Lyapunov函数的输出约束自适应滤波控制方法。构造具有时变约束边界的正切型时变障碍Lyapunov函数,通过时变边界函数的参数设置,使系统输出具有较好的瞬态和稳态性能;设计径向基函数(RBF)神经网络及权重自适应学习律,在线逼近由模型不确定性和未知干扰组成的复合干扰,并将逼近值用于反馈控制;采用二阶指令滤波反步法设计状态反馈控制律和误差补偿机制,避免反步设计中“计算爆炸”的问题,同时消除滤波误差,提高系统位置跟踪精度;依据Lyapunov稳定性理论证明闭环系统中所有误差信号的收敛性。仿真结果表明:系统的稳态误差在所提方法下约为3.48×10^(-8)m,相比于其他控制方法,跟踪误差始终约束在时变的约束边界内,跟踪精度和控制性能均得到提升。

占空比传输机制下基于协同预测的时变不确定系统递推滤波

以工业互联网为背景,研究占空比传输机制下一类时变不确定系统的滤波问题,结合协同预测方法设计了新颖的递推滤波算法,解决了占空比传输机制下滤波性能降低的问题。首先给出描述占空比传输机制的数学模型,然后提出结合协同预测方法的递推滤波方案,设计基于占空比机制的递推滤波算法,推导了滤波误差协方差矩阵的一个上界,随后分析这个上界的有界性,实现了在稀疏数据情形下提高滤波性能的目的。仿真结果验证了所提算法的高效性和有效性。