Parameter estimation of maneuvering targets in OTHR based on sparse time-frequency representation

This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages： Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform（FFT）. And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function（HAF） method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method.

Influence of explosion parameters on wavelet packet frequency band energy distribution of blast vibration

Blast vibration analysis is one of the important foundations for studying the control technology of blast vibration damage.According to blast vibration live data that have been collected and the characteristics of short-time non-stationary random signals,the wavelet packet energy spectrum analysis for blast vibration signal has made by wavelet packet analysis technology and the signals were measured under different explosion parameters(the maximal section dose,the distance of blast source to measuring point and the section number of millisecond detonator).The results show that more than 95% frequency band energy of the signals s1-s8 concentrates at 0-200 Hz and the main vibration frequency bands of the signals s1-s8 are 70.313-125,46.875-93.75,15.625-93.75,0-62.5,42.969-125,15.625-82.031,7.813-62.5 and 0-62.5 Hz.Energy distributions for different frequency bands of blast vibration signal are obtained and the characteristics of energy distributions for blast vibration signal measured under different explosion parameters are analyzed.From blast vibration signal energy,the decreasing law of blast seismic waves measured under different explosion parameters was studied and the wavelet packet analysis is an effective means for studying seismic effect induced by blast.

The Time-frequency Characteristic of a Large Volume Airgun Source Wavelet and Its Influencing Factors

Through analyzing the near-field hydrophone records of the airgun experiment in the Jiemian reservoir,Fujian,we study the time-frequency characteristic of airgun source wavelet and the influence of gun depth and firing pressure,and explain the process of bubble oscillation based on the Johnson( 1994) bubble model. The data analysis shows that:( 1) Airgun wavelet is composed of primary pulse and bubble pulse. The primary pulse,which is of large amplitude,short duration and wide frequency band,is usually used in shallow exploration. The bubble pulse,which is concentrated in the low-frequency range,is usually used in deep exploration with deep vertical penetration and far horizontal propagation.( 2) The variation of primary pulse amplitude with gun depth is very small,bubble pulse amplitude and the dominant frequency increase,and peak-bubble ratio and bubble period decrease. When the gun depth is 10 m,primary pulse amplitude and peakbubble ratio are maximum,which is suitable for shallow exploration; when gun depth is25 m,bubble pulse amplitude is large, and peak-bubble ratio is minimum, which is suitable for deep exploration.( 3) The primary pulse amplitude,bubble pulse amplitude,peak-bubble ratio,and bubble period increase and the dominant frequency decreases with increased firing pressure.

DOPPLER PARAMETER EXTRACTION OF MOVING TARGETS IN SAR IMAGING BY WAVELET TRANSFORM

In this paper, the shortages of Wigner-Ville Distribution(WVD) double-linearity in extracting the Doppler parameters of moving-targets are discussed, especially in multi-point moving-target imaging processing based on the spectrum characteristics of moving-target echo signals in Synthetic Aperture Radar (SAR) imaging processing and the properties of WVD. Combined with the characteristics of Continuous Wavelet Transform (CWT), the responsibility and advantages of CWT in multi-point moving-target Doppler parameter extraction are analyzed. Finally a kind of multi-point moving-target Doppler parameter extracting algorithm based on CWT are developed, and the computer stimulating results demonstrate the correctness of the algorithm.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Application of Wavelet Packet De-noising in Time-Frequency Analysis of the Local Wave Method

The local wave method is a very good time-frequency method for nonstationaryvibration signal analysis. But the interfering noise has a big influence on the accuracy oftime-frequency analysis. The wavelet packet de-noising method can eliminate the interference ofnoise and improve the signal-noise-ratio. This paper uses the local wave method to decompose thede-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally,an example of wavelet packet de-noising and a local wave time-frequency spectrum application ofdiesel engine surface vibration signal is put forward.

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

An adaptive unscented Kalman filter （AUKF） and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter （UKF） when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

Research on LPI radar signal detection and parameter estimation technology

Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics,making the detection and estimation of LPI radar signals extremely difficult,and leading to highly required significant research on perception technology in the battlefield environment.This paper proposes a visibility graphs(VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation(TFR).On the one hand,the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms.On the other hand,the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency(IF).Simulation performance shows that,compared with the most advanced methods,the algorithm proposed in this paper has a valuable advantage.Meanwhile,the calculation cost of the algorithm is quite low,and it is achievable in the future battlefield.

Design and Realization of Signal Processing Platform of Multi-Parameter Wearable Medical Devices

This paper presents a design of new type of multi-parameter wearable medical devices signal processing platform. The signal processing algorithm has a QRS-wave detection algorithm based on LADT, wavelet transformation and threshold detection with TMS320VC5509 DSP system. The DSP can greatly increase the speed of QRS-wave detection, and the results can be practical used for multi-parameter wearable device detection of abnormal ECG.

Novel Time-frequency Analysis and Representation of EEG

A novel method of EEG time-frequency analysis and representation based on a wavelet network is presented. The wavelet network model can represent the EEG data effectively. Based on the wavelet network model, a novel time-frequency energy distribution function is obtained, which has the same time-frequency resolution as Wigner-Ville distribution and is free of cross-term interference. There is a great potential for the use of the novel time-frequency representation of nonstationary biosignal based on a wavelet network in the field of the electrophysiological signal processing and time-frequency analysis.

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

Empirical Bayes Test for Two-parameter Exponential Distribution under Type-Ⅱ Censored Samples

The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.

基于遥感多参数和IPSO-WNN的冬小麦单产估测

冬小麦是我国的主要粮食作物之一。为进一步准确地估测冬小麦产量,以陕西省关中平原为研究区域,选取冬小麦主要生育期与水分胁迫和光合作用等密切相关的条件植被温度指数(VTCI)、叶面积指数(LAI)和光合有效辐射吸收比率(FPAR)作为遥感特征参数,采用改进的粒子群算法优化小波神经网络(IPSO-WNN)以改善梯度下降方法易陷入局部最优的缺陷,并构建冬小麦产量估测模型。结果表明,IPSO-WNN模型的决定系数R2为0.66,平均绝对百分比误差(MAPE)为7.59%,相比于BPNN(R2=0.46,MAPE为11.80%)与WNN(R2=0.52,MAPE为9.80%),IPSO-WNN能够进一步提高模型的精度、增强模型的鲁棒性。采用灵敏度分析的方法探究对冬小麦产量影响较大的输入参数,结果发现,抽穗-灌浆期的FPAR对冬小麦产量影响最大,其次拔节期的VTCI、抽穗-灌浆期和乳熟期的LAI以及返青期和拔节期的FPAR对冬小麦产量的影响较大。通过IPSO-WNN输出获取冬小麦综合监测指数I,构建I与统计单产之间的估产模型以估测关中平原冬小麦单产,结果显示,估测单产与统计单产之间的R2为0.63,均方根误差(RMSE)为505.50 kg/hm^(2),相比于前人的研究较好地解决了估产模型存在的“低产高估”的问题,因此,本文基于IPSO-WNN构建的估产模型能够较准确地估测关中平原冬小麦产量。

基于改进经验小波变换的海洋平台结构模态参数自动识别方法

针对经验小波变换(empirical wavelet transform,EWT)方法在处理低信噪比信号中频谱分割边界容易产生误判的问题,提出了一种改进经验小波变换(improved empirical wavelet transform,IEWT)的结构模态参数自动识别方法。首先计算信号的互功率谱矩阵,采用奇异值分解(SVD)及尺度空间(SSPP)方法确定频谱的分割边界,将信号分解为若干固有模态函数(IMF)分量,再结合随机减量技术(RDT)和希尔伯特变换(HT)实现模态参数的自动识别。使用IEWT方法对自由振动响应信号及ASCE Benchmark模型信号进行模态参数识别,并分别与EWT方法、基于自回归功率谱的经验小波变换(AR-EWT)方法及小波变换(WT)方法进行对比,结果表明IEWT方法能够自适应确定频谱分割边界,对结构的频率及阻尼比等模态参数具有较高的识别精度;进一步将该方法应用到实验室海洋平台模型的模态参数识别中,证明该方法可用于复杂噪声环境下的低频结构的模态参数识别。

迭代奇异值方法在机械结构模态分离重构中的应用

通过时频分解技术,将复杂的多模态信号分解成单模态成分,从而可以采用比较简单可靠的单模态识别方法对机械结构复杂模态信号进行参数辨识。经验小波变换(EWT)算法能有效解决模态分离问题,一些改进型EWT算法能有效克服噪声干扰,但是在模态重构时,滤波器彼此重叠、临近模态互相干扰,会不可避免地出现重构模态失真。本文针对模态分离重构问题展开研究,分析了EWT算法在模态分离重构中面临的重构失真问题,提出了基于迭代截断奇异值分解(ITSVD)方法的改进算法,并在仿真信号和含结合面机械结构模型振动响应信号上进行了应用。结果表明,所提ITSVD⁃EWT算法能够更好地实现机械结构模态分离重构。

Target parameter estimation for OTFS-integrated radar and communications based on sparse reconstruction preprocessing

Orthogonal time-frequency space(OTFS)is a new modulation technique proposed in recent years for high Doppler wireless scenes.To solve the parameter estimation problem of the OTFS-integrated radar and communications system,we propose a parameter estimation method based on sparse reconstruction preprocessing to reduce the computational effort of the traditional weighted subspace fitting(WSF)algorithm.First,an OTFS-integrated echo signal model is constructed.Then,the echo signal is transformed to the time domain to separate the target angle from the range,and the range and angle of the detected target are coarsely estimated by using the sparse reconstruction algorithm.Finally,the WSF algorithm is used to refine the search with the coarse estimate at the center to obtain an accurate estimate.The simulations demonstrate the effectiveness and superiority of the proposed parameterestimation algorithm.

活塞-缸套摩擦副状态表征参数选取方法研究

建立柴油机试验台架采集数据,对机体表面振动信号进行时频分析,探明不同激励源与机体表面振动信号的关系。选取变分模态分解(variational mode decomposition,VMD)算法对振动信号进行分解,提取各分量的表征参数。通过探究转矩、转速、润滑油温度及配缸间隙与各表征参数的相关性,初步确定相关性强的表征参数集。通过多评价准则对上述表征参数集进行分析,最终得出贡献度最高的表征参数为本征模态函数(intrinsic mode function,IMF)1的标准差、均方频率、峭度、最大奇异值、频域积分和IMF6的脉冲因子、标准差、重心频率、频率方差及最大奇异值。

小波包分解重构的励磁变压器整流谐波附加损耗计算

谐波损耗是造成励磁变压器安全事故的主要原因之一,损耗值过大易影响正常变电和输电,为此提出基于小波包分解重构的励磁变压器整流谐波附加损耗计算方法。通过获取小波包分解和重构系数,分析励磁变压器两种谐波附加损耗;利用开路和短路实验得到励磁变压器等效电路参数,采用小波包分解重构算法,计算不同谐波次数下的电阻和电抗并与基准值对比,得到励磁变压器整流谐波附加损耗值。最后,选取某种型号的励磁变压器,利用所提方法计算其在不同谐波次数下的附加损耗值,结果表明,得到的损耗值计算结果与实际结果非常接近,验证了所提方法具有较高的实用价值。

不同组合模型的地球自转参数预报对比

以最小二乘法、小波去噪、小波神经网络和BP神经网络为基础,构建9种地球自转参数预报模型,并进行30 d短周期预报。结果表明,对于极移预报,基于BP神经网络的地球自转参数预报模型效果不佳,RMSE均大于1.5 mas;最小二乘与小波神经网络组合模型的预报效果最好,RMSE小于1.3 mas。对于日长变化预报,最小二乘与小波神经网络组合模型的预报效果不佳,RMSE均大于0.18 ms;小波神经网络预报模型预报效果最好,RMSE为0.07 ms。

振动信号在跨越油气管道安全检测中的应用

提出一种基于振动传感器的跨越油气管道意外载荷检测方法.基于阈值处理理论,构建阈值-模极大值去噪方法,将重心频率、均方频率和频率标准差作为判断油气管道运行状态的特征参数;通过搭建长输管道振动实验系统,采用外物撞击的方法模拟跨越油气管道的意外载荷,对振动信号进行降噪处理并提取特征参数.结果表明,与传统去噪法相比,本方法的信噪比和计算速度获得大幅提升;管道正常运行和外物撞击状态下振动信号的相关特征参数存在显著差异,说明振动信号作为跨越油气管道状态检测指标的有效性;振动信号相关特征参数随着信号源与传感器距离的增加而增加,而对管道输量不敏感;流体流向对振动信号有重要影响,信号源在传感器上游时,相关特征参数显著高于信号源在传感器下游的情况.