基于时差序列的多机无源定位算法研究

多站时差序列定位法是一种针对脉冲辐射源的无源定位方法,它是利用短时间内获得的大量时差信息进行定位。文中基于时差序列的定位体制,提出了2个实用的多机定位算法。在目标脉冲波束扫过我方飞行编队的短时间内,将载机位置近似为常值,从而可以使用能够瞬时定位的两步加权最小二乘算法,当测量误差比较大时,采用最大似然法进一步提高定位精度。仿真结果表明,文中提出的基于时差序列的多机定位算法能够达到较高的定位精度,具有很好的工程实用价值。

双站无源时差序列定位算法研究

针对观测站个数受限情况下的无源定位问题,提出一种存在观测站站址误差时使用固定双站对慢匀速直线运动目标进行时差序列定位的解析算法。在目标运动模型的基础上引入目标初始位置和速度的假设值,建立了关于目标假设值误差和观测站站址误差的线性方程组,实现了对目标参数和站址误差的联合求解。仿真表明,当目标假设值取值合理和观测站站址误差较小时,无需多次迭代,算法的定位性能与泰勒级数展开法相近,能够达到克拉美罗下界。

一种基于多站时差和方位信息的联合分选方法

复杂体制雷达信号具有脉间信息关联性弱的特点,但是同一个辐射源发射的脉冲到达不同接收站的时间差在短时间内保持不变,单个接收站接收到同一个辐射源脉冲的方位信息变化缓慢。针对上述特点,提出了一种基于多站时差和方位信息的联合分选方法。对多站时差分选方法进行改进,根据主站和辅站交错脉冲列进行脉冲索引配对,对脉冲配对数据的时差进行直方图统计,对时差直方图进行时差序列搜索与方位信息联合处理,分离出每个辐射源对应的脉冲列。仿真结果表明,脉冲索引配对提高了脉冲配对效率;时差序列搜索与方位信息联合处理可剔除方位错误的脉冲配对,有效提高分选正确率并降低虚警率。

基于TDOA的非同步照射辐射源定位算法

针对现有基于信号到达时差(TDOA)的定位算法难以适应辐射源信号非同步到达的定位问题,提出了一种新的非同步照射辐射源定位算法。该算法利用脉冲样本图外推各站辐射源脉冲的到达时间(TOA)序列,进而计算TDOA序列(TDOAs),解决了非同步照射引起的TDOA计算困难问题,通过两条双曲线的渐近线交点求取初值,通过牛顿迭代法实现对辐射源位置的估计。最后给出的数值仿真结果验证了该方法的有效性。

测量TDOA的完全非同步照射辐射源定位算法

针对现有基于TDOA的定位算法难以适应信号完全非同步到达的辐射源定位问题,提出了一种新的基于TDOA的完全非同步照射辐射源定位算法。由各观测站自动提取辐射源脉冲样本图及开始时间,副站将提取的脉冲样本图及开始时间传给主站,主站利用脉冲样本图匹配及外推技术得到TDOA序列(TDOAs),解决了非同步照射引起的无法计算TDOA问题。通过计算两条时差双曲线的渐近线交点近似求取初值,通过牛顿迭代法实现对辐射源位置的估计。最后给出的数值仿真结果验证了该方法的有效性。

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

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.

Slope displacement prediction based on morphological filtering

Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.

Anomaly Detection in MODIS Land Products via Time Series Analysis

With remote sensing information products becoming increasingly varied and arguably improved, scientific applications of such products rely on their quality assessment. In an operational context such as the NASA (National Aeronautics and Space Administration) information production based on the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board Earth Observing System (EOS) Terra and Aqua satellites, efficient ways of detecting product anomaly, i.e., to discriminate between product artifacts and real changes in Earth processes being monitored, are extremely important to assist and inform the user communities about potential unreliability in the products. A technique for anomaly detection, known as MAD (the median of absolute deviate from the median), in MODIS land products via time series analysis is described, which can handle intra- and in-ter-annual variation in the data by using MAD statistics of the original data and their first-order difference. This method is shown to be robust and work across major land products, including NDVI, active fire, snow cover, and surface reflectance, and its applicabil-ity to multi-disciplinary products is anticipated.

Surface thermal centroid anomaly of the eastern equatorial Pacific as a unified Nio index

By analyzing the variability of global SST （sea surface temperature） anomalies, we propose a unified Nifio index using the surface thermal centroid anomaly of the region along the Pacific equator embraced by the 0.7~C contour line of the standard deviation of the SST anomalies and try to unify the traditional Nifio regions into a single entity. The unified Nifio region covers almost all of the traditional Nifio regions. The anomaly time series of the averaged SST over this region are closely correlated to historical Nifio indices. The anomaly time series of the zonal and meridional thermal centroid have close correlation with historical TNI （Trans-Nifio index） indices, showing differences among E1 Nifio （La Nifia） events. The meridional centroid anomaly suggests that areas of maximum temperature anomaly are moving meridionally （although slightly） with synchronous zonal movement. The zonal centroid anomalies of the unified Nifio region are found helpful in the classification of the Eastern Pacific （EP）/Central Pacific （CP） types of E1 Nifio events. More importantly, the zonal centroid anomaly shows that warm areas might move during a single warming/cooling phase. All the current Nifio indices can be well represented by a simple linear combination of unified Nifio indices, which suggests that the thermal anomaly （SSTA） and thermal centroid location anomaly of the unified Nifio region would yield a more complete image of each E1 Nifio/ La Nina event.

Research on Seismicity and Source Parameters of Small Earthquakes in the Zipingpu Reservoir before the Wenchuan M_S 8. 0 Earthquake

Based on the digital seismic waveform data observed from regional and reservoir seismic networks,the seismicity and source parameters in the Zipingpu reservoir area from January 1,2000 to May 11,2008 before the Wenchuan MS8.0 earthquake are studied. The analysis shows a continuous activity of small earthquakes before the Wenchuan MS8. 0 earthquake. The level of seismic activity was from ML2.0 to ML3.0 in recent years. The impoundment of Zipingpu reservoir started in September 2005 and the obvious increment of seismic activities occurred in February 2008,but according to the analysis of the longer time series,the frequency and intensity of seismic activities do not exceed its range,which means it is still a fluctuation of regional seismic activities or tectonic activity. At the same time,we also studied the spatio-temporal distribution and the change of apparent stress difference in the source region before the great earthquake,and the results show that the apparent stress values of small earthquakes are generally higher than fitted values of apparent stress in the reservoir area in the nearly three months before the Wenchuan MS8.0 earthquake. It can be seen from the spatial distribution of apparent stress difference,that the epicenter of the earthquake is located at a low stress distribution area,and the relatively high apparent stress difference is at the east of the epicenter. Apparent stress increment occurred in local areas.

The Credit Cycle and the Business Cycle in the Economy of Turkey

This study examines econometric relationships between bank lending and the business cycle in Turkey. Firstly, the cyclical components of the real GDP and real bank loans were determined using time series. A cointegration analysis and a vector error-correction model with quarterly data were used for period of 1987：01-2013：03. The results of cointegration analysis indicate that there is a single stable long-run equilibrium relationship between real bank loans and macroeconomic variables. The response of bank loans to GDP shocks is positive. Bank loans have pro-cyclical character in Turkey.

An Empirical Study on the stock Price of Modeling

the model in time series analysis are widely used in the field of economy. We often use the model in time series to analyze data, but without regard to the rationality of the model. In this paper, we introduce and analyze Ping An Of China（601318） shares at the opening price（2013/01/04-2013/07/04）.The model is established by analyzing data. Modeling steps of ARIMA model and GARCH model are presented in this paper. The data whether ARIMA model is suitable by white noise. Or the data whether GARCH model is suitable by since the correlation of variance test. By comparing the analysis, it selects a more reasonable model.

ASYMPTOTIC NORMALITY OF SOME ESTIMATORS IN A FIXED-DESIGN SEMIPARAMETRIC REGRESSION MODEL WITH LINEAR TIME SERIES ERRORS

Consider a semiparametric regression model with linear time series errors Y_k= x′ _kβ + g(t_k) + ε_k, 1 ≤ k ≤ n, where Y_k's are responses, x_k =(x_(k1),x_(k2),···,x_(kp))′ and t_k ∈ T is contained in R are fixed design points, β =(β_1,β_2,···,β_p)′ is an unknown parameter vector, g(·) is an unknown bounded real-valuedfunction defined on a compact subset T of the real line R, and ε_k is a linear process given byε_k = ∑ from j=0 to ∞ of ψ_je_(k-j), ψ_0=1, where ∑ from j=0 to ∞ of |ψ_j| < ∞, and e_j,j=0, +-1, +-2,···, ard i.i.d. random variables. In this paper we establish the asymptoticnormality of the least squares estimator of β, a smooth estimator of g(·), and estimators of theautocovariance and autocorrelation functions of the linear process ε_k.

Numerical analysis of wave hazards in a harbor

Resonance may occur when the periods of incoming waves are close to the eigen-periods of harbor basin.The amplified waves by resonance in harbor will induce serious wave hazards to harbor structures and vehicles in it.Through traditional theoretical approaches,the eigen-periods of harbor basin with regular shapes can be obtained.In our study,we proposed a numerical model to simulate the behavior characteristics of the harbor waves.A finite difference numerical model based on the shallow water equations(SWE) is developed to simulate incoming tsunami and tidal waves.By analyzing the time series data of water surface wave amplitude variations at selected synthetic observation locations,we estimate the wave height and arrival time in coastal area.Furthermore,we use frequency spectrum analysis to investigate the natural frequencies from the data recorded at the synthetic observation stations.