Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

Nonuniform linear array DOA estimation using EM criterion

In this letter, we address the problem of Direction of Arrival (DOA) estimation with nonuniform linear array in the context of sparse Bayesian learning (SBL) framework. The nonuniform array output is deemed as an incomplete-data observation, and a hypothetical uniform linear array output is treated as an unavailable complete-data observation. Then the Expectation-Maximization (EM) criterion is directly utilized to iteratively maximize the expected value of the complete-data log likelihood under the posterior distribution of the latent variable. The novelties of the proposed method lie in its capability of interpolating the actual received data to a virtual uniform linear array, therefore extending the achievable array aperture. Simulation results manifests the superiority of the proposed method over off-the-shelf algorithms, specially on circumstances such as low SNR, insufficient snapshots, and spatially adjacent sources.     

大牛地气田气井无阻流量的确定方法研究

大牛地气田气井试井大多采用一点法，由于气层渗透性差，测试时间短，流压很难达到稳定，计算的无阻流量往往偏高，且无法获得正确的产能方程。通过气田多口井修正等时试井数据进行统计分析，获得了一点法试井在不同生产时间下计算无阻流量的经验公式；利用气田修正等时试井的二项式方程系数曰，计算惯性阻力系数并经过曲线回归处理获得了经验关系式，很好地解决了一点法试井计算产能方程的问题。     

基于均匀圆阵的信号二维方向角互相关估计

针对复值乘性和加性噪声同时存在的情况,提出一种基于均匀圆阵的信号二维方向角互相关估计方法.本文推导出互相关估计公式,分析了估计的统计性能,给出了最优参数选择原则.在不改变均匀圆阵阵列流形的情况下,此方法抑制了信号中的乘性和加性噪声,且计算量小.计算机仿真证明了此方法的有效性.     

一种基于空间到达角的卫星电子侦察信号分选方法

在建立简化的卫星电子侦察空间模型的基础上，提出了一种利用空间到达角分选卫星电子侦察信号的新方法。通过仿真实验验证了这种方法的可行性。该方法可用于密集信号的预处理。     

Biases of Success Rate Differences Shown in Binomial Effect Size Displays.

The intent of a binomial effect size display (BESD) is to show "the [real-world] importance of [an] effect indexed by a correlation [r]" (R. Rosenthal, 1994, p. 242) by reexpressing this correlation as a success rate difference (SRD) (e.g., treatment group success rate - control group success rate). However, SRDs displayed in BESDs generally overestimate real-world SRDs implied by correlations of (a) dichotomous X and Y variables (φ coefficients), (b) dichotomous X and continuous Y variables (point-biserial coefficients [rphs]). and (c) continuous X and Y variables (rxys). Furthermore, overestimation biases are larger for rxys than for rphs. Differences in the sizes of biases linked to different correlations suggest that BESD SRDs reported for different correlations are not comparable. The stochastic difference index (N. Cliff, 1993: A. Vargha & H. D. Delaney, 2000) is recommended as an alternative to the BESD. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

榆林气田气井废弃条件的确定

榆林气田是典型的低渗透气田，要准确计算其采收率和可采储量，必需确定气井的废弃条件。传统方法对气井的废弃地层压力进行计算时，由于忽视了开发过程中产能方程的变化，造成计算结果存在一定的误差。根据气井废弃地层压力的相关理论和计算方法，考虑了对气井产能方程影响较大的参数的变化，结合榆林气田气井矿场实际，推导出了适合榆林气田计算气井任意时刻二项式方程系数的公式，进而得到气井废弃时的稳定二项式方程。根据垂直管流法计算得到废弃井底流压，从而得到可靠的气井废弃地层压力，再结合经济评价即可确定气井的废弃产量。实例计算表明，新方法实用、有效，更切合气田实际。     

Water quality surveys: A statistical method based on determinism, quantiles and the binomial distribution

A statistical method for surveying water quality and analysing the results in terms of quantiles is described. The value of a water quality variable in a grab-sample is taken as a deterministic function of the temporal and spatial coordinates of the sampling point. Quantiles can then be defined for any specified region in time and space as unique, fixed values, and randomly located grab-samples may be used to estimate them for the purposes of describing or regulating water quality. Robust statistical inferences are possible using the binomial distribution. Properties of the quantiles, confidence limits, comparisons with fixed values or between regions, assessment of trends, simultaneous inferences and estimation of sample sizes are discussed. Two examples of water quality surveys, one of Port Phillip Bay, the other of the Maribyrnong River, Victoria, are used to illustrate the theory, paying particular attention to practical problems which arose. The proposed method is compared with others based on stochastic models, and is claimed to be simpler and more reliable.     

基于AUPF的TDOA几何定位跟踪算法研究

使用无源时差（TDOA）定位技术确定无人机等小型辐射源目标的位置是当前研究的热点,针对时差定位算法较为复杂的实际情况,推导了时差双曲线的几何解,并提出了一种基于自适应无迹粒子滤波（AUPF）技术的移动目标定位跟踪方法。通过仿真对该方法在不同场景的应用效果进行了验证,进一步比较分析了算法的定位精度。结果表明,基于自适应无迹粒子滤波的时差几何定位跟踪算法可以在多种情况下较好地拟合出目标真实运动轨迹,实现对运动目标的定位跟踪,同时拥有更低的定位误差和更高的轨迹包容度,使用该方法可以显著提高对非合作移动辐射源目标的位置估计性能。     

Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

Efficiently performing high-resolution direction of arrival (DOA) estimation under low signal-to-noise ratio (SNR) conditions has always been a challenge task in the literatures. Obvi-ously, in order to address this problem, the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs. However, it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all. Therefore, this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators. Specifically, these basic spatial spec-trums are employed as the input of multi-source information fusion model. And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines, namely neural networks (NN), support vector machine (SVM), and random forests (RF). The final meta-spec-trum can be obtained by performing a final decision-making method. Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators. Even under low SNR conditions, promising DOA estimation performance can be achieved.