基于多相参处理间隔频响特征聚类的有源假目标鉴别方法

现有真-假目标智能识别算法大多基于监督学习,且在低信噪比条件下表现不好。针对上述问题,该文分别利用真、假目标在多个相参处理间隔(CPIs)内散射特性的时变性和唯一性,提出一种多相参处理间隔频响特征聚类的真、假目标无监督鉴别方法。首先,在快-慢时域中沿快时间维度对真、假目标进行加窗截断,提取快-慢时间域频率响应特征用于构建初步样本集;然后,通过Agglomerative聚类和特征融合网络组成的两步识别算法对真-假目标进行识别;最后,提出一种多相参处理间隔联合决策方法提升识别性能和可靠性。经仿真和实测数据检验,证明了所提方法可实现真实目标和多种有源假目标的有效分离。

内外环电容传感器空间灵敏度特性

为了设计应用于两相流相关流速测量的电容传感器,研制了一种新型的内外环电容传感器,利用有限元仿真的方法对内外环电容传感器的轴向灵敏度、空间滤波特性以及时间频率响应3个空间灵敏度特性指标做了定量分析.结果显示,内外环电容传感器的轴向灵敏度曲线近似于高斯分布,而电极宽度的变化会引起轴向灵敏度峰值的改变,当电极宽度为20,mm时,轴向灵敏度峰值最大;内外环电容传感器相当于一个低通滤波器,电极宽度增加,空间频带变窄,同时时间频带也变窄.

Smoothing Non-Stationary Time Series Using the Discrete Cosine Transform

This paper considers the problem of smoothing a non-stationary time series(having either deterministic and/or stochastic trends) using the discrete cosine transform(DCT).The DCT is a powerful tool which has found fruitful applications in filtering and smoothing as it can closely approximate the optimal Karhunen-Loeve transform(KLT).In fact,it is known that it almost corresponds to the KLT for first-order autoregressive processes with a root close to unity:This is the case with most economic and financial time series.A number of new results are derived in the paper:(a) The explicit form of the linear smoother based on the DCT,which is found to have time-varying weights and that uses all observations;(b) the extrapolation of the DCT-smoothed series;(c) the form of the average frequency response function,which is shown to approximate the frequency response of the ideal low pass filter;(d) the asymptotic distribution of the DCT coefficients under the assumptions of deterministic or stochastic trends;(e) two news method for selecting an appropriate degree of smoothing,in general and under the assumptions in(d).These findings are applied and illustrated using several real world economic and financial time series.The results indicate that the DCT-based smoother that is proposed can find many useful applications in economic and financial time series.