共查询到18条相似文献,搜索用时 281 毫秒
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分析了周期Wigner-Hough变换(PWHT)进行离散化计算时,对线性调频连续波(LFMCW)信号
的检测性能
。通过推导高斯白噪声中LFMCW信号经离散化PWHT后信噪比处理增益表达式,分析了离散PWH
T的弱信号检测性能。通过推导离散PWHT较离散Wigner-Hough变换(WHT)的检测性能优势表
达式,比较了离散PWHT与离散WHT算法的检测性能。仿真实验验证了离散PWHT的信噪
比处理增益具有准相干积累的效果,PWHT较WHT更优的检测性能。 相似文献
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当存在离格信号时,基于稀疏表示的波达角(DOA)估计算法性能损失严重。为解决这个问题,在对接收数据协方差矩阵进行Khatri-Rao积变换的基础上,推导了离格信号网格偏离量与紧邻信号原子系数之间的关系,提出了一种单一离格信号DOA估计方法。为提高对邻近离格信号DOA的估计性能,利用矩阵的广义逆性质提出了基于多原子系数的联合估计方法。仿真实验表明,单一离格信号DOA估计方法在低信噪比下有较好的性能,联合估计方法在高信噪比条件下对邻近离格信号DOA有较高的估计精度,同时所提算法估计性能几乎不受网格划分间距的影响,可以通过增大网格间距降低算法运算量。相关研究对阵列天线DOA估计具有一定的参考价值。 相似文献
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提出了一种基于重叠变换的自适应干扰抑制算法,利用调制重叠变换(MLT)把接收机信号映射到变换域进行抗干扰处理,它可以有效地把干扰能量集中在有限的变换域子带(Transform Bins)中,与基于DFT、DCT等块变换相比有更好的滤波效果。由于可以使用基于重叠变换的快速算法,因此它结构简单,节省计算量。仿真结果表明,基于MLT的变换域处理方案可以有效地改善系统性能。 相似文献
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由于机动目标的逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)成像中存在多普勒扩散现象,易导致成像分辨率降低。针对此问题,在多分量三次相位信号(Cubic Phase Signal,CPS)模型基础上,提出一种基于Radon-CPF-Fourier变换(Radon-Cubic Phase Function-Fourier Transform,RCFT)的机动目标ISAR成像方法。RCFT可实现CPF中自项能量的相干积累,减轻多普勒扩散带来的影响。与现有的基于时频分析和参数估计的ISAR成像算法相比,RCFT的主副比性能提升了至少2 dB,在时频分辨率无损失时具备更好的自项能量积累能力,可获得更高质量的成像结果。仿真验证了所提方法的有效性。 相似文献
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为提高二次雷达(Secondary Surveillance Radar,SSR)信号分析处理能力,针
对傅里叶变换在时频域分析的局限性,利用小波信号奇异性检测特点,通过对S模式询
问、应答信号进行小波分解,计算第一层高频系数,得到信号脉冲持续时间,实现了信号报
头检测,并比较高频系数模极大值,提取出信号调制信息,实现了基于小波变换的二进制差
分相移键控(DPSK)和二进制振幅键控(ASK)解调,验证了小波变换技术分析处理二次雷
达信号的可行性。 相似文献
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Data mining techniques have numerous applications in credit scoring of customers in the banking field. One of the most popular data mining techniques is the classification method. Previous researches have demonstrated that using the feature selection (FS) algorithms and ensemble classifiers can improve the banks' performance in credit scoring problems. In this domain, the main issue is the simultaneous and the hybrid utilization of several FS and ensemble learning classification algorithms with respect to their parameters setting, in order to achieve a higher performance in the proposed model. As a result, the present paper has developed a hybrid data mining model of feature selection and ensemble learning classification algorithms on the basis of three stages. The first stage, as expected, deals with the data gathering and pre-processing. In the second stage, four FS algorithms are employed, including principal component analysis (PCA), genetic algorithm (GA), information gain ratio, and relief attribute evaluation function. In here, parameters setting of FS methods is based on the classification accuracy resulted from the implementation of the support vector machine (SVM) classification algorithm. After choosing the appropriate model for each selected feature, they are applied to the base and ensemble classification algorithms. In this stage, the best FS algorithm with its parameters setting is indicated for the modeling stage of the proposed model. In the third stage, the classification algorithms are employed for the dataset prepared from each FS algorithm. The results exhibited that in the second stage, PCA algorithm is the best FS algorithm. In the third stage, the classification results showed that the artificial neural network (ANN) adaptive boosting (AdaBoost) method has higher classification accuracy. Ultimately, the paper verified and proposed the hybrid model as an operative and strong model for performing credit scoring. 相似文献
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《Journal of Interactive Marketing》2002,16(4):37-50
The performance of a direct marketing scoring model at a particular mailing depth, d, is usually measured by the total amount of revenue generated by sending an offer to the customers with the 100d% largest scores (predicted values). Commonly used variable selection algorithms optimize some function of model fit (squared difference between training and predicted values). This article (1) discusses issues involved in selecting a mailing depth, d, and (2) proposes a variable selection algorithm that optimizes the performance as the primary objective. The relationship between fit and performance is discussed. The performance-based algorithm is compared with fit-based algorithms using two real direct marketing data sets. These experimental results indicate that performance-based variable selection is 3–4% better than corresponding fit-based models, on average, when the mailing depth is between 20% and 40%. 相似文献
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根据时分多址(TDMA)系统的同步特征,利用TDMA运动目标准周期性信号的到达时间,提出
了3种在三站时差定位系统中实现目标定位的算法。采用目标运动分析的方法,对TDMA目标
位置的可观测性进行分析,提出了目标运动分析时差定位算法,利用目标航迹上多个位置的
时差实现目标的定位。运用目标运动分析测距算法,提出了测距与传统时差定位和目标运动
分析时差定位相结合的两种定位算法。3种定位算法充分利用了目标的运动特性
,提高了TDMA目标的定位精度,避免了传统时差定位算法中的多解和无解现象。仿真
结果验证了算法的有效性。 相似文献
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为对深空网天线组阵中几种相关合成算法进行分析比较,构建了一种新的Simulink仿真模型。将其应用于某测控站天线组阵试验数据,验证了模型的可行性。在此仿真模型下,对Simple、Sumple和Matrix-free 算法进行了频标同源/频标不同源、弱信号/强信号、2/3/6个天线等组阵情况下的仿真分析。三种算法在频标同源情况下的合成效率均优于不同源的情况;强信号组阵情况下,三种算法的信噪比合成性能基本相当;Simple算法在6天线情况下,信噪比合成性能下降;Sumple算法在组阵的天线数目很少时,合成信噪比较低且不稳定,在天线数目较多时性能良好;Matrix-free算法性能稳健,合成效率始终大于95%。该Simulink仿真模型对于进行天线组阵信号相关算法的分析具有一定的价值。 相似文献
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针对正交频分复用系统中的信号峰值平均功率比问题,提出了一种基于时域采样点幅度筛选的低复杂度部分传输序列算法。算法通过设置信号经过逆向傅里叶变换后时域采样点的幅度之和为判别函数,并设置适当的幅度门限,筛选出幅度大于门限的信号采样点集合,利用该集合来搜索信号峰均比抑制相位因子,从而降低算法复杂度。仿真表明,与传统峰均比抑制算法相比,改进算法在保持峰均比抑制性能的同时降低了算法复杂度。 相似文献