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常规IIR宽带波束形成器可以获得比FIR宽带波束形成器更好的性能,但其需要多极点的自适应调整过程,存在稳定性无法保证,计算复杂度较高等问题.本文提出一种新的基于IIR滤波器的宽带波束形成算法.该算法基于高阶Laguerre宽带波束形成器,利用双线性变换和函数束方法设计相应的低阶等价IIR宽带波束形成器.仿真实验及理论分析表明,该方法无需常规IIR宽带波束形成器的多极点自适应调整过程,在保证算法稳定性的同时,减少了计算复杂度,并提高了输出信干噪比(SINR). 相似文献
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该文针对多脉冲发射协同结构的分布式全相参雷达,首先建立了全相参模式下的信号模型,然后推导了输出信噪比增益(oSNRg)的解析式,并基于相干参数估计的克拉美-罗界(CRB),利用多项式拟合,获得了oSNRg上界的数值解.结果表明:增加发射天线数或脉冲数能够提高oSNRg;而增加接收天线数能否提高oSNRg取决于输入信噪比大小,输入信噪比较大时则oSNRg随之提高,较小时反而随之下降.最后的仿真实验验证了研究结论的正确性. 相似文献
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一种新型非线性卡尔曼滤波方法 总被引:6,自引:0,他引:6
提出了一种新型非线性卡尔曼滤波方法—单形无迹求积卡尔曼滤波(SUQKF)方法,该方法通过对单形无迹卡尔曼滤(SUKF)波所用的采样点进行修正,并与高斯-拉盖尔积分准则相结合,构造了一组个数、权系数和空间分布确定的新型高阶采样点,用来进行滤波。同时指出SUKF是SUQKF的特例。将所提方法通过实验与扩展卡尔曼滤波(EKF)、容积求积卡尔曼滤波(CQKF)进行比较,结果表明:SUQKF方法滤波精度高于EKF和CQKF,且收敛速度较快,实时性优于CQKF。 相似文献
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This article is concerned with the polynomial filtering problem for a class of nonlinear stochastic systems governed by the Itô differential equation. The system under investigation involves polynomial nonlinearities, unknown‐but‐bounded disturbances, and state‐ and disturbance‐dependent noises ((x,d)‐dependent noises for short). By expanding the polynomial nonlinear functions in Taylor series around the state estimate, a new polynomial filter design method is developed with hope to reduce the conservatism of the existing results. In virtue of stochastic analysis and inequality technique, sufficient conditions in terms of parameter‐dependent linear matrix inequalities (PDLMIs) are derived to guarantee that the estimation error system is input‐to‐state stable in probability. Moreover, the desired polynomial matrix can be obtained by solving the PDLMIs via the sum‐of‐squares approach. The effectiveness and applicability of the proposed method are illustrated by two numerical examples with one concerning the permanent magnet synchronous motor. 相似文献
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Imperfections in the manufacturing process of flow measuring probes affect their measuring behavior. Nevertheless, in order to provide the highest possible accuracy, each individual multi-hole pressure probe has to be calibrated before using them in turbomachinery. This paper presents a novel method based on artificial neural networks (ANN) to predict the flow parameters of multi-hole pressure probes. A two-stage ANN approach using multilayer perceptron (MLP) is proposed in this study. The two-stage prediction approach involves two MLP networks, which represent the calibration data and the prediction error. For a given set of inputs, outputs from both networks are combined to estimate the measured value. The calibration data of a 5-hole probe at RWTH Aachen was used to develop and validate the proposed ANN models and two-stage prediction approach. The results showed that the ANN can predict the flow parameters with high accuracy. Using the two-stage approach, the prediction accuracy was further improved compared to polynomial functions, i.e. a commonly used method in probe calibration. Furthermore, the proposed approach offers high interpolation capabilities while preventing overfitting (i.e. failure to fit new data). Unlike polynomials, it is shown that the ANN based method can provide accurate predictions at intermediate points without large oscillations. 相似文献
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This article considers the continuous version of the refueling station location problem on a tree network, which is a common structure in numerous toll roads worldwide, so as to locate a single alternative-fuel refueling station to maximize the traffic flow covered in round trips/day. Two reduction properties regarding the problem size and some optimality conditions are derived. Based on these conditions, an exact polynomial algorithm is developed to determine the set of optimal locations for the refueling station. A small tree network example is solved to illustrate the algorithm. 相似文献