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未知信号
    The Resolution of Overlapping Echoes of Unknown Signal Shape
    未知信号形状下的多径回波分辨
    This is only attempt to measure the frequency of unknown signal by using chaos, but it is significance to reality measurement.
    这虽然只是利用混沌测量未知信号频率的一个理论探讨,但它对实际测量有着重要的指导意义。
    Which jo is the best for a given unknown signal and a noise power σ?
    对于一给定的未知信号和噪声指数σ,哪一个jo是最佳的呢 ?
    It is developed based on the standard Kalman filtering scheme, and hence preserves the merits of the Kalman filer for signal estimation in the sense that it produces an optimal estimate of the unknown signal in a recursive manner.
    它是在标准的卡尔曼滤波基础上产生的,因此保留了卡尔曼滤波对信号估计的优点,同时以递归的方式实现对未知信号的最优估计。
    Recognition of the modulation type of an unknown signal provides insight into its structure, origin and properties.
    通过对未知信号调制模式的识别能让我们洞察未知信号的结构、起源以及信号的属性。
    This article puts forward the mathematical model of multivariated time series analysis - the autoregression model of linear combination of awarting-decision (ALCAR(n)). Digit imitation analysis shows ALCAN analysis can high precisely estimate the signal character parameters of folding process and high precisely decompose folding process on this condition of unknown signal and noise character parameters.
    本文提出一种多变量时间序列分析的数学模型—待定线性组合自回归模型(ALCAR(n)),数字仿真分析表明在未知信号及噪声特性参数条件下,ALCAR(n)模型分析可以高精度地估出叠加过程中各信号的自回归参数以及高精度地将各叠加信号分解开。
    The ability to construct an optimum filter bank corresponding to the adaptive signal properties of the WPT and the merits of the Kalman filter to produce a linear, unbiased, minimum error-variance estimate of an unknown signal and on-line parameters estimating of the measurement noise are employed, which enableds the algorithm to track multiple independent and time-varying narrow-band interferences so that they can be effectively eliminated.
    该方法综合利用了WPT自适应构建信号特性所对应的“最佳”滤波器组的能力和Kalman滤波对未知信号的线性的、无偏的、最小方差估计的特点以及量测噪声参数在线实时估计策略的优点 ,可以很好地恢复多个独立的、时变的窄带干扰以便消除。
    With the features based on the reflection coefficients of AR model extracted from vibration signals,HMM was used to calculate the matching degree among the unknown signal and the gearbox's states,which formed the features for SVM to diagnosis. The result shows that this proposal method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.
    通过从减速箱振动信号中有效提取AR特征,利用HMM计算未知信号与减速器各状态的匹配程度,形成特征向量提供给SVM最后判别,实验结果表明该方法优于单纯的HMM或SVM诊断方法,能利用少量训练样本有效地完成直升机减速器的故障诊断。
    This algorithm is developed based on the standard Kalman filtering scheme, and hence preserves the merits of the Kalman filter for signal estimation in the sense that it produces an optimal estimate of the unknown signal in a recursive manner.
    这种算法通过离散小波变换滤波层实现对信号的同时估计和分解,它是在标准的卡尔曼滤波基础上产生的,因此保留了卡尔曼滤波对信号估计的优点,同时以递归的方式实现对未知信号的最优估计。
 

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