分布形式未知的矩法<英>

我们考虑分布形式未知的矩法,假设分布函数F_θ(x)可用一多项式表示或逼近。多项式的阶数r未知,其系数为未知参数θ_1,…,θ_r。我们给出了θ_1,…,θ_r的一种新的矩法估计,r可自动地求得,估计是强一致的。     

Nonlinear wavelet estimation of regression function with random desigm

The nonlinear wavelet estimator of regression function with random design is constructed. The optimal uniform convergence rate of the estimator in a ball of Besov spaceB ³ _p,q is proved under quite general assumpations. The adaptive nonlinear wavelet estimator with near-optimal convergence rate in a wide range of smoothness function classes is also constructed. The properties of the nonlinear wavelet estimator given for random design regression and only with bounded third order moment of the error can be compared with those of nonlinear wavelet estimator given in literature for equal-spaced fixed design regression with i.i.d. Gauss error. Project supported by Doctoral Programme Foundation, the National Natural Science Foundation of China (Grant No. 19871003) and Natural Science Fundation of Heilongjiang Province, China.     

使用稀疏贝叶斯学习的水声多途信道盲估计

提出了一种使用稀疏贝叶斯学习(SBL)的多途信号盲解卷积方法对水声多途信道的信道脉冲响应(CIR)进行盲估计。该方法利用垂直阵和多频SBL获得宽带舰船声源在不同垂直到达角上的复数域多频点信号,取其相位对垂直阵接收信号匹配滤波,得到每条路径上的CIR,将多路径CIR相干叠加得到最终的多途CIR结果。仿真与海试数据处理结果表明,相比于原有的基于交替投影的多途信号盲解卷积方法,所提出方法有以下几个好处:(1)无需准确预估多途信号数目;(2)分离的多途信号的方位更准确且信号相位更可靠;(3)有效获取了舰船与阵列之间的CIR.并且将弱路径CIR的平均时间估计误差从4.7 ms缩小到1.0 ms.显著提高了弱路径CIR的时间估计精度。使用稀疏贝叶斯学习的多途信号盲解卷积方法能够有效提高多途环境下水声信道盲估计的性能。     

抵消Wigner-Ville分布交叉项的窄带运动声源无源测速

利用声波的多普勒频移可以对窄带运动声源进行单传感器无源测速,其性能很大程度上取决于能否精确地估计出声波的瞬时频率.Wigner-Ville分布虽然时频分辨率高,但存在交叉项干扰,很少被直接用于瞬时频率估计。对此,提出了抵消Wigner-Ville分布交叉项的单传感器窄带声源无源测速方法。利用交叉项与声源速度的关系构造一个抵消项,引入到Wigner-Ville分布中,通过对声源速度估计值进行迭代更新,使抵消项与交叉项相位相反,从而约掉交叉项。经实测噪声数据验证,对一辆以6.07 m/s匀速运动的卡车(信噪比约为29 dB)测速误差为0.1 m/s,运行时间为4.6 s,对一架以28.90 m/s匀速运动的直升机(信噪比约为16 dB)测速误差为0.46 m/s,运行时间为1.2 s,均优于匹配Wigner变换和多普勒线性调频小波变换测速方法.     

Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy

Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon’s entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits.     

Parameter and state estimation algorithm for single-input single-output linear systems using the canonical state space models

This paper presents a new parameter and state estimation algorithm for single-input single-output systems based on canonical state space models from the given input–output data. Difficulties of identification for state space models lie in that there exist unknown noise terms in the formation vector and unknown state variables. By means of the hierarchical identification principle, those noise terms in the information vector are replaced with the estimated residuals and a new least squares algorithm is proposed for parameter estimation and the system states are computed by using the estimated parameters. Finally, an example is provided.     

结合幅度谱和功率谱字典的语音增强方法

从双路字典学习、噪声功率谱估计、语音幅度谱重构角度提出了一种改进的谱特征稀疏表示语音增强方法。在字典学习阶段,融合功率谱与幅度谱特征,采用区分性字典降低语音字典和噪声字典的相干性;在语音增强阶段,提出一种噪声功率谱估计方法对非平稳噪声进行跟踪估计;考虑到幅度谱和功率谱特征对不同噪声的适应程度不同,设计了语音重构权值表。对分别由幅度谱和功率谱恢复而来的两路信号进行自适应加权重构,结合相位补偿函数得到增强后的语音信号。实验结果表明,该方法在平稳、非平稳噪声环境下相比于单一谱特征的语音增强方法平均提高31.6%,改善了语音增强方法的性能。     

改进的信道稀疏度检测正交多载波多普勒估计方法

针对正交频分复用水声移动通信中频域变采样技术联合信道稀疏度检测的多普勒估计算法在冰水混合区复杂信道环境下多普勒估计效果欠佳且计算量大的问题,提出了一种改进的多普勒估计算法。采用时域重采样技术对信号进行不同压缩因子的补偿,避免了频域变采样技术中的高阶快速傅里叶变换运算,从而降低了算法复杂度。利用梳状导频位置向量结合时域变采样技术更准确提取的导频子载波,进而获得更加精确的信道估计和信道稀疏度检测结果,提高了多普勒估计精度。仿真结果和冰下试验结果表明,与原始算法相比,改进的方法在冰水混合水域复杂多径时变信道环境下可以有效实现对信号多普勒畸变的跟踪并且计算量显著下降,能够保障正交频分复用水声移动实时通信。     

Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR

Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBCLFM) signals require prior knowledge, are computationally complex, and exhibit poor performance at low signal-to-noise ratios(SNRs). To overcome these problems, a blind parameter estimation method based on a Duffing oscillator array is proposed. A new relationship formula among the state of the Duffing oscillator, the pseudo-random sequence of the PRBC-LFM signal, and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived, providing the theoretical basis for blind parameter estimation. Methods based on amplitude method, short-time Fourier transform method, and power spectrum entropy method are used to binarize the output of the Duffing oscillator array, and their performance is compared. The pseudo-random sequence is estimated using Duffing oscillator array synchronization, and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency. Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 d B.     

An Improved Tiered Head Pose Estimation Network with Self-Adjust Loss Function

As an important task in computer vision, head pose estimation has been widely applied in both academia and industry. However, there remains two challenges in the field of head pose estimation: (1) even given the same task (e.g., tiredness detection), the existing algorithms usually consider the estimation of the three angles (i.e., roll, yaw, and pitch) as separate facets, which disregard their interplay as well as differences and thus share the same parameters for all layers; and (2) the discontinuity in angle estimation definitely reduces the accuracy. To solve these two problems, a THESL-Net (tiered head pose estimation with self-adjust loss network) model is proposed in this study. Specifically, first, an idea of stepped estimation using distinct network layers is proposed, gaining a greater freedom during angle estimation. Furthermore, the reasons for the discontinuity in angle estimation are revealed, including not only labeling the dataset with quaternions or Euler angles, but also the loss function that simply adds the classification and regression losses. Subsequently, a self-adjustment constraint on the loss function is applied, making the angle estimation more consistent. Finally, to examine the influence of different angle ranges on the proposed model, experiments are conducted on three popular public benchmark datasets, BIWI, AFLW2000, and UPNA, demonstrating that the proposed model outperforms the state-of-the-art approaches.