小波能量积累器在信号处理中的应用

 根据小波变换的多尺度分解特性,提出了多尺度小波能量积累器的概念,并给出了相应的多尺度小波能量积累算法,将信号在不同分解尺度上的小波能量进行积累。在此基础上设计出一种适用于各种雷达,尤其是微波雷达弱信号处理的小波能量检测算法,对低信噪比信号检测进行了仿真实验。结果表明,该检测算法对强白噪声及强色噪声背景下的低信噪比信号的检测非常有效。     

自适应陷波滤波器应用研究

本文介绍自适应陷波滤波器的基本理论,提出了设计极窄带自适应陷波器和滤波器的方法,介绍了用作相位估计器的概念。作者证明自适应相位估计器是理想条件下的最大似然比相位估计器,计算机仿真研究和硬件实验结果均表明该估计器有良好的性能,它的显著优点是同时完成了信号的检测和相位差的估计。     

脉冲声信号影响锥特征的自适应选取EI北大核心CSCD

为了解决低信噪比下脉冲声信号影响锥特征的自适应选取和检测问题,提出了一种改进的整体嵌套边缘检测方法。利用脉冲信号小波域的时间-尺度分析谱图中明显的边缘效应特征,构造自适应影响锥(A-COI)模型。该模型可自适应输出最适影响锥部分,在减弱噪声干扰的同时最大程度的包含了脉冲信号的主要特征。进而将最适影响锥部分对应的小波系数用于脉冲信号检测,有效提升了低信噪比下的检测性能。对典型直升机桨-涡干扰脉冲信号的仿真和实验数据进行分析,结果表明在0 dB,2 dB,5 dB信噪比的复杂环境下,使用基于A-COI模型的检测率分别达到了65.13%,82.33%,95.27%,相对于传统固定大小影响锥的检测算法提升了42.42%,22.99%和2.36%。     

高速运动目标的自适应线谱增强算法研究

本文研究了低信噪比、目标高速运动情况下,多普勒频移较大时针对线谱信号的自适应增强算法。对海试数据的分析表明,常规自适应线谱增强算法对多普勒频移下的线谱增强效果明显,但要求信噪比较高,且实时性较差;基于高阶累积量对角切片的自适应线谱增强算法对非线性高斯色噪声下的线谱增强效果显著,但对低信噪比环境下变化线谱的跟踪性能较差;频域批处理自适应线谱增强算法对低信噪比下的线谱信号具有很好的增强效果,降低了运算量。     

激光多普勒测量自适应陷波器去噪研究

针对激光多普勒测量中声光调制、电光调制等引入的多频率噪音干扰,提出一种兼顾收敛性、鲁棒稳定性的自适应陷波器去噪方法.并在时域内研究自适应陷波器算法及其构造,最后给出了仿真实例.该算法不需具体的激光多普勒测试系统模型,方法简单,易于实现,试验结果表明了该方法的有效性.     

一种提高激光告警器性能的自适应方法

激光告警是激光对抗的重要前提和基本手段，如何在复杂背景噪声下识别来袭激光信号，进而准确报警是激光告警面临的首要问题。该文分析了激光告警设备检测概率与信噪比的关系，从关系式可以看出，为了提高激光告警设备的检测概率，需要增加激光信号的信噪比。提出了一种最大信噪比的自适应算法，进行了算法分析，并利用矩阵变换作出了算法简化，从而保证了系统信噪比的最佳化。最后对最佳权向量的求解这个问题，给出了适用于工程化的最佳权向量求解的简单迭代算法。     

小波包自适应阈值语音降噪新算法

为了克服低信噪比输入下,语音增强造成语音清音中的弱分量损失,造成重构信号包络失真的问题。论文提出了一种新的语音增强方法。该方法根据语音感知模型,采用不完全小波包分解拟合语音临界频带,并对语音按子带能量进行清浊音区分处理,在阈值计算上,提出了一种清浊音分离,基于子带信号能量的小波包自适应阈值算法。通过仿真实验,客观评测和听音测试表明,该算法在低信噪比输入时较传统算法,能够更加有效地减少重构信号包络失真,在不损伤语音清晰度和自然度的前提下,使输出信噪比明显提高。将该算法与能量谱减法结合,进行二次增强能进一步提高降噪输出的语音质量。     

空时多普勒频移域运动小目标的抗干扰探测方法

有源声呐使用多普勒敏感信号可在多普勒频移域分离杂波、混响等干扰和运动目标,但低信干比时强干扰的时间旁瓣泄露和多普勒旁瓣泄露会淹没弱目标.针对该问题,提出了一种基于自适应最小均方(LMS)算法和宽带模糊函数(WAF)的运动小目标抗干扰探测方法.首先在空间多普勒频移域上利用基于LMS的自适应陷波器抑制干扰,然后在快时间多普...     

应用于助听器反馈抑制的信噪比自适应滤波算法*

针对基于自适应滤波器的助听器反馈抑制系统,本文提出了一种基于信噪比的归一化最小均方误差算法,采用最小值统计法估计误差信号的噪声分量,从而计算出误差信号的信噪比来计算自适应滤波系数的更新步长。当误差信号信噪比越高,语音占主要成分,信号的相关性越强,此时将滤波器的更新步长控制在较小值,减小滤波器的失调量。当信噪比越低时,噪声占主要成分,信号的相关性相对较弱,更新步长取较大值,加快滤波器的收敛速度。在仿真实验中,本文提出的基于信噪比的归一化最小均方误差算法相较于传统算法在平均稳态失调量和稳态失调范围上分别低1dB和2dB,其最大稳态增益提高了4dB,同时具有更快的稳态收敛速度,验证了本文提出算法的有效性。     

一种检测低信噪比下未知波形时变相干信号的方法*

从获取的数据中检测目标信号,是雷达、声呐等领域重要的研究内容。时频域严重畸变的回波信号以及时变线谱信号的自适应检测,具有重要意义。为了挖掘和推广自适应相干累积（Adaptive Coherent Integration,ACI）技术在未知时变信号检测领域的能力,本文给出了ACI算法的基本原理,并进行了系统性地理论推导,得到了ACI算法的宽带时变自回归滑动平均模型（ARMA）和窄带复解析模型。利用这些模型解释了产生信号相干累积的机理,分析得出了产生相干累积的条件,揭示了这种时变系统的许多奇异特性。结合仿真实验和实际海上实验数据对ACI算法展开验证,结果表明ACI算法对于低信噪比下未知波形信号具有优异的检测能力,展示出ACI算法在水下探测等相关领域存在广泛的实际应用前景。     

Lattice form adaptive infinite impulse response filtering algorithm for active noise control

In some situations of active noise control, infinite impulse response (IIR) filters are more suitable than finite impulse response (FIR) filters owing to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control; however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability and relatively slow convergence speed for noise composed of narrow-band components with large power disparity. To overcome these difficulties along with using the direct form IIR filters, a new adaptive algorithm is proposed in this paper, which uses and updates the lattice form adaptive IIR filter in an active noise control system. Full mathematical derivations of the proposed algorithm are presented, and the comparison between the proposed algorithm and the commonly used filtered-u LMS and filtered-v LMS algorithms shows the superiority of the proposed algorithm.     

Detection of weak signals using fast projection algorithms

The problem of detecting weak signals in complex noise situations using projection adaptive algorithms is considered. The existing algorithms are analyzed, and a novel algorithm oriented at detecting weak signals is proposed. Results of the algorithm’s operation are demonstrated in a simulated noise situation consisting of interference signals with different intensities under the assumption of their multipath propagation and scattering. The proposed algorithm is compared with the well-known classical Capon algorithm, and a significant reduction in the contact loss time as applied to a low-noise target near strong interference sources is demonstrated.     

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.     

Improving performance of FxRLS algorithm for active noise control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. New impulsive noise control algorithms based on filtered-x recursive least square (FxRLS) algorithm are presented. The FxRLS algorithm gives better convergence than the filtered-x least mean square (FxLMS) algorithm and its variants but lacks robustness in the presence of high impulsive noise. In order to improve the robustness of FxRLS algorithm for ANC of impulsive noise, two modifications are suggested. First proposed modification clips the reference and error signals while, the second modification incorporates energy of the error signal in the gain of FxRLS (MGFxRLS) algorithm. The results demonstrate improved stability and robustness of proposed modifications in the FxRLS algorithm. However, another limitation associated with the FxRLS algorithm is its computationally complex nature. In order to reduce the computational load, a hybrid algorithm based on proposed MGFxRLS and normalized step size FxLMS (NSS-FXLMS) is also developed in this paper. The proposed hybrid algorithm combines the stability of NSS-FxLMS algorithm with the fast convergence speed of the proposed MGFxRLS algorithm. The results of the proposed hybrid algorithm prove that its convergence speed is faster than that of NSS-FxLMS algorithm with computational complexity lesser than that of FxRLS algorithm.     

Fast phase retrieval in slightly off-axis digital holography

In this study, three efficient algorithms are proposed for fast phase retrieval in slightly off-axis digital holography using spectrum cropping, spatial multiplexing, and complex encoding. In the first algorithm, the real spectral order of the subtracted hologram is filtered and cropped, and the number of pixels is decreased in the subsequent retrieval operations. In the second algorithm, two sequential subtracted holograms are digitally phase shifted and spatial multiplexed into one synthetic hologram, and thus only one inverse Fourier transformation is then required. In the third algorithm, two sequential subtracted holograms are encoded separately into the real part and the imaginary part of a complex hologram. Two cross-correlations can be used to reconstruct the phase, thereby improving the utilization of the spectrum. The three new algorithms speed up our previously proposed retrieval method with the assistance of specimen-free holograms. Our experiments demonstrated the validity and improved time requirements of the proposed methods.     

一种ZF与SP结合的BLAST译码算法

多输入多输出虽能显著增加信道容量,但译码复杂度与精度一直是亟待解决的核心问题之一。将现有的贝尔实验室分层空时码检测迫零算法与球形译码算法充分结合,重点考察无线多输入多输出信道基本特征即信道条件数与信噪比,提出了一种自适应的贝尔实验室分层空时码解码算法,在保证误比特率性能的条件下,降低了系统的译码复杂度;为无线通信的长期演进研究奠定了良好基础。     

A robust negative obstacle detection method using seed-growing and dynamic programming for visually-impaired/blind persons

Though a significant amount of work has been done on detecting obstacles, not much attention has been given to the detection of drop offs, e.g., sidewalk curbs, downward stairs, and other hazards. In this paper, we propose algorithms for detecting negative obstacles in an urban setting using stereo vision and two-stage dynamic programming (TSDP) technique. We are developing computer vision algorithms for sensing important terrain features as an aid to blind navigation, which interpret visual information obtained from images collected by cameras mounted on camera legs nearly as high as young person. This paper focuses specifically on a novel computer vision algorithm for detecting negative obstacles (i.e. anything below the level of the ground, such as holes and drop-offs), which are important and ubiquitous features on and near sidewalks and other walkways. The proposed algorithm is compared to other algorithms such as belief propagation and random growing correspondence seeds (GCS). According to the results, the proposed method achieves higher speed, more accurate disparity map and lower RMS errors. The speed of the proposed algorithm is about 28% higher than the random GCS algorithm. We demonstrate experimental results on typical sidewalk scenes to show the effectiveness of the proposed method.     

IMPLEMENTATION OF A BROADBAND DUCT ANC SYSTEM USING ADAPTIVE SPATIALLY FEEDFORWARD STRUCTURE

An adaptive spatially feedforward algorithm is proposed for broadband attenuation of noise in ducts. Acoustic feedback generally exists in this active noise control structure. Munjal and Eriksson (1988 Journal of Acoustical Society of America84, 1086-1093) derived an ideal controller for the spatially feedforward structure. The ideal controller can be partitioned into two parts. The first part represents a repetitive controller that can be implemented by an infinite impulse response (IIR) filter, whereas the second part represents the dynamics of transducer that can be implemented by a finite impulse response (FIR) filter. In the paper, the IIR filter is merged with the original plant. The FIR filter is adaptively updated by the least-mean-square (LMS) algorithm to accommodate perturbations and uncertainties in the system. The proposed algorithm is implemented via a floating point digital signal processor and compared with other commonly used algorithms such as the Filtered-X LMS algorithm, the feedback neutralization algorithm, and the Filtered-U LMS algorithm. Experimental results show that the system has attained 15·7 dB maximal attenuation in the frequency band 200-600 Hz.     

Fast free-surface detection and level-set function definition in SPH solvers

The present paper proposes a novel algorithm to detect the free-surface in particle simulations, both in two and three dimensions. Since the proposed algorithms are based on SPH interpolations their implementation does not require complex geometrical procedures. Thus the free-surface detection can be easily embedded in SPH solvers, without a significant increase of the CPU time. Throughout this procedure accurate normal vectors to the free-surface are made available. Then it is possible to define a level-set function algorithm which is presented in detail. The latter allows in-depth analyses of three-dimensional free-surface simulations by using standard visualization tools, including internal features of the flow. The algorithms proposed for detecting free-surface particles and defining the level-set function are validated on simple and complex two- and three-dimensional flow simulations. The usefulness of the proposed procedures to post-process and analyze complex flows are illustrated on realistic examples.     

数字信号处理器红外弱小目标搜索算法

提出了一种基于数字信号处理器（DSP）的红外弱小目标搜索高速实时算法。算法主要针对天空中不规则云团在预处理过程中残留的边缘而设计,是在背景预测算法基础上改进的一种基于陷波滤波器的搜索算法。在DSP实时处理平台上实现并经实际场景试验验证,该算法能有效削弱残留边缘的影响,使最远探测距离指标上升至前向迎头26 km,实时处理速度达到75 Hz,满足系统要求。采用信噪比增益指标对算法进行了评价,结果显示,该算法能够显著提高图像信噪比,利于检测出目标。