一个通用的频谱误差校正快速算法

建立通用的频谱误差数学模型;得出利用信号频域能量信息反求信号幅值、利用信号频域能量重心信息反求信号频率的频谱误差校正算法。该算法适用于加任意对称窗的情形,且速度快、精度高,其有效性得到了数值仿真的验证。     

一种采用相位补偿的线谱相干检测器

分析了加窗和实信号情况下相干线谱检测器的检测性能。从频谱泄漏现象出发,通过对实信号做加窗处理,有效地降低了信号傅里叶变换后频谱泄漏的旁瓣级,提升了弱信号的探测能力;针对分段处理时同一频率不同数据段出现的相位差,采用相位补偿的方法,实现了各段数据的相干叠加。理论分析和计算机仿真表明,相比于平均功率谱检测器(Average Power Processor,AVGPR),加窗相位补偿检测器(Window Phase-Compensated Processor,WPCPR)有10 lg L(L为分段数)信噪比的提高,从而拓宽了相干线谱检测的适用范围。此外,还从频域波束形成的角度,给出了WPCPR能提高信噪比的一种物理解释,并指出了仿真中信噪比增益与理论值存在差异的原因。     

矩形窗三点插值傅里叶变换高精度频率估计方法

针对电力系统信号频率估计的高准确性要求,该文提出一种基于矩形窗的三点复频域插值频率估计方法。所提出的方法首先采用矩形窗对信号加权处理,然后进行离散傅里叶变换,最后选择离散频谱中幅度最大的三根谱线进行复频域插值,得到频率估计结果。该方法同时考虑离散频谱中的正、负频谱,消除短程和长程频谱泄漏对频率估计误差的影响。采用主瓣最窄的矩形窗进行加窗,能最大程度地减少白噪声对频率估计误差的影响。仿真和实验结果表明：在不同周期和噪声强度情况下,所提出的方法均可以实现对信号频率的准确估计。在与已有的加窗插值傅里叶变换方法相比,该文所提出的插值算法抗噪性更好、频率估计误差更小,适用于对电力系统信号频率的准确估计。     

窗谱校正方法的实用峰值搜寻算法研究

因窗函数的多样性及大部分窗函数的频谱公式过于复杂，利用窗函数频谱图形的校正方法在实际使用中有其局限性。本文通过对加窗信号的傅氏变换的分析，得出泄漏对信号的频域参量影响的定量表达式，推导出对信号的频域参量进行校正的一般性公式。在此基础上提出一种实用的窗函数频谱峰值搜寻算法。仿真结果表明窗谱图形峰值搜寻校正方法同样可达到窗谱公式校正方法的精度。     

应用频域ICA对参考输入进行预处理的ANC系统

传统有源降噪(ANC)理论和系统希望将声场能量全部抵消,不能适用于存在信号情况下的ANC应用,为尝试解决这一问题,本文提出了一种新的将频域独立成分分析(ICA)技术用于频域ANC系统,对系统参考输入进行预处理的方法.ICA的分离作用可以减少参考输入和信号的相关性,显著降低信号对ANc控制滤波器的干扰,减少了信号的失真,提高了系统收敛前后的声场信噪比.理论分析和仿真实验表明:该系统具有传统ANC系统所不具备的信号背景下降低噪声的能力.     

基于多重重叠技术的振动信号频谱分析方法研究

由于具有较小的旁瓣,汉宁、汉明和三角窗常被用于振动信号频谱分析中。但是,加这些窗会导致落在窗边缘的脉冲信号受到抑制而无法被有效检测出来,而脉冲信号中可能含有设备运行重要信息。针对此问题,研究了基于多重重叠技术的振动信号频谱分析方法并提出了相对最大值抽取的多重重叠算法。通过虚拟仿真信号和MFS-MG机械故障仿真试验台实测信号对多重重叠频谱分析方法进行了试验测试。虚拟仿真结果表明:边缘脉冲信号的多重重叠频谱幅值为一般频谱分析方法的8~10倍;试验台故障模拟信号的分析结果同样表明,多重重叠频谱分析方法比一般频谱分析方法更能表现边缘脉冲信号的宽频特征。     

用加窗插值法抑制振动信号工频噪声

对广泛存在的工频噪声,采用加窗插值方法开展研究,以抑制振动信号的工频噪声。首先,对比分析矩形窗、汉宁窗和六项余弦窗三种典型窗函数的特征;其次,采用数值仿真方法分析窗函数对插值计算结果的影响,当工频噪声频谱与有用成分频谱的间距不同时应选用不同的窗函数;最后,将加窗插值法用于实测振动信号的工频噪声抑制,当振动信号加汉宁窗时工频噪声的插值计算结果最准确,工频噪声抑制效果最好。数值仿真和工程应用表明加窗插值法用于振动信号工频噪声抑制是有效的。     

干扰抑制门在线谱提取中的应用

由于舰船辐射噪声和海洋环境噪声的复杂性,直接从舰船辐射噪声解调信号中提取线谱比较困难。为了降低线谱提取过程中宽带噪声干扰的影响,设计了一个干扰抑制门。对单频输入信号和谐波输入信号的仿真,验证了抑制算法的有效性,同时对干扰抑制门的信噪比增益进行了统计分析。对实测舰船辐射噪声数据处理,结果表明干扰抑制门的应用能有效降低信号中的宽带干扰,为线谱提取提供便利。从线谱中提取的螺旋桨轴频可作为舰船目标分类和识别的特征量。干扰抑制门算法亦可用于线谱检测。     

一种自适应的主动声呐直达波干扰抑制算法

为了克服主动声呐编队作战条件下的直达波干扰,达到检测微弱目标回波信号的目的．提出了一种基于频域分块自适应滤波算法的直达波抑制方法。这种方法将自适应滤波理论和匹配相关技术相结合,首先对接收数据做频域分块自适应滤波,再利用已知发射信号副本进行匹配相关,最后提取目标回波信号。其中,频域分块自适应滤波算法相比基本最小均方算法,计算复杂度降低,收敛速度快,基本保证了主动声呐每个探测周期内的直达波抑制和目标回波的提取。通过数值仿真实验表明：在强直达波干扰存在条件下,该算法对目标回波的提取在精度和鲁棒性等方面均优于以往的直接匹配相关方法。     

离散频谱分析比值校正法幅值和相位的抗噪性分析

谐波信号离散频谱分析的比值校正法(内插法)在无噪声时是一种准确的校正方法,只存在计算时的舍入误差,但在包含噪声尤其信噪比较低时,校正精度会有所下降,甚至误差很大.研究了比值校正法的幅值与相位加不同的窗函数及加性高斯白噪声时的统计方差公式,并通过不同信噪比下的仿真验证了其准确性.建议避免在归一化频率误差较低的情况下使用加矩形窗的比值校正法来校正相位.     

适用于MC DS-CDMA信号的定时同步算法

提出了一种适用于MC DS-CDMA信号的定时同步方案.该方案针对MC DS-CD-MA系统的特点,通过频域相关器输出矢量旋转同时获得多个同步检测统计量的近似值,既提高了捕获性能,又成倍降低了定时跟踪算法复杂度.理论分析和数值结果证明了方案的可行性.     

Implementing guided wave mode control by use of a phased transducer array

A multichannel time-delay system has been built and applied to a transducer array for implementing guided wave mode control. The time-delay system has a capability of sending high energy controllable tone-burst signals from eight independent channels with arbitrary time delays from 0 to 30 μs with resolution of 0.025 μs. Software time delays are also provided for summing up received signals of each channel. Theoretical discussions indicate the impact of the time delay capability on the bandwidth and sensitivity improvement of a transducer array for guided wave generation. Determination of both physical and software time delay values is based on a knowledge of dispersion curves and element spacing. Based on reference signals, a non-knowledge-based automatic time-delay searching algorithm was introduced for guided wave mode selection. Experiments were conducted with a phased comb transducer array mounted on a carbon steel pipe. The experimental results show that signal to noise ratio has been greatly improved by use of the time-delay system. Some other benefits of the phased array, including unidirection generation and mode control flexibility, are discussed     

混响环境中突发声源定向方法及性能

针对突发性声源在混响条件下的定向技术进行了研究。基于传统广义互相关时延估计的定向方法,通过对互相关函数计算结果进行判别与约束、对数据帧采用滑动方式、对时延估计结果进行能量加权选举等措施,有效提高了声源定向结果的稳定性。实验结果表明,改进的算法在混响条件下能有效地对突发性声源进行可靠定向,可用于对枪声、爆炸声等突发性信号进行精确定向和定位。     

An improved method for automatic detection and location of defects in electronic components using scanning ultrasonic microscopy

Deals with a method for the automatic analysis and characterization of defects due to encapsulation and/or surface mount processes of microelectronic devices. This method is based on digital signal processing of ultrasonic signals in the 10-100-MHz frequency range. In particular, it is used for automatic evaluation of time-of-flight between echoes received by the acoustic transducer. The signals are first pre-processed by a new algorithm, based on Wiener filtering, and then by a numeric algorithm, based on the wavelet transform, already applied successfully to this problem. The pre-processing phase increases the sensitivity of the successive numeric algorithm. The theory underlying the pre-processor and the chosen procedure to implement it are described in detail in this paper. Furthermore, experimental results obtained by applying the proposed method on acoustic signals from an electronic structure acquired through an ultrasonic scanning system are given and discussed.     

The extended function fast Fourier transform (EF-FFT)

The periodicity assumption implicit in fast Fourier transform (FFT) techniques can be utilized through time-domain prealiasing to obtain the spectral components of infinite-duration time-domain reflectometry signals when they can be modeled, outside the observation window, with step and/or exponential functions. The technique is shown to be more accurate than both conventional windowing and the other FFT approaches described in the literature for analysis of steplike signals. The duality equation relating the extension functions introduced in the extended function FFT (EF-FFT) method to conventional window functions is derived. Using this relation, it is shown that signals with high-frequency content only within the observation window are best analyzed with EF-FFT methods and that signals with time-distributed spectral components (e.g., speech) are best analyzed with conventional FFT methods     

双目标相干源窄带信号波达方向估计算法

利用水下目标发出的窄带信号,能够实现目标方向的精确估计。针对均匀线列阵接收水下目标窄带信号的特点,提出一种相干源波达方向(Direction of Arrival,DOA)估计的快速算法。通过精确估计目标信号对应的空间频率,直接计算出单目标或双目标信号的波达方向。对该算法进行了理论推导,通过仿真对算法进行验证,并对影响DOA估计精度的因素进行了分析。仿真结果表明,该算法具有较高的估计精度,不受信号时间长度的影响,算法计算量小,便于实时处理,具有较好的工程应用前景。     

Sweep signal time-delay estimation with time-frequency cross-correlation algorithm

Sweep signal is usually employed as a source signal in active detection such as radar and sonar. Since the frequency spectrum of the sweep signal varies against time, a novel algorithm, namely a time-frequency cross-correlation (TFCC) algorithm based on wavelet packet transform (WPT), is proposed to estimate the time delay of sweep signal. In this algorithm, the source sweep and the received signals are decomposed with WPT to obtain their time-frequency representations and the TFCC between the source sweep and the received signals is performed. Each reflected sweep in the received signal is converted into a time-frequency correlation peak whose position can indicate its time delay. The TFCC algorithm can suppress ambient noise effectively and improve the performance of sweep extraction and can match more precisely the source and the reflected sweeps to their known time-frequency characters. Numerical experiments were performed to compare the performance of the TFCC algorithm with that of the conventional cross- correlation and phase-data algorithms. The results proved that the TFCC algorithm can extract the reflected sweeps effectively and its performance is better than that of the conventional algorithms.     

Fourier-plane windowing in the binary joint transform correlator for multiple target detection

With recent advances in state-of-the-art spatial light modulators, the optical joint transform correlator (JTC) and the binary joint transform correlator (BJTC) are becoming practical signal-processing tools. The performance of these devices is limited by the difficulty of separating the cross correlation between the reference and the targets in the scene from signals resulting from cross correlations between objects in the target scene. One technique that reduces this problem is to use a sliding window in the Fourier plane as a convolution mask filter to set an adaptive binarization threshold. This suppresses the autocorrelation response and reduces the dynamic range of the Fourier-plane signal. This results in correlation performance improvement by a factor of 2 to 4. A mathematical model is developed to describe the windowing process for both the JTC and BJTC for the case in which the scene contains multiple targets and background clutter. The derivation of the windowing process is general and includes any spatial high-pass or bandpass filtering in the Fourier plane. The results are supported with experimental data.     

微波、毫米波相位噪声测试系统的研制

提出了首先对微波、毫米波信号进行下变频,再利用锁相环提取被测试信号相位噪声的相位噪声提取方法,采用现代谱分析技术对提取出的相位噪声信号在频率中进行分析,并利用"反卷积"技术实现测试系统的误差校正,研制实现了微波、毫米波相位噪声测试系统.实验测试结果表明该系统具有测试灵敏度高和被测信号频率范围广的优点,证明了它具有较大的应用价值.     

A study of indoor positioning systems using iBeacons with different transmission power levels

Abstract

The prevalence of smartphones has increased the demand for and application of location-based services. However, the Global Positioning System, currently the most widely used positioning technology, cannot provide accurate positioning services when obstructed by obstacles. Consequently, this system can only provide outdoor application services such as outdoor navigation and tracking. In 2013, Apple Inc. released iBeacon, a positioning technology based on Bluetooth low energy (BLE). This device transmits Bluetooth signals within a specific range, in which the signals are received by other smartphones to calculate distances for providing indoor positioning-related services. In this study, the iBeacon transmission power level is adjusted to significantly increase Bluetooth signal differences in indoor environments. Therefore, it can reduce received signal strength indicators (RSSI) similarity for some reference points by adjusting the power level. Subsequently, radio frequency signals are filtered using a modified moving average filter to reduce signal variations after reception. Next, pattern matching and the K-Nearest Neighbors (KNNs) algorithm are integrated to facilitate positioning. The integration of the modified moving average filter with the KNNs algorithm increases the positioning accuracy by 23.08% during the online phase. This finding can thus improve location-based services.