基于PSO-ESPRIT算法的SAW温度传感器解调方法

目的 为了提高声表面波(Surface acoustic wave,SAW)温度传感器的测量精度,设计一种基于PSO-ESPRIT算法的高精度SAW温度传感器解调方法。方法 以ESPRIT谱估计方法为基础,把Hankel矩阵的时间窗长度与计算噪声方差时的K值作为粒子群优化(Particle swarm optimization, PSO)算法的输入变量,并以频率估计标准差作为粒子的适应度函数,利用PSO对ESPRIT算法中的参数进行优化,以改善频率估计精度,从而提高SAW回波信号频率估计的分辨率,实现SAW温度传感器的高精度解调。结果 仿真和实验结果表明,所设计的方法与其他谱估计算法相比,其对SAW回波信号估计的频率误差最小,标准差小于0.66kHz。把设计的算法用于SAW温度传感器的温度解调,得到的温度值与实际温度的误差小于0.4℃。结论 测试结果说明,设计的温度解调方法提高了SAW回波信号频率解调精度,可用于SAW温度传感器的解调,实现了对食品包装储运过程中温度的实时监测。     

基于调制FFT的谐振式SAW传感器快速频率估计算法

针对高动态环境下的谐振式声表面波（Surface Acoustic Wave,SAW）传感器快速精确频率估计,提出一种基于调制快速傅里叶变换（Fast Fourier Transform,FFT）的谐振式SAW传感器快速频率估计算法。对单次谐振式SAW传感器回波信号进行N点取样后进行调制FFT计算,获得回波信号频谱,然后利用最大谱线的两相邻谱线取代I_Rife算法频谱细化后的谱线对频率偏移因子进行估计,最后使用频率偏移因子对最大频谱频率进行修正。该算法较I_Rife算法不需要判断频率修正方向,减少了3N次复数乘法和4（N-1）次复数加法运算,频率估计均方根误差的平均值减小了26%。该算法在提高精度的同时,实现了对谐振式SAW传感器的快速频率估计。     

声表面波温度传感器抗干扰技术研究

目的设计一种声表面波(SAW)温度传感器抗干扰技术,以提高温度测量的稳定性。方法分析SAW谐振器(SAWR)回波特性,建立SAWR回波信号熵能量模型,发现SAWR回波信号的衰减过程与熵能量的上升过程对应。当回波信号达到噪声水平时,熵能量的单调上升过程消失。为了抑制正弦干扰设计一种改进型自相关算法,利用该算法对信号进行去噪的同时使谐振器回波信号的衰减特性和正弦干扰的等幅特性得到保持。结果根据模拟仿真结果设置了SAWR回波信号的检测阈值(V_(thre)=1),并对该阈值进行了蒙特卡罗仿真实验。仿真结果表明,当信号信噪比大于4dB时,SAWR回波信号的检测率达到86%,而正弦干扰误检率小于0.5%。最后应用该算法对实际的正弦信号和SAWR回波信号进行了检测,得到的误检率接近于0。结论实验结果显示,所设计的算法可以用作声表面波温度传感器的抗干扰技术。     

声表面波谐振器型振荡器的研制

本文介绍了金属条射栅双端对声表面波谐振器型的原理和结构特点，给出了一种采用声表面波谐振器稳频的低噪声，高稳定性的振荡器电路设计方案。对影响振荡器频率稳定度的因素进行了分析讨论，并探讨改善声表面波振荡器频率稳定性的方法。该声表面波谐振器的中心频率为１２０ＭＨｚ，无载Ｑｖ，大于２０，０００，插入损耗小于６．０ｄＢ，经测试，秒级频率稳定度为１０＾－１０数量级，在自由室温下的日平均波动为１０＾－６／ｄ数量     

基于SVD-Rife算法的声表面波应变传感器高精度解调方法设计

目的提出一种可用于储运材料表面应变检测的声表面波(SAW)检测方法,设计一种基于SVD-Rife算法的高精度SAW应变传感器解调方法。方法利用SVD方法实现对SAW谐振器回波信号去噪,以提高解调精度;基于Rife算法设计一种谱细分估计方法,该方法可减少系统的硬件要求,提高谱估计精度。结果对设计的解调方法进行了仿真和实验,仿真结果表明估计方差得到改善,最大误差为0.25 kHz。对提出的SAW应变传感器进行了实验,得到的传感器线性度为1.45%,重复性为1.09%,与传统的解调方法相比线性度得到改善。结论实验结果显示所设计的解调方法可用于储运材料表面应变的检测。     

含噪多频信号频率估计算法

为抑制多频信号中非待估计频率分量频谱泄漏对频率估计的影响,提出一种新的频率估计算法。利用快速傅里叶变换(FFT)算法对采样信号进行预处理,得到每个频率分量的频谱索引,从而得到各频率分量粗略的幅值和初相位估计值;采用频率搬移策略滤除多频信号中的非待估计频率分量,得到降频信号;对降频信号进行频谱分析,并经迭代计算得到各频率分量信号精确的频率、幅值和初相位估计值。在无噪声、不同信噪比等条件下进行了仿真实验,结果表明,所提算法具有良好的频率估计性能,有效抑制了多频信号中频谱泄漏的影响,提高了频率估计精度,优于现有优秀算法。     

采用双本振源激励的声表面波谐振器无线测量系统

在声表面波无线传感的实际应用中,针对以差动形式同时出现的两个声表面波谐振器设计了一种采用双本振源激励的阅读器,每次产生包括两个载波频率的激励信号,使两个谐振器同时响应。实际搭建了无线测量系统,对阅读器的发射和接收链路进行了测试,并通过上位机实时显示谐振频率,测量结果表明了系统的可靠性。最后,实验分析了系统在不同载波频率激励信号下的差动频率测量结果和最远测量距离。     

基于ADS半物理平台的声表面波标签测试

提出了一种基于ADS半物理平台的声表面波标签测试方法。采用矢量网络分析仪实际测试声表面波标签实物的S参数,在ADS软件中构建标签的物理抽象模型,并虚拟构建标签激励信号,利用ADS瞬态仿真功能获得标签回波信号,再采用MATLAB对回波进行数字正交解调,从而客观准确地测量标签各反射栅的时延、相位。最后通过搭建的半物理平台对声表面波标签进行了测试,表明了ADS半物理平台的有效性。     

实复转换式衰减信号参数估计算法

为抑制衰减实信号中负频率成分对参数估计的影响,提出一种实复转换式参数估计算法。预估计采样信号频谱能量最大值点的索引值;构造只含有负频率成分的参考信号,并将采样信号和参考信号相减实现实复转换,以抑制负频率频谱泄漏的影响;利用频谱两点插值算法得到频率偏差、衰减因子和复幅值的粗估计值,并重新生成参考信号和复信号;通过迭代计算得到精确的频率、衰减因子、初幅值和初相位估计值。以频率估计为例的仿真实验结果表明:所提算法可有效地抑制负频率频谱泄漏的影响,提高中高信噪比条件下的频率估计精度,特别是信号频率较低时的频率估计精度,提升了频率估计的综合性能。此外,在科氏流量计中进行了实测实验,检验了所提算法的有效性。     

一种激光多普勒信号的频率估计算法

分析了激光多普勒回波信号的特征,提出了一种激光多普勒信号的频率估计算法.用FFT(Fast Fourier Transform)技术求得信号的自相关函数,并由功率谱得到信号频率的粗估计,然后用粗估计值对自相关函数移频,并根据移频信号自相关函数在一点的相位,估计频偏,对粗估计进行频率校正得到频率估计值.在进行信号频率粗估计和求相位时,利用计算过程中得到的结果和FFT因子的对称性,减少运算量.仿真结果表明,本算法有较小的均方根误差和平均绝对误差,应用于激光多普勒测速实验,结果与仿真一致.     

基于全相位HHT的瞬时频率测量

全相位谱分析能够很好地抑制频谱泄漏、优化系统性能,其关键技术在于对数据进行全相位预处理。将这一技术运用于HHT求解瞬时频率中,提出了一种全相位HHT的瞬时频率测量算法,分析了全相位预处理和HHT求解瞬时频率的算法,将信号首先进行全相位预处理,然后用HHT算法求解信号的瞬时频率。这种算法是一种优化算法性能的算法,不会过多增加系统的计算量。通过对一非平稳信号分析,验证了基于全相位HHT的瞬时频率方差更小,具有更高的精确性。     

STFT及其在回声消除方面的应用

论述了STFT(Short-Time Fourier Transform)理论及其特点,设计了一个在桌面音频系统中由于麦克风和听筒的耦合而产生语音干扰(主要是回声)的消除系统,该系统利用STFT将所需信号和噪声信号变换到频域,通过频谱相减消除噪声。提出了一个基于多信道回声消除器的子带最小二乘滤波器理论分析和估算方法,该方法具有较强的自适应能力、算法收敛快、控制精度高。     

一种新的相位编码信号多普勒补偿算法研究

相位编码信号是一种多普勒频率敏感信号,而且码长越大,对多普勒频率越敏感,严重影响了雷达对高径向速度目标和远距离目标探测的能力。该文在分析伪码序列与多普勒容限关系的基础上,提出一种新的伪码调相准连续波雷达的多普勒补偿算法。该算法将回波信号取平方后送到动目标检测(MTD)滤波器中,通过提取回波信号中的多普勒频率信息,进行多普勒补偿。从仿真结果可以看出,该算法能够有效地降低多普勒频率对脉冲压缩结果的影响,验证了方法的可行性。     

FFT幅相联合的快速高精度频率估计方法

快速傅里叶变换（Fast Fourier Transform,FFT）常用于信号频率估计,采用填零的方法可降低幅度谱频率搜索间隔的量化误差,但是会使频率估计的计算量成倍增加。本文提出了一种FFT幅相联合的快速高精度频率估计算法,首先利用信号采样的频谱序列和尾首样本差确定幅度谱及峰值位置,然后由频谱序列在幅度谱峰值位置和信号采样的尾首样本差来确定频率搜索间隔的量化误差校正值。因此,所提方法同时利用了幅度谱峰值的位置信息与相位信息。分析结果表明,与仅基于幅度谱搜索的FFT算法相比,所提方法的计算复杂度更低,且定位精度更高。     

A successive parameter estimation algorithm for chirplet signal decomposition

In ultrasonic imaging systems, the patterns of detected echoes correspond to the shape, size, and orientation of the reflectors and the physical properties of the propagation path. However, these echoes often are overlapped due to closely spaced reflectors and/or microstructure scattering. The decomposition of these echoes is a major and challenging problem. Therefore, signal modeling and parameter estimation of the nonstationary ultrasonic echoes is critical for image analysis, target detection, and object recognition. In this paper, a successive parameter estimation algorithm based on the chirplet transform is presented. The chirplet transform is used not only as a means for time-frequency representation, but also to estimate the echo parameters, including the amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate. Furthermore, noise performance analysis using the Cramer Rao lower bounds demonstrates that the parameter estimator based on the chirplet transform is a minimum variance and unbiased estimator for signal-to-noise ratio (SNR) as low as 2.5 dB. To demonstrate the superior time-frequency and parameter estimation performance of the chirplet decomposition, ultrasonic flaw echoes embedded in grain scattering, and multiple interfering chirplets emitted by a large, brown bat have been analyzed. It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements. Numerical and analytical results show that the algorithm is efficient and successful in high-fidelity signal representation.     

Ultrasonic data compression via parameter estimation

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.     

A Simple Recursive Algorithm for Simultaneous Magnitude and Frequency Estimation

A new approach in the design of digital algorithms for simultaneous local system magnitude and frequency estimation of a signal with time-varying frequency is presented. The algorithm is derived using the maximum likelihood method. The pure sinusoidal voltage model was assumed. The investigation has been simplified because the total similarity to the state of the problem of dc offset and frequency estimation has been noticed. Finite impulse response (FIR) digital filters are used to minimize the noise effect and to eliminate the presence of harmonic effects. The algorithm showed a very high level of robustness, as well as high measurement accuracy over a wide range of frequency changes. The algorithm convergence provided fast response and adaptability. This technique provides accurate estimates in about 25 ms and requires modest computations. The theoretical bases of the technique are described. To demonstrate the performance of the developed algorithm, computer-simulated data records are processed. The proposed algorithm has been tested in a laboratory to establish its feasibility in a real-time environment.