综合射频信号处理平台中多功能核心板卡的主备切换方法

本文主要对综合射频信号处理平台中一种多功能核心板卡的主备切换方法进行了分析与设计。从平台架构出发,通过对主备切换的触发条件、涉及的功能以及切换所需的关键技术点进行分析,详细论证了进行主备切换时的技术需求、操作逻辑、关键技术点,并完成了相应的方案设计及测试验证。

基于相参频谱合成技术的步进频信号处理

调频步进信号指的是采用线性调频信号取代简单脉冲,作为步进频信号子脉冲的信号形式。采用基于相参频谱合成技术的步进频信号处理方法,解决传统步进频信号处理方法距离模糊严重、易于产生伪峰的问题。理论分析和仿真结果表明,只要参数设计合理,该方法能够有效实现步进频信号的子脉冲合成,得到可靠的高分辨距离信息。

基于小波消噪的EMD模型在GPS振动信号处理中的应用

经验模式分解和小波分解是当前有效处理非平稳信号的两种时频分析方法,它们各具有其优缺点,适用于不同的应用。从对GPS振动信号的预处理、时域和频域处理入手,结果分析表明,对GPS振动信号先进行小波滤波消除随机噪声的干扰,再应用经验模式分解更有利于变形特征信息的分离和提取,提取的信号与振动平台记录数据更加吻合。

数字BPM电子学在束流位置测量中流强依赖性研究

在BPM测量系统中,流强依赖(BCD)是影响BPM测量精度和可靠性的一个重要因素,本文通过对BPM信号处理链路分析、BPM计算方法的研究,探索在BPM测量过程中产生流强依赖的主要原因;并利用Matlab模拟实验、实验室测试、实际束流测试,找到解决BPM测量流强依赖的方法。研究结果表明,BPM信号处理通道的非线性、ADC的直流偏置是产生BPM测量流强依赖的两个主要因素。本文经过对BPM射频信号处理电路改进,对BPM信号算理算法的优化工作,解决了自研数字BPM电子学的流强依赖性问题。

电磁辐射可视化测量分析仪设计

结合天线与射频技术结合可视化开发环境,研究出可视化辐射检测仪。介绍了宽带全极化信号接收天线设计,由高精度AD8312射频检波管和LM741集成放大芯片组成的射频检波电路,结合模数转换和主控平台以及可视化软件的设计。研究了辐射数据标定与串口通讯数据传输以确保数据的真实性。结合电磁信息可视化处理,能够测量所在空间的电磁信号强度,将强度转化为颜色的深浅,实现整个测量场景电磁辐射分布的可视化图形展现。

FAST WAY FOR MOVING OBJECT TRACKING BASED ON BALLOON SNAKE WITH REGION INFORMATION

A novel Snake model with region information is proposed to detect and track moving objects. Generally, the region-information-based approach is sensitive to illumination changes and small movement in the background, while the edge-information-based approach often obtains incorrect results for ambiguous images. The two types of information are introduced in computing the image force. Edge-information-based features make the algorithm fast and robust, and region information makes the active confour energy function obtains correct results for ambiguous images. Furthermore, an automatic contour initialization method using double difference images is given to meet the requirement of video sequence tracking. Meanwhile, a simple forecast section is added to estimate the position of the contour in the algorithm so that it can improve the convergence speed of the active contour. Experimental results show that the computation time of the algorithm is less than 0.1 s/frame. And it can be applied to a real-time system.

索爱“机皇”T618VS“机王”P802

图1、图2展示的是索尼爱立信时下最红火的机型T618与P802的PCB板图。就手机功能而论，T618远远不及P802强大而又够智能。须知，P802还是具有“飞行模式”且在众手机中是第一台可以在飞机上使用的商务机器呢!从电路布局可以看出，T618更为简洁，而P802在芯片数目与电路集成上都有增无减。

A novel blind deconvolution algorithm using single frequency bin

Former frequency-domain blind devolution algorithms need to consider a large number of frequency bins and recover the sources in different orders and with different amplitudes in each frequency bin,so they suffer from permutation and amplitude indeterminacy troubles. Based on sliding discrete Fourier transform,the presented deconvolution algorithm can directly recover time-domain sources from frequency-domain convolutive model using single frequency bin. It only needs to execute blind sepa-ration of instantaneous mixture once there are no permutation and amplitude indeterminacy troubles. Compared with former algorithms,the algorithm greatly reduces the computation cost as only one frequency bin is considered. Its good and robust per-formance is demonstrated by simulations when the signal-to-noise-ratio is high.

Ultrasonic flaw detection using EMD-based signal processing

The precise detection of flaw echoes buried in backscattefing noise caused by material microstructure is a problem of great importance in uhrasonic non-destructive testing （NDT）. In this work, empirical mode decomposition （EMD） is proposed to deal with ultrasonic signal. A time-frequency filtering method based on EMD is designed to suppress noise and enhance flaw signals. Simulated results are presented, showing that the proposed method has an excellent performance even for a very low signal-to-noise ratio （SNR）. The improvement in flaw detection was experimentally verified using stainless steel pipe sample with artificial flaws.

Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency

The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.

ALGORITHM FOR THE DETECTION AND PARAMETER ESTIMATION OF MULTICOMPONENT LFM SIGNALS

A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation. Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noisc Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.

A New Range Sidelobe Suppression Technique for Randomly Intermittent Spectra HF Radar Signal

The randomly intermittent spectra (RIS) signal is employed to combat spectrum congestion in radar and other radio services to evade the external interferences in high-frequency (HF) and ultrahigh-frequency (UHF) bands. However, the spectra discontinuity of the signal gets rise to high range sidelobes when matching the reflected echo, which is much more difficult for targets detection. So it is indispensable to investigate the technique for sidelobes suppression of the range profile when RIS signal is utilized, This paper introduced a new processing technique based on time domain filtering to lower the range sidelobes. A robust and effetive algorithm is adopted to solve the coefficients of the filter, and the restriction on the desired response of the filter is derived. The simulation results show that the peak range sidelobe can be reduced to -27 dB from -9.5 dB while the frequency band span (FBS) is 200 kHz.

Particle swarm optimization based RVM classifier for non-linear circuit fault diagnosis

A relevance vector machine (RVM) based fault diagnosis method was presented for non-linear circuits. In order to simplify RVM classifier, parameters selection based on particle swarm optimization (PSO) and preprocessing technique based on the kurtosis and entropy of signals were used. Firstly, sinusoidal inputs with different frequencies were applied to the circuit under test (CUT). Then, the resulting frequency responses were sampled to generate features. The frequency response was sampled to compute its kurtosis and entropy, which can show the information capacity of signal. By analyzing the output signals, the proposed method can detect and identify faulty components in circuits. The results indicate that the fault classes can be classified correctly for at least 99% of the test data in example circuit. And the proposed method can diagnose hard and soft faults.

Application of Hilbert-Huang signal processing to ultrasonic non-destructive testing of oil pipelines

In this paper, a detection technique for locating and determining the extent of defects and cracks in oil pipelines based on Hilbert-Huang time-frequency analysis is proposed. The ultrasonic signals reflected from defect-free pipelines and from pipelines with defects were processed using Hilbert-Huang transform, a recently developed signal processing technique based on direct extraction of the energy associated with the intrinsic time scales in the signal. Experimental results showed that the proposed method is feasible and can accurately and efficiently determine the location and size of defects in pipelines.

Time-Frequency Signal Processing for Gas-Liquid Two Phase Flow Through a Horizontal Venturi Based on Adaptive Optimal-Kernel Theory

A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%.

BLIND ADAPTIVE MULTIUSER DETECTION BASED ON A CONSTRAINED MMSE CRITERION

The proposed blind adaptive multiuser detector utilizes the signature waveform and time information of the desired user. With each received sample vector, the proposed algorithm updates the detector and gives the symbol estimate in the current time slot. Such property facilitates it to track time-varying channels.

Audio Zero-Watermark Scheme Based on Discrete Cosine Transform-Discrete Wavelet TransformSingular Value Decomposition

Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD) is presented.The watermark is registered by performing SVD on the coefficients generated through DWT and DCT to avoid data modification and host signal degradation.Simulation results show that the proposed zero-watermarking algorithm is strongly robust to common signal processing methods such as requantization,MP3 compression,resampling,addition of white Gaussian noise,and low-pass filtering.

Instantaneous frequency estimation of multi-component Chirp signals in noisy environments

A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency （IF） is very useful. But in noisy environments, it is hard to estimate the 1F of a multi-component Chirp signal accurately. Wigner distribution maxima （WDM） are usually utilized for this estimation. But in practice, estimation bias increases when some points deviate from the true IF in high noise environments. This paper presents a new method of multi-component Chirp signal 1F estimation named Wigner Viterbi fit （WVF）, based on Wigner-Ville distribution （WVD） and the Viterbi algorithm. First, we transform the WVD of the Chirp signal into digital image, and apply the Viterbi algorithm to separate the components and estimate their IF. At last, we establish a linear model to fit the estimation results. Theoretical analysis and simulation results prove that this new method has high precision and better performance than WDM in high noise environments, and better suppression of interference and the edge effect. Compared with WDM, WVF can reduce the mean square error （MSE） by 50% when the signal to noise ration （SNR） is in the range of-15dB to -11dB. WVF is an effective and promising 1F estimation method.

Efficient Timing and Frequency Offset Estimation Scheme for OFDM Systems

A new training symbol weighted by pseudo-noise(PN) sequence is designed and an efficient timing and fre-quency offset estimation scheme for orthogonal frequency division multiplexing(OFDM) systems is proposed. The timing synchronization is accomplished by using the piecewise symmetric conjugate of the primitive training symbol and the good autocorrelation of PN weighted factor. The frequency synchronization is finished by utilizing the training symbol whose PN weighted factor is removed after the timing synchronization. Compared with conventional schemes, the pro-posed scheme can achieve a smaller mean square error and provide a wider frequency acquisition range.