基于局域波分解的机动目标ISAR成像

在飞机一类惰性较大的目标作机动飞行时,在ISAR成像的短时间内,同一距离单元的回波可近似看成多分量线性调频信号的叠加,且调频斜率各不相同. 因此,在ISAR成像的横向压缩处理中,先利用局域波分解方法将每个距离单元的回波分解为若干个线性调频信号,然后再进行Wigner-Ville变换(WVT),获取目标的瞬时多普勒谱,进而实现目标的二维成像. 该处理方法不仅避免了WVT交叉干扰项的产生,而且在保持WVT的高时频分辨能力的同时减少了计算量,仿真结果证明了该方法的有效性.     

Detection of replay attacks in cyber-physical systems using a frequency-based signature

This paper proposes a frequency-based approach for the detection of replay attacks affecting cyber-physical systems (CPS). In particular, the method employs a sinusoidal signal with a time-varying frequency (authentication signal) into the closed-loop system and checks whether the time profile of the frequency components in the output signal are compatible with the authentication signal or not. In order to carry out this target, the couplings between inputs and outputs are eliminated using a dynamic decoupling technique based on vector fitting. In this way, a signature introduced on a specific input channel will affect only the output that is selected to be associated with that input, which is a property that can be exploited to determine which channels are being affected. A bank of band-pass filters is used to generate signals whose energies can be compared to reconstruct an estimation of the time-varying frequency profile. By matching the known frequency profile with its estimation, the detector can provide the information about whether a replay attack is being carried out or not. The design of the signal generator and the detector are thoroughly discussed, and an example based on a quadruple-tank process is used to show the application and effectiveness of the proposed method.     

Blind estimation of carrier frequency offset for OFDM systems with time-varying DC offset

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in radar and communication systems. High sensitivity to carrier frequency offset (CFO) is one of the major drawbacks of OFDM. CFO estimation for OFDM systems had been extensively studied and various algorithms had been proposed. However, the established algorithms may be compromised by the adoption of direct-conversion architecture and multi-mode low noise amplifier in the OFDM receiver, which introduces time-varying direct current offset (TV-DCO) into the system. In our previous study, we developed an eigen-decomposition based estimation algorithm, which is robust to TV-DCO but suffers from performance degradation under low to medium signal-to-noise ratio and requires high computation efforts. To address those issues, we in this paper propose a novel blind CFO estimation algorithm. By making use of the second order differential filtering and subspace method, the proposed algorithm achieves great performance improvement with reduced complexity. The performance of the proposed algorithm is demonstrated by simulations.     

基于LMS算法的自适应干扰消除

介绍了自适应干扰消除的原理和基于LMS的自适应滤波算法,并分析了干扰抵消的频域块LMS算法,给出了算法的数学推导与模型。在宽带干扰,单音余弦干扰和线性调频干扰的条件下,利用Matlab软件进行了算法性能仿真,结果表明LMS算法可以有效地消除上述三种干扰,提高信号传输的质量。     

子空间法在动态源盲信号处理中的应用

通常的盲源分离算法都不具备对未知信号源个数估计的能力,而很多场合,源信号的个数是未知的甚至可能是动态变化的。子空间法的应用实现了源信号数目未知和动态变化的超定盲信号分离中源信号数目的在线跟踪估计。子空间法是将观测到的传感器信号划分为信号子空间和噪声子空间,这样做不仅能很大限度的降低噪声水平,还可以根据特征值大小来估计源信号数目。利用赤迟信息准则(AIC准则)和最小描述长度(MDL信息论准则)实现了动态源问题中阈值的在线调整。     

基于四阶累积量的CSPRIT算法

分析了基于二阶统计的CSPRIT算法在空间相关高斯噪声环境中存在的问题，提出将四阶累积量与CSPRIT算法相结合，处理一维二元相移键控信号（BPSK）和多元幅移键控信号（MASK），实现信号到达角（DOA）的估计和波束形成器的构造。与基于二阶统计的CSPRIT算法相比，基于四阶累积量的改进算法能够有效抑制空间相关的高斯噪声，提高信号估计精度。计算机仿真验证了该算法的有效性。     

MTN optimal control of MIMO non-affine nonlinear time-varying discrete systems for tracking only by output feedback

In practice, many controlled plants are equipped with MIMO non-affine nonlinear systems. The existing methods for tracking control of time-varying nonlinear systems mostly target the systems with special structures or focus only on the control based on neural networks which are unsuitable for real-time control due to their computation complexity. It is thus necessary to find a new approach to real-time tracking control of time-varying nonlinear systems. In this paper, a control scheme based on multi-dimensional Taylor network (MTN) is proposed to achieve the real-time output feedback tracking control of multi-input multi-output (MIMO) non-affine nonlinear time-varying discrete systems relative to the given reference signals with online training. A set of ideal output signals are selected by the given reference signals, the optimal control laws of the system relative to the selected ideal output signals are set by the minimum principle, and the corresponding optimal outputs are taken as the desired output signals. Then, the MTN controller (MTNC) is generated automatically to fit the optimal control laws, and the conjugate gradient (CG) method is employed to train the network parameters offline to obtain the initial parameters of MTNC for online learning. Addressing the time-varying characteristics of the system, the back-propagation (BP) algorithm is implemented to adjust the weight parameters of MTNC for its desired real-time output tracking control by the given reference signals, and the sufficient condition for the stability of the system is identified. Simulation results show that the proposed control scheme is effective and the actual output of the system tracks the given reference signals satisfactorily.     

Time-frequency channel modeling and estimation of multi-carrier spread spectrum communication systems

In wireless communications, the channel is typically modeled as a random, linear, time-varying system that spreads the transmitted signal in both time and frequency due to multi-path propagation and Doppler effects. Estimated channel parameters allow system designers to develop coherent receivers that increase the system performance. In this paper, we show how time-frequency analysis can be used to model and estimate the time-varying channel of a multi-carrier spread spectrum (MCSS) system using a complex quadratic sequence as the spreading code. We will show that for this spreading code, the effects of time delays and Doppler frequency shifts, caused by the mobility of environment objects, can be combined and represented effectively as time shifts. The discrete evolutionary transform (DET), as a time-frequency analysis method, enables us to estimate the effective time shifts via a spreading function and to use them to equalize the channel. Using the effective time shifts, the time-varying channel can be represented simply as linear-time invariant system by embedding the Doppler shifts that characterize the time-varying channel into effective time shifts. The channel parameters are used to estimate the data bit sent. To illustrate the performance of the proposed method we perform several simulations with different levels of channel noise, jammer interference, and Doppler frequency shifts.     

一种基于非线性特征提取的被动声纳目标识别方法研究

目标噪声特征提取是被动声纳目标识别系统的关键技术。首先提出了一种利用从噪声极限环中提取的非线性特征来分析舰船噪声信号的新方法,然后采用基于自适应遗传BP算法的神经网络对提取的特征进行分类。实验结果表明,该系统具有较好的分类效果。     

基于快速傅立叶变换的信号频谱分析仿真

信号序列的DFT本身就是信号频谱的采样集,所以DFT可直接用于分析信号的频谱。快速傅立叶变换是一种有效、实用的信号DFT算法。文章通过对DIT-FFT算法的详细分析,提出了一种计算机软件仿真信号频谱分析方案,并给出了关键算法的C语言程序。     

微弱信号检测技术的研究

微弱信号检测就是利用近代电子学和信号处理方法从噪声中提取有用信号,其关键在于抑制噪声,恢复、增加和提取有用信号.本文将从信号处理系统信噪比的改善来简单地论述微弱信号检测的原理,重点介绍了用相关检测法和取样积分法检测微弱信号的原理、方法和应用.     

Discrete time robust detection of stochastic signals in non-Gaussian contaminated noise

For many applications in signal detection, imprecise knowledge of the underlying noise process often makes desirable the employment of a robust detector. In this paper we consider the discrete time detection of stochastic signals in white noise, where the univariate noise density is known perfectly only on an interval about the origin. We present a method to enhance the asymptotic performance of the detector by exploiting this knowledge, and at the same time preserve robustness properties of the detector to the remaining inexact knowledge of the univariate noise density via a saddlepoint condition. We then provide examples to show that improved performance is indeed obtained.     

Conjugate gradient-based FLANN algorithms in nonlinear active noise control

The conjugate gradient (CG) method exhibits fast convergence speed than the steepest descent, which has received considerable attention. In this work, we propose two CG-based methods for nonlinear active noise control (NLANC). The proposed filtered-s Bessel CG (FsBCG)-I algorithm implements the functional link artificial neural network (FLANN) as a controller, and it is derived from the Matérn kernel to achieve enhanced performance in various environments. On the basis of the FsBCG-I algorithm, we further develop the FsBCG-II algorithm, which utilizes the Bessel function of the first kind to constrain outliers. As an alternative, the FsBCG-II algorithm has reduced computational complexity and similar performance as compared to the FsBCG-I algorithm. Moreover, the convergence property of the algorithms is analyzed. The proposed algorithms are compared with some highly cited previous works. Extensive simulation results demonstrate that the proposed algorithms can achieve robust performance when the noise source is impulsive, Gaussian, logistic, and time-varying.     

基于乘性和加性噪声中指数信号的频率最小二乘估计

Estimating the parameter of signals is a very important problem in Statistical Signal Processing. In this paper ,we obtain the least squared estimator of frequency of an exponential signal in presence of both multiplicative and additive noise.     

基于乘性和加性噪声中指数信号的频率最小二乘估计

Estimating the parameter of signals is a very important problem in Statistical Signal Processing. In this paper ,we obtain the least squared estimator of frequency of an exponential signal in presence of both multiplicative and additive noise.     

An intelligent propagation distance estimation algorithm based on fundamental frequency energy distribution for periodic vibration localization

Earth surface vibrations generated by passing vehicles, excavation equipment, footsteps, etc., attract increasing attentions in the research community due to their wide applications. In this paper, we investigate the periodic vibration source localization problem, which has recently shown significance in excavation device detection and localization for urban underground pipeline network protection. An intelligent propagation distance estimation algorithm based on a novel fundamental frequency energy distribution (FBED) feature is developed for periodic vibration signal localization. Contributions of the paper lie in three aspects: 1) a novel frequency band energy distribution (FBED) feature is developed to characterize the property of vibrations at different propagation distances; 2) an intelligent propagation distance estimation model built on the FBED feature with machine learning algorithms is proposed, where for comparisons, the support vector machine (SVM) for regression and regularized extreme learning machine (RELM) are used; 3) a localization algorithm based on the distance-of-arrival (DisOA) estimation using three piezoelectric transducer sensors is given for source position estimation. To testify the effectiveness of the proposed algorithms, case studies on real collected periodic vibration signals generated by two electric hammers with different fundamental frequencies are presented in the paper. The transmission medium is the cement road and experiments on vibration signals recorded at different propagation distances are conducted.     

An iterative state-space identification method with data correlation for MIMO systems with measurement noise

This work presents an iterative concept of the State-space Realization Algorithm with Data Correlation (SSRA-DC) to identify MIMO systems with measurement noise and subjected to a reduced number of samples acquired from the process. The measurement noise is characterized as a random signal with properties of white noise and having up to 1% of the output signal amplitude. The proposed technique is based on the Markov parameters matrix’s feedback in an iterative algorithm supported by the SSRA-DC method. A gain factor takes part in the closed-loop to update the Markov parameters matrix, reducing their residues at each iteration. A fixed value for the gain is applied all over the iterations. The Gaussian White Noise (GWN) is employed as the input excitation signal in simulated experiments of mass-damper-springer models with 50 and 100 degrees of freedom. For some algorithm settings, one hundred simulations, each holding more than 100 iterations, are performed to statistically demonstrate the iterative algorithm’s effectiveness compared to the conventional SSRA-DC. Further comparative analysis is accomplished between the iterative method with the ARMAX and N4SID algorithms.     

An improved pipeline leak detection and localization method based on compressed sensing and event-triggered particle filter

This paper proposes an improved model based pipeline leak detection and localization method based on compressed sensing (CS) and event-triggered (ET) particle filter (ET-PF). First, the state space model of the pipeline system is established based on the characteristic line method. Then, the CS method is used to preprocess the sensor signals to recover the potentially lost leak information which is caused by the low sampling frequency of the industrial pipeline sensors, and an event based beetle antennae search (BAS) particle filter (BAS-PF) is proposed to improve the accuracy and efficiency of the pipeline state estimation. Finally, a pipeline leak detection and localization method is developed based on the proposed signal processing, and state estimation algorithms, as well as a pipeline partition strategy. Experiment results show that the proposed method can accurately detect and locate the leak of the pipeline system with a localization error of about 1.4%.     

Bias-compensated affine-projection-like algorithm based on maximum correntropy criterion for robust filtering

This article proposes an affine-projection-like maximum correntropy (APLMC) algorithm for robust adaptive filtering. The proposed APLMC algorithm is derived by using the objective function based on the maximum correntropy criterion (MCC), which can availably suppress the bad effects of impulsive noise on filter weight updates. But the overall performance of the APLMC algorithm may be decreased when the input signal is polluted by noise. To compensate for the deviation of the APLMC algorithm in the input noise interference environment, the bias compensation (BC) method is introduced. Therefore, the bias-compensated APLMC (BC-APLMC) algorithm is presented. Besides, the convergence of the BC-APLMC algorithm in the mean and the mean square sense is studied, which provides a constraint range for the step-size. Computer simulation results show that the APLMC, and BC-APLMC algorithms are valid in acoustic echo cancellation and system identification applications. It also shows that the proposed algorithms are robust in the presence of input noise and impulse noise.     

An on-line orthogonal wavelet denoising algorithm for high-resolution surface scans

This paper deals with noise detection and threshold free on-line denoising procedure for discrete scanning probe microscopy (SPM) surface images using wavelets. In this sense, the proposed denoising procedure works without thresholds for the localisation of noise, as well for the stop criterium of the algorithm. In particular, a proposition which states a constructive structural property of the wavelets tree with respect to a defined seminorm has been proven for a special technical case. Using orthogonal wavelets, it is possible to obtain an efficient localisation of noise and as a consequence a denoising of the measured signal. An on-line denoising algorithm, which is based upon the discrete wavelet transform (DWT), is proposed to detect unavoidable measured noise in the acquired data. With the help of a seminorm the noise of a signal is defined as an incoherent part of a measured signal and it is possible to rearrange the wavelet basis which can illuminate the differences between its coherent and incoherent part. In effect, the procedure looks for the subspaces consisting of wavelet packets characterised either by small or opposing components in the wavelet domain. Taking real measurements the effectiveness of the proposed denoising algorithm is validated and compared with Gaussian FIR- and Median filter. The proposed method was built using the free wavelet toolboxes from the WaveLab 850 library of the Stanford University (USA).