An Adaptive Detection Algorithm

A general problem of signal detection in a background of unknown Gaussian noise is addressed, using the techniques of statistical hypothesis testing. Signal presence is sought in one data vector, and another independent set of signal-free data vectors is available which share the unknown covariance matrix of the noise in the former vector. A likelihood ratio decision rule is derived and its performance evaluated in both the noise-only and signal-plus-noise cases.     

Dynamics of the Phase-Locked Loop without a Limiter

The general (nth order) phase-locked loop is analyzed, of which the amplitude is not constant. The input carrier signal is amplitude-modulated by wide-band stationary Gaussian noise, and the signal, superposed with the additive white stationary Gaussian noise, enters the nonlimited phase-locked loop. Under the above assumptions the loop can be shown to constitute an n-dimensional vector Markov process, so that the process satisfies the n-dimensional Fokker-Plank equation. The probability density function depends on the effective loop signal-to-noise ratio and the effective modulation power.     

Robust detection of small stochastic signals

We consider the problem of detecting a stochastic signal in white not-necessarily-Gaussian noise, using vector valued observations. The locally optimal detector is presented and its performance evaluated. The least-favorable signal spectrum and noise density (over specified classes) are found, and it is shown that the detector using these least-favorable assumptions is minimax robust. The class of spectra is that of any stochastic signal of specified power whose spectrum can be bounded from above and from below by two given positive functions. The class of densities is the ε-contamination model. We present examples of the performance achievable with the robust detector in one of these the spectral uncertainty class corresponds to the unknown Doppler shift of a radar return signal. It is demonstrated that the standard matched-filter's performance degradation with increasing Doppler shift can be avoided almost entirely through use of the robust processor     

一种基于极大似然估计的信号盲抽取算法

针对盲信号抽取问题,根据极大似然估计原理,给出一种基于极大似然估计的分步盲抽取算法。根据信号不同的延时特性确立抽取信号,完成第一步抽取。由于初步抽取信号存在噪声污染,根据极大似然估计,估计抽取信号的概率密度函数,确立优化代价函数,利用自然梯度方法进行优化,确立最终抽取向量迭代方式,完成对抽取信号优化处理。通过仿真证明算法具有良好的收敛性和抗噪性,在SNR17 d B时,抽取信号与源信号的相似度达到95%。     

Error Analysis of the Optimal Antenna Array Processors

The optimal weights of an antenna array processor, which maximizes the output signal-to-noise ratio (SNR) in the absence of errors, are computed using the noise-alone matrix inverse (NAMI) and the steering vector in the look direction or the signal-plus-noise matrix inverse (SPNMI) and the steering vector. In practice the estimated steering vector as well as the estimated optimal weights are corrupted by random errors. This paper has analyzed the effects of these errors on the performance of the NAMI processor and the SPNMI processor by deriving analytic expressions for the output signal power, output noise power, output SNR, and the array gain as a function of the error variance. The treatment is for a general array configuration and no assumption about a particular array geometry is made.     

含噪声的转子碰摩混沌信号分类识别

采用基于竞争学习和聚类分析的学习向量量化 ( LVQ)方法 ,研究转子碰摩混沌响应信号的神经网络分类识别问题 ,给出了相应的理论分析和计算结果 ,着重研究了 LVQ网络在不同噪声时的识别情况。分析结果表明 ,该方法可以实现转子碰摩混沌信号与其它响应信号的分类识别 ,并且具有良好的抗噪性能 ,为转子碰摩混沌信号的分类识别提供了一种较为直接的实时处理方法。     

Method for denoising and reconstructing radar HRRP using modified sparse auto-encoder

A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environment are contaminated by strong noise.Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition.In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-lin...     

Some Aspects of Swerling Models for Fluctuating Radar Cross Section

The structure of Swerling's 1957 model for fluctuating radar cross section and signal amplitude is examined prinwrily in terms of mixture representations. The authors also show the relationship of Nakagami's m distrubution to Swerling's amplitude distribution, and Nakagami's q distribution (Hoyt vector density) is reduced to the Swerling distribution. Nonuniform phase distributions associated with Swerling's functions are also derived. Results are given when Swerling's amplitude density is used as a prior distribution with Bennett's (Rice's) distribution for the amplitude of a sine wave plus narrowband Gaussian noise.     

Analysis of a Signal Processor for an Antenna Array

A signal processor that provides ratio-squared predetection combining, has been investigated for application in an adaptive antenna array. The analysis and experimental data presented here pertain to the signal processing gain when the antenna array is illuminated by a coherent signal source and a partially coherent noise source. For a noise source which is coherent, the processing gain depends on relative strength of the signal and noise, relative directions of arrival, and the usual "array factor." The array exhibits capturing effects much as in an FM receiver. The effective antenna pattern is a superposition of two beams of different magnitudes, one directed to the signal source and the other to the noise source. When the noise is partially coherent, the behavior of the signal processor is quite complex. Analytical prediction and experimental simulation measurement on a four-channel system indicate that the partially coherent noise may be regarded as the source of an incoherent noise component plus a coherent noise component with the magnitude of the latter determined by the coherence coefficient for the noise source.     

The Effect of Hard Limiting an Angle-Modulated Signal Plus Noise

The effect of hard limiting an angle-modulated signal plus narrow-band Gaussian noise is analyzed. Several examples are considered?sinusoidal angle modulation, Gaussian angle modulation, and biphase angle modulation. The general conclusion is that when a zonal band-pass filter is used, which rejects dc and second harmonics, an angle-modulated signal plus Gaussian noise provides the same output signal-to-noise ratio as shown by Davenport for a CW signal plus Gaussian noise. However, when a narrow bandpass filter is used, which has a bandwidth approximately equal to the input angle-modulated signal, an angle-modulated signal plus Gaussian noise has a better output signal-to-noise ratio than a CW signal plus Gaussian noise.     

Now You See It, Now You Don't

The signal enhancement properties of a single-loop delayline integrator (DLI) are derived for operation in the presence of correlated noise. The signal is assumed periodic with a period equal to the delay of the DLI and present only for a finite time interval. The noise is assumed additive, zero mean, statistically independent from the signal, and present for an infinite time. The noise also is assumed covariance stationary with a specified auto correlation function. Enhancement is defined as the ratio of the increase in average signal power to the increase in average noise power due to integration. The results are general in that the enhancement is derived in terms of the autocorrelation coefficients of the input noise. Plots of enhancement versus number of signal observations and feedback gain are shown for uncorrelated and exponentially correlated noise. The plots show graphically the optimum choices of feedback gain and number of signal observations for a specified auto correlation function and signal observation time. When strongly correlated noise, characteristic of 10-GHz backscatter from salt flats at low incidence angles, rather than uncorrelated noise is assumed present at the input of the DLI, an example, using DLI parameters typical of modern airborne radars, shows a degradation in the maximum achievable enhancement of 9 dB.     

A Fast Beamforming Algorithm for Large Arrays

This beamforming algorithm is written specifically for array radars in which the number of array elements K is very large compared with the number of jammers L the radar is designed to suppress. It uses a set of M noise vectors to construct a basis for the jammer component of the antenna output vectors. The component of the quiescent weight vector orthogonal to each basis vector is calculated, renormalized to unit length, and identified as the adapted weight vector. This algorithm is effective in the suppression of many types of jammers. The number of noise samples M required in the construction of the adapted weight vector is approximately equal to L. In the special case of L narrowband noise jammers, for example, a choice of M = L usually reduces the receiver output jammer power to a few dBs above the white noise background. It is permissible to have M相似文献   

噪声调制连续波雷达信号波形射频隐身特性

雷达信号波形的射频(RF)隐身性能是雷达系统能否适应现代战场环境的重要因素,雷达射频隐身信号波形设计是现代雷达系统设计中的重要课题.首先,在介绍随机噪声信号雷达原理的基础上,基于Schleher截获因子阐述了噪声调制连续波雷达信号波形的射频隐身特性.然后,分析了高斯噪声相位和频率调制连续波雷达输出自相关函数和高斯噪声相...     

Noise factor and antenna gains in the signal/noise equation for over-the-horizon radar

A recommended form of the signal-to-noise equation that includes both internal and external system noise and signal/noise processing losses is discussed. The recommended form conforms to the internationally accepted definition of system operating noise factor but is extended to include signal/noise processing. The predetection signal-to-noise ratio (SNR) of a radar or communication system is proportional to the power gain of the transmit antenna and the directive gain of the receive antenna, and is inversely proportional to the operating noise factor of the receiving system. The operating noise factor is approximately equal to the product of the external noise factor and the signal/noise processing factor when the system is external noise limited, as is usually the case for over-the-horizon (OTH) radar.<>     

面向不均匀飞机舱音信号样本的模糊支持向量机识别方法

 针对驾驶舱话音记录器(CVR)中记录的舱音背景信息多而复杂、频率范围宽、非平稳等特点,通过对15种舱音信息进行傅里叶变换和小波包变换,依次提取其Mel倒谱系数(MFCC)和小波包分解系数(WPC),利用距离可分性判据对MFCC和WPC信息进行压缩融合,得到舱音信息特征向量。设计了面向不均衡样本的模糊支持向量机(FSVM),分别计算每种类别样本及其内每种舱音信息的2个隶属度,然后利用FSVM对舱音信号进行分类识别,解决了CVR信号含噪奇异样本和数目不均衡样本时识别性能较差的缺点,实验表明该方法明显优于常规支持向量机(SVM)和FSVM,分类识别率达到98.33%。     

压气机内部噪声特征与转子叶片声固耦合机理分析

航空发动机压气机转子叶片故障多由机械激励和气动激励造成,而高强声波对转子叶片的激振因素不容忽视。通过开展某型涡扇发动机压气机内部噪声测试试验,研究压气机转子叶片振动机理及其与噪声信号的对应关系。阐述了压气机内部旋转不稳定性非定常压力波作用机制,提出了基于刚性壁声波导管技术的导出式噪声测量方法,完成了某型涡扇发动机压气机内部噪声信号测试,对噪声信号进行了频谱分析和声传播特性分析。研究结果表明,某型涡扇发动机压气机内部噪声信号频谱呈现高峰值纯音分量1 402 Hz,并且该纯音分量与转子叶片通过频率呈现特定的频率组合关系。该纯音分量的噪声源在压气机内部沿发动机顺航向方向从后向前传播。利用旋转不稳定性理论,将声源频率在不同坐标系下进行转换,当噪声源周向模态数为13时,该纯音分量可调制出与高压一级转子叶片一阶振动频率相对应的激振频率。     

Some Matched Filter Configurations for Infrared Systems

An analysis of the output of three alternative matched filter configurations in an infrared scanning system model is presented. The sensor is corrupted by thermal noise, generation-recombination noise, photon noise, and modulation noise, the latter providing an extreme discoloration in the signal passband. Expressions for the signal voltage density spectrum, signal pulse shape, noise power spectrum, and average noise power at the matched filter output are derived where the integral evaluations attendant to these derivations do not appear elsewhere in the literature. The paper also provides graphical displays of the signal-to-noise power ratio at the filter output versus various system parameters, noise power spectrum out of the matched filter versus ?, and the signal pulse shape out of the filter versus time. Also included are discussions of practically realizable approximations to the matched filters and curve fitting techniques for the signal pulse shape function.     

Background noise suppression for signal enhancement by noveltyfiltering

The enhancement of weak signals in the presence of background and channel noise is necessary to design a robust automatic signal detection and recognition system. The autoassociative property of neural networks can be used to map the identifying characteristics of input source waveforms or their spectra. This paper is directed at the exploitation of such neural network properties for novelty filtering that improves the detection probability of weak signals by learning and subsequent subtraction of noise background from the input waveform. A neural-network-based preprocessor that learns to selectively filter out the background noise without significantly affecting the signal will be highly useful in solving practical signal enhancement problems. An analytical basis is established for the operation of neural-network-based novelty filters that enhance the signal detectability in the presence of noise background and channel noise     

An Adaptive Threshold System for Nonstationary Noise Backgrounds

The problem of detecting target signals in an ocean environment using active sonar is complicated by the nonstationary background which usually consists of both ambient ocean noise and reverberation. on. In this paper a signal processing system capable of detecting a signal in nonstationary noise is introduced. This system makes use of the mean and variance time functions of the nonstationary noise background in order to design estimation filters which will cope with the nonstationarity. Appropriate statistics of the noise and signal (tone burst) plus noise have been obtained and are used to determine the probabilities of false alarm and detection and the receiver operating characteristics.     

Weak Signal Detection in Non-Gaussian Noise of Unknown Level

An adaptive threshold detector to test for the presence of a weak signal in additive non-Gaussian noise of unknown level is discussed. The detector consists of a locally optimum detector, a noise level estimator, and a decision device. The detection threshold is made adaptive according to the information provided by the noise level estimator in order to keep a fixed false-alarm probability. Asymptotic performance characteristics are obtained indicating relationships among the basic system parameters such as the reference noise sample size and the underlying noise statistics. It is shown that, as the reference noise sample size is made sufficiently large, the adaptive threshold detector attains the performance of a corresponding locally optimum detector for detecting the weak signal were the noise level known.