Sonar Array Detection of Gaussian Signals in Gaussian Noise of Unknown Power

A statistical test is postulated for detecting, with an M-element hydrophone array, a Gaussian signal in spatially independent Gaussian noise of unknown power. The test is an extension of the uniformly-most-powerful (UMP) unbiased test for a two-element array. The output signal-to-noise ratio of the test is calculated and, for a large number of independent space-time samples, is shown to be no better than a mean-level detector (MLD). Receiver operating characteristic curves (ROC) for the MLD are computed and compared to the ROC curves for the optimum (Bayes) parametric detector. The input signal-to-noise power ratios required to provide a detection probability of 0.5 differ by less than 0.2 dB for a fifty-element array with wide variation in false-alarm probability and time-bandwidth product. This result suggests that both the extended bivariate UMP unbiased test and the MLD perform close to the unknown UMP unbiased test for independence of a multivariate Gaussian distribution.     

Adaptive Detection Probabilities for Fluctuating Target Models in Nonhomogeneous Gaussian Noise

Under the assumption that the average noise power may vary from cell to cell, new, more easily computed expressions are given for the probability of detecting a fluctuating target by means of a cell-averaging CFAR test. The generalized chi-square family of fluctuating targets is considered with the Swerling I and III models given as special cases.     

Maximum Likelihood DOA Estimation in Unknown Colored Noise Fields

Direction-of-arrival (DOA) estimation in unknown noise environments is an important but challenging problem. Several methods based on maximum likelihood (ML) criteria and parameterization of signals or noise covariances have been established. Generally, to obtain the exact ML (EML) solutions, the DOAs must be jointly estimated along with other noise or signal parameters by optimizing a complicated nonlinear function over a high-dimensional problem space. Although the computation complexity can be reduced via derivation of suboptimal approximate ML (AML) functions using large sample assumption or least square criteria, nevertheless the AML estimators still require multi-dimensional search and the accuracy is lost to some extent. A particle swarm optimization (PSO) based solution is proposed here to compute the EML functions and explore the potential superior performances. A key characteristic of PSO is that the algorithm itself is highly robust yet remarkably simple to implement, while processing similar capabilities as other evolutionary algorithms such as the genetic algorithm (GA). Simulation results confirm the advantage of paring PSO with EML, and the PSO-EML estimator is shown to significantly outperform AML-based techniques in various scenarios at less computational costs.     

Ideal Limiting of Periodic Signals in Random Noise

A method based on Hermite polynomials is developed for analysis of the output resulting from passing Fourier expandable signals and noise through general zero-memory devices. New results are developed for the output time and autocorrelation functions. Special results are generated for an ideal clipper. Techniques for obtaining results for practical cases are discussed. The method itself can be modified and applied to non-Gaussian noise and general nonlinear devices with nondeterministic signals.     

Stability of Parameter Estimates for a Gaussian Process

Maximum-likelihood estimates for the levels of the mean value function and the covariance function of a Gaussian random process are investigated. The stability of these estimates is examined as the actual covariance function of the process deviates from the form assumed in the estimators. It is found that the time-bandwidth product for stationary processes represents an upper bound on the number of estimator terms that can be safely used when estimating with uncertainty about the process covariance function. This result is consistent with other interpretations of the time-bandwidth product and tempers the conclusion that, in principle, an infinite number of estimator terms can be used to obtain a perfect estimate of the covariance level. In practice, the estimate of the level can never be perfect, and the accuracy of the estimate depends on the observation interval. Finally, conditions are established to ensure asymptotic stability of the estimates and physical interpretations are presented.     

Detection in Laplace Noise

The discrete time detection of a known constant signal in white stationary Laplace noise is considered. Exact expressions describing the performance of both the Neyman-Pearson optimal detector and the suboptimal linear detector are presented. Also, graphs of the receiver operating characteristics are given. The actual performance of the Neyman-Pearson optimal detector is compared to that predicted by a Gaussian approximation to the distribution of the test statistic.     

直升机舱内降噪技术研究

舱内噪声问题是影响直升机竞争力的一个重要因素。以某直升机作为研究对象,将飞行噪声数据作为输入,分别从噪声预计技术、噪声被动控制技术、噪声主动控制技术三个方面开展直升机降噪技术研究,并根据理论分析和试验结果,实施了直升机舱内主动、被动综合降噪设计,以较小的重量代价,取得了试验室环境6dBA综合降噪效果,达到了预期目标。     

Sensor Fault Detection in a System with Random Disturbances

Sensor faults are detected in an operating automatic system by a simplified version of the dedicated observer scheme. Control inputs are augmented by a random disturbance of moderate intensity. The dedicated observer in this case is a Kalman filter, driven by a single sensor. This filter provides estimates of the outputs from the other, nonredundant, sensors. A logical combination of these functionally redundant signals with the actual sensor signals provides prompt detection of incipient faults on all instruments without false alarms. The scheme is applied to a simulation of the lateral axis control system of a hydrofoil boat in which four sensors are to be covered by the fault detection scheme. Tests indicate that the scheme is robust with respect to variations in the intensity of the random disturbance.     

Output of a Balanced Frequency Discriminator Driven by a Sine Wave and Gaussian Noise

The output of a realizable balanced frequency discriminator is calculated for an input consisting of a sine wave plus Gaussian noise. Explicit autocorrelation and power spectra are found for one practical embodiment of the discriminator for various input carrier-to-noise ratios, with the carrier tuned to center frequency and also off-center. The formulas also permit the calculation of the output for the case of a spectrum of noise slowly swept through the discriminator. Although qualitatively similar to results previously obtained with an ideal discriminator, substantial differences are also found. Measurements are made that closely verified the theoretical results. No limiting is assumed.     

Detection in correlated Gaussian plus impulsive noise

A detector which is designed to operate in a correlated Gaussian-plus-impulsive-noise environment is presented. The detector whitens the data robustly and then uses a two-sided threshold test to determine the presence of impulsive samples. The impulsive samples are discarded, and the remaining samples are used to detect the presence or absence of a signal using a matched filter. An approximate analysis is presented, and simulations are used to demonstrate the effectiveness of this approach     

Detection of Known Signals in Nonstationary Noise

The basic design of a nonlinear, time-invariant filter is postulated for detecting signal pulses of known shape imbedded in nonstationary noise. The noise is a sample function of a Gaussian random process whose statistics are approximately constant during the length of a signal pulse. The parameters of the filter are optimized to maximize the output signal-to-noise ratio (SNR). The resulting nonlinear filter has the interesting property of approximating the performance of an adaptive filter in that it weights each frequency band of each input pulse by a factor that depends on the instantaneous noise power spectrum present at that time. The SNR at the output of the nonlinear filter is compared to that at the output of a matched filter. The relative performance of the nonlinear system is good when the signal pulses have large time-bandwidth products and the instantaneous noise power spectrum is colored in the signal pass band.     

含噪振动信号中早期碰摩的故障检测研究

 研究了在有噪环境下发动机转子系统早期碰摩故障的检测问题。利用最优参数搜索法改进了独立分量分析( ICA) 算法, 用于求解转子系统振动信号与噪声的盲分离问题。在此基础上, 对分离后的振动信号利用小波包分解进行早期碰摩信号的检测。结果显示最优搜索的ICA 算法运行效率高, 信号分离纯度好, 对振动信号有高效的降噪作用, 并利用小波包分解准确地检测出振动信号中的碰摩信息, 其效果优于小波分解法。信噪分离与小波包分解相结合有望用于工程实践中的早期碰摩故障检测和诊断。     

Error Probability for Fading CPSK Signals in Gaussian and Impulsive Atmospheric Noise Environments

Recently the performance of digital communication systems in the presence of impulsive atmospheric noise has been evaluated assuming a noise model which is in excellent agreement with experimental results. We evaluate the bit error probability for coherent phase-shift keying (CPSK) signaling assuming the same atmospheric noise model but considering a more reasonable representation of the communication channel which accounts for the simultaneous presence of Gaussian (always present) atmospheric noise and signal fading.     

Detection of Changes in the Characteristics of a Gauss-Markov Process

By an extension to the theory of sequential detection with dependent measurements, it is possible to develop a sequential probability ratio test (SPRT) to detect changes in regime in a Gauss-Markov process rather than detecting which of the two regimes exists. It is shown how a posterior form of this extended SPRT may be simplified to reduce computational complexity. The simplified SPRT's are in fact modifications of the original SPRT detecting the regime and not the change. The tests are applied to the problem of fault detection in a gyro navigational system; the results of a detailed computer simulation are given.     

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.     

噪声载荷作用下薄壁柱壳结构的随机振动响应的估算方法

给出了噪声载荷作用下薄壁柱壳结构随机振动加速度响应功率谱密度的计算公式和计算方法,并与实际测量获得的加速度响应功率谱密度进行了比较,计算的功率谱与实测的功率谱具有较好的一致性,说明这种估算噪声载荷作用下薄壁柱壳结构随机振动加速度响应功率谱密度的方法是合理可行的。用同样方法导出的Von Mises应力响应的功率谱密度及其均方值的计算公式,可直接用于疲劳强度分析。      

Spectral Location of Rayleigh and "m" Fading Signals in White Gaussian Noise

The classical problem is considered of locating a fading sinusoidal signal known to be present in one of several frequency " cells," each of which contains additive white Gaussian noise. The signal fading is assumed to follow the popular Rayleigh distribution, but generalizations tions to non-Rayleigh fading are included in terms of the " in" distribution due to Nakagami. The channel observation time is allowed to be either predetermined or variable (corresponding, respectively, to " fixed sample size" and " sequential" reception), and the practically important situation of intermittent signal transmissions is also examined. Results are in the form of optimal and near-optimal receiver structures, and of measures of performance.     

The Nonparametric Detection of Signals Embedded in Log-Normal Noise

The nonparametric detection of signals embedded in log-normal noise is discussed. The generalized sign (GS), Mann-Whitney (MW), modified Savage (MS), and modified rank squared (MRS) non-parametic detectors are considered and are compared with the mean and trimmed mean (TM) detectors when envelope detection is used. The detection of both nonfluctuating and Rayleigh fluctuating signals is considered.     

硅微静电悬浮陀螺微位移检测电路噪声分析

硅微静电悬浮惯性仪表主要面向空间应用,如卫星无拖曳控制,星载重力测量等,具有潜在分辨率高、功耗低、体积小、多轴集成等特点。仪表的高分辨率实现对检测电路的噪声提出了较高要求。本文以硅微静电悬浮陀螺仪单频调幅电容式微位移检测电路为例,从电路外部干扰噪声耦合机理及抑制、电路内部噪声分析及优化两个方面,给出了电源耦合噪声、电路温度特性以及放大器电路噪声分析优化等方面的理论推导、仿真及实验验证。