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.     

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.     

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.     

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.     

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.     

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.     

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.     

Comments on "Optimum Bandwidth of a Low-Pass Filter for Detection of a Pulse in Nonstationary Noise"

In a recent correspondence1 a calculation of the optimum bandwidth of a low-pass RC filter for the detection of a pulse signal in nonstationary noise was presented. The purpose of this correspondence is: 1) to point out additional references to the work which has been conducted in the stationary noise case, and 2) to present an interesting alternate derivation of the expected output noise power for the nonstationary noise case.     

On Transient and Periodic Signal Detection in Time-Varying Noise Power

Detectability of periodic and synchronously recurrent transient signals in a noisy environment in which the noise power is time varying is investigated. For at least one noise model, it is shown that the basic nonlinearity of the optimum detector is a limiter. Performance of this optimum detector is compared with analog cross-correlation and clipper cross-correlation (CCC) detectors. It is shown that the CCC performs nearly optimally, especially at low signal-to-noise ratios, and that its performance is significantly better than that of the analog cross correlator.     

噪声检测自适应窗口滤波算法

脉冲噪声的存在,从视觉上影响图像的质量。为了去除图像中的脉冲噪声,提出了利用相似邻居数对图像中的噪声像素进行标记并建立相应矩阵,接着根据噪声的污染程度自适应的选择滤波窗口,最后对噪声像素进行自适应均值滤波去除图像中的脉冲噪声。实验结果表明,算法能有效降低图像中的脉冲噪声,对图像的边缘与细节保持较好。     

限带高斯白噪声声压时程模拟的修正三角级数法

针对传统三角级数法模拟高声压级下的高斯白噪声声压有效值与理论值存在误差的问题,提出一种修正三角级数法,预先将各谐波振幅置为1,最后调整声压有效值为理论值,模拟过程简单,消除了传统方法模拟有效值与理论值之间的误差。数值模拟得到的声压时程数据准确可靠,具有良好的随机性,表明方法用于限带高斯白噪声声压时程模拟具有合理性、可行性。     

On the Limitations of Random Sensor Placement for Distributed Signal Detection

We consider the design of a sensor network for detecting an emitter which if present is known to be located in an interval but whose exact position is unknown. We seek to minimize the total system power consumption subject to detection performance constrains by carefully choosing the thresholds and positions of the sensors. Toward this goal, we propose an iterative algorithm for the optimization problem. Numerical results are given to provide insights into the design of such networks. We show that random sensor placement can perform poorly, in contrast to what many currently believe.     

Maximum Likelihood Energy Detection of Mary Orthogonal Signals

The literature on energy detection is extended by applying it to the processing of M'ary orthogonal communication signals of arbitrary time-bandwidth product. Guassian noise channel transition probabilities are derived for maximum likelihood energy detection, modified to the extent of including an erasure threshold. Relations and computational techniques are described for determining the symbol erasure and error probabilities for general signal alphabet sizes (M) and time-bandwidth products. When time of arrival is known exactly and Doppler is negligible, gible, it is determined that energy detection is inferior to noncoherent matched filter detection. For time-bandwidth products of the order of 100 and error probabilities around 10 3, a loss of about 5 dB occurs which is attributable to a lack of knowledge of the detail signal structure. However, for problems where time of arrival and/ or Doppler are unknown, energy detection will perform nearly as well as matched filter detection of, for example, spread spectrum signals, and is also simpler to implement. General energy detection performance curves are given in terms of required signal energy for specific error and erasure probabilities, as a function of M'ary and time-bandwidth product.     

The Effect of the Common Process Noise on the Two-Sensor Fused-Track Covariance

This note deals with the effect of the common process noise on the fusion (combination) of the state estimates of a target based on measurements obtained by two different sensors. This problem arises in a multisensor environment where each sensor has its information processing (tracking) subsystem. In the case of an ?-? tracking filter the effect of the process noise is that, over a wide range of its variance, the uncertainty area corresponding to the fused estimates is about 70 percent of the single-sensor uncertainty area as opposed to 50 percent obtained if the dependence is ignored.     

Common-Mode Noise Generation in a DC-to-DC Converter

Analytical and experimental considerations of the common-mode noise in a dc-to-dc connverterare discussed. Analyzing the mechanism of the common-mode noise generation by means of a high-frequency equivalent circuit, we estimate the output noise voltage generated during the transistor's turn-on time. This commonmode noise consists mainly of two frequency components distributed in several megahertz. The effects of parasitic parameters on the noise voltage and frequencies are clarified. In addition, the effect of a common-mode choke on the noise suppression is discussed experimentally.