时变微弱扩频信号检测

提出了一种解决微弱高动态扩频信号捕获的新方法,利用分数阶域滤波设计了伪码—时频联合匹配滤波的框架,最后进行了相关计算并给出了应用实例。     

On Signal and Noise Level Estimation in a Coherent PCM Channel

Joint maximum likelihood estimators are presented for the signal amplitude and noise power density in a coherent PCM channel with white Gaussian noise and a correlation receiver. The estimates are based upon the correlation coefficient outputs of the receiver. From these estimators, an estimator for the quantity (received signal energy)/bit/,(noise power)/(unit bandwidth) upon which the error probabilities depend, is derived. This estimator is shown to be useful as 1) a point estimator for the signal-to-noise ratio for the higher values of this ratio (about 4 dB or greater), and 2) an easily calculated statistic upon which to base data acceptance or rejection criteria. The acceptance or rejection levels are obtained by the use of confidence interval curves in conjunction with word error probability data.     

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.     

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.     

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.     

自适应神经元在信号检测中的应用

本文对自适应神经元构成的自适应应网进行了分析,对该网络在信号检测中的应用进行了讨论产给出计算机仿真结果。     

Bandpass Signal Sampling and Coherent Detection

Coherent detectors in radar and communications receivers are generally implemented in the form of two parallel baseband channels which form in-phase (I) and quadrature (Q) components of a received RF/IF signal. Phase errors of several degrees due to imperfect matching of these separate channels limit the performance achievable from signal processors such as moving target indicators (MTI), coherent integrators, Doppler filters, antenna array processors, and coherent sidelobe cancellers. Thus methods in which a single analog to digital (A/D) converter samples and digitizes the IF signal directly, eliminating the need for IF to baseband conversion, have been of recent interest and are the subject of this paper. To obtain accurate coherent detection from IF samples taken near the Nyquist rate requires interpolation based upon a number of stored samples. An algorithm derived from sampling theory is defined and used to demonstrate accurate reconstruction of the original IF signal from digitized samples. In-phase and quadrature components of the signal are shown to be available from processed samples with demonstrated phase errors less than 0.2°.     

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.     

HOPFIELD网络在信号检测和识别中的应用

本文综述了ＨＯＰＦＩＥＬＤ的结构、算法及在信号检测识别的应用,并对离散型ＨＯＰＦＩＥＬＤ网络进行了计算机仿真。     

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.     

飞机飞行振动信号中周期分量的识别

准确地掌握飞机飞行振动信号中的周期分量有十分重要的意义。本文采用自相关函数和现代谱分析中的最大熵法 (MEM模型法 )相结合的方法对×型飞机飞行振动信号进行了分析 ,通过运用MATLAB语言进行计算 ,得出了×型飞机飞行振动环境中的周期分量。     

Radar Signal Propagation and Detection Through Ice

In this paper we describe the existing and planned radar measurements of the planetary bodies. The dielectric properties of water ice and other potential surface and subsurface materials are discussed, as well as their dependency on temperature and structure. We then evaluate the performance of subsurface sounding radars using these parameters. Finally we describe some laboratory technique to help interpret the radar data, presenting some results obtained using dielectric spectroscopy methods.     

基于PD处理技术的雷达弱信号检测方法

针对脉冲跟踪测量雷达普遍存在的远距离弱信号检测能力不足的问题,基于PD（PulseDoppler,脉冲多普勒）雷达的杂波抑制与信号处理技术,提出脉冲雷达中的弱信号检测方法;详细分析了在脉冲跟踪测量雷达中应用PD处理技术的可行性,得出在一定约束条件下,可将PD处理技术运用于脉冲跟踪测量雷达,并提出了具体实现方法;最后,通过数据仿真和脉冲雷达实际测量数据进行验证,结果表明该方法能有效提高信噪比。     

Bayesian Quickest Signal Detection in a Discrete-Time Observation

This paper deals with the problem of quickest detection of a signal in discrete-time observations where the noise is not necessarily additive. By introducing a new cost function, penalizing the decision delay, in addition to penalizing wrong decisions as in the classical case, a global risk function is derived for use in a Bayesian framework. The minimization of the average risk leads to the optimum Bayesian decision regions, giving the structure of the optimum receiver. Some simplifications for elementary costs and some applications are investigated. The optimum receiver is shown to be a parallel bank of classical optimum filters, each one matched to a particular delay of the signal to be detected. Our approach is shown to apply to the detection of certain changes in a stochastic process.     

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.     

BP网络在信号检测中的应用

本文对ＢＰ网络在信号的检测和处理方面的性能进行了分析,对该网络在信号检测中的应用进行了讨论并给出了计算机仿真结果。     

Energy Detection of a Random Process in Colored Gaussian Noise

A likelihood receiver for a Gaussian random signal process in colored Gaussian noise is realized with a quadratic form of a finite-duration sample of the input process. Such a receiver may be called a "filtered energy detector." The output statistic is compared with a threshold and if the threshold is exceeded, a signal is said to be present. False alarm and detection probabilities may be estimated if tabulated distributions can be fitted to the actual distributions of the test statistic which are unknown. Gamma distributions were fitted to the conditional probability densities of the output statistic by equating means and variances, formulas for which are derived assuming a large observation interval. A numerical example is given for the case in which the noise and signal processes have spectral densities of the same shape or are flat. The optimum filter turns out to be a band-limited noise whitener. The factors governing false alarm and detection probabilities are the filter bandwidth, the sample duration, and the signal level compared to the noise. Two sets of receiver operating characteristic curves are presented to complete the example.     

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.