Detection of determinate signals with a fixed false alarm probability in the case of autoregression model of interference with unknown parameters

The paper is devoted to investigation of nonadaptive detection of determinate signals with fixed false alarm probability in the case of unknown covariance matrix of interference components. The interference model represents the Gaussian model of random process of the first-order autoregression (AR-interference). Application of the method of projection in the N-dimensional Hilbert space permitted to formulate the conditions, when the low-rank detectors guaranteed fixed false alarm probability at an unknown covariance matrix of AR-interference. The efficiency of this detector is assessed by the simulation method. It is shown that for the first-order AR-interference an acceptable loss (as compared with the Neumann-Pearson optimal detector) can be achieved.     

Gaussian Cramer-Rao bound for direction estimation of noncircular signals in unknown noise fields

This paper focuses on the stochastic Cramer-Rao bound (CRB) on direction of arrival (DOA) estimation accuracy for noncircular Gaussian sources in the general case of an arbitrary unknown Gaussian noise field parameterized by a vector of unknowns. Explicit closed-form expressions of the stochastic CRB for DOA parameters alone are obtained directly from the Slepian-Bangs formula for general noncircular complex Gaussian distributions. As a special case, the CRB under the nonuniform white noise assumption is derived. Our expressions can be viewed as extensions of the well-known results by Stoica and Nehorai, Ottersten et al., Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models.     

Importance sampling for estimation of false alarm probability

The purpose of this paper is to describe a technique of importance sampling for Monte Carlo simulation of Radar signal detectors under the ‘No Signal’ condition. The input to the detector is ‘restricted’ to a range that is more likely to cause a ‘false alarm’, thereby increasing the number of false alarms in a given sample. A model based on the theory of the Gauss-Markov process is developed so as to allow consideration of the case when the successive noise (clutter) samples are correlated. The technique enables estimation of very low false alarm probabilities with a relatively moderate sample size.     

Analysis of stabilization quality false alarm probability by a radar adaptive detector in order statistics

New expressions are derived for estimating the false alarm probability by adaptive threshold detectors, using the method of averaging and method of order statistics, in the presence of nonstationary interference. Potentialities of these devices, from the viewpoint of their stabilization of false alarm probability in the presence of nonstationary interference, are defined, and comparative analysis of the devices is performed. Original Russian Text ? F. M. Andreyev, I. V. Taranchenko, 2007, published in Izvestiya VUZ. Radioelektronika, 2007, Vol. 50, No. 4, pp. 3–11.     

Bit-error probability for optimum combining of binary signals in the presence of interference and noise

We derive an exact bit-error probability (BEP) expression for coherent detection of binary signals with optimum combining in wireless systems in the presence of multiple cochannel interferers and thermal noise. A flat Rayleigh fading environment with space diversity, uncorrelated equal-power interferers, and additive white Gaussian noise is considered. The approach is to use the chain rule of conditional expectation together with the joint probability density function (pdf) of the eigenvalues of the interference correlation matrix. This joint pdf is related to the Vandermonde determinant. Let N/sub A/ denote the number of antennas and N/sub I/ the number of interferers. We consider both the cases of an overloaded system, in which N/sub I//spl ges/N/sub A/, and an underloaded system, in which N/sub I/相似文献   

The equivalence- singularity problem for Gaussian signals in Gaussian noise (Corresp.)

The structural properties and sample path behavior of Gaussian signals which can and which cannot be perfectly detected in a given Gaussian noise are described.     

Linear prediction analysis of speech signals in the presence ofwhite Gaussian noise with unknown variance

A simple method is presented to compensate for noise effects before performing linear prediction analysis of speech signals in the presence of white noise with unknown variance. The method determines a suitable bias that should be subtracted from the zero-lag autocorrelation function, rather than deriving the exact noise variance. The resulting linear prediction filter is guaranteed to be stable. Since the bias used is always smaller than the minimum eigenvalue of the autocorrelation matrix. In addition to a comparison with other methods, the proposed method is examined from various viewpoints, including the degree of formant intensity, signal-to-noise ratio (SNR), deviation of compensated spectra and objective distortion measures. The improvements observed across a data set, consisting of four sentences uttered by six speakers, indicate that the compensated spectra measured with low SNRs are comparable to the uncompensated counterparts measured with approximately 5 dB higher SNRs     

Expansion of likelihood ratio for signals in Gaussian noise

It is shown that the coefficients of the Taylor expansion of the likelihood ratio for random signals in Gaussian noise are proportional to the mean-square estimates of the successive powers of the signal evaluated at zero. A similar relationship holds for the expansion of the logarithm of the likelihood ratio.     

Estimation of the relative delay of two similar signals of unknown phases in white Gaussian noise

In this paper we consider the estimation of the delay of a signal of unknown phase obscured by white noise and by another signal of unknown phase and known delay. We also consider the situation where both delays are unknown. Use is made of Shannon's sampling analysis^{1}as developed by Woodward.^{2}We conclude that the relative carrier phase of the two signals must be first estimated in order to maximize the {em a posteriori} probability of the estimate, but that knowledge of the absolute phase is only of secondary value.     

Improved energy detector for random signals in gaussian noise

New and improved energy detector for random signals in Gaussian noise is proposed by replacing the squaring operation of the signal amplitude in the conventional energy detector with an arbitrary positive power operation. Numerical results show that the best power operation depends on the probability of false alarm, the probability of detection, the average signal-to-noise ratio or the sample size. By choosing the optimum power operation according to different system settings, new energy detectors with better detection performances can be derived. These results give useful guidance on how to improve the performances of current wireless systems using the energy detector. It also confirms that the conventional energy detector based on the generalized likelihood ratio test using the generalized likelihood function is not optimum in terms of the detection performance.     

Probability of false alarm in CA-CFAR device downstream from linear-law detector

An approximate and simple expression for the false alarm probability of a CA-CFAR device downstream from a linear-law detector is presented. The results obtained are in good agreement with those found using a Monte Carlo simulation.<>     

Memoryless quantizer- detectors for constant signals in m-dependent noise

The performance and design of data quantizers for use in memoryless discrete-time detection systems operating with dependent samples is considered. This problem is approached on a large-sample-size small-signal basis, andm-dependence is used to model the dependence structure among data samples. By considering the particular situation of detecting known constant signals in additive noise, previous results for memoryless detector analysis are applied to derive expressions for the evaluation of quantizer-detector performance for this situation. Design criteria are established for the optimum selection of quantizer parameters, and these are seen to be generalizations of earlier designs for the corresponding independent sampling case.     

A sharp upper bound for the probability of error of the likelihoodratio test for detecting signals in white Gaussian noise

A new sharp upper bound for the probability of error of the likelihood ratio test is given for the detection in white Gaussian noise of any random vector whose norm is greater than, or equal to, a given value and whose probability of presence is less than, or equal to, one half. Also, a new test for the detection of such vectors is described. This test does not depend on the distribution of the signal vector but nevertheless its probability of error is less than, or equal to, the given upper bound     

A general likelihood-ratio formula for random signals in Gaussian noise

It is shown that the likelihood ratio for the detection of a random, not necessarily Gaussian, signal in additive white Gaussian noise has the same form as that for a known signal in white Gaussian noise. The role of the known signal is played by the casual least-squares estimate of the signal from the observations. However, the "correlation" integral has to be interpreted in a special sense as an Itô stochastic integral. It will be shown that the formula includes all known explicit formulas for signals in white Gaussian noise. However, and more important, the formula suggests an "estimator-correlator" philosophy for engineering approximation of the optimum receiver. Some extensions of the above result are also discussed, e.g., additive finite-variance, not necessarily Gaussian, noise plus a white Gaussian noise component. Purely colored Gaussian noise can be treated if whitening filters can be specified. The analog implementation of Itô integrals is briefly discussed. The proofs of the formulas are based on the concept of an innovation process, which has been useful in certain related problems of linear and nonlinear least-squares estimation, and on the concept of covariance factorization.     

雪崩光电二极管恒虚警率控制在激光成像系统中的应用

雪崩光电二极管以其接收灵敏度高,响应速度快等优点,常用于扫描式激光成像系统中,由于雪崩光电二极管的工作电压随背景和温度的变化而变化,因此正确设置其工作偏压,对充分发挥接收系统的探测灵敏度是重要的。主要讨论采用恒虚警率控制法自动设置雪崩光电二极管的偏压,调整噪声门限以获得光电接收系统最佳工作状态。     

超宽带信号在高斯噪声中传播性能的研究

超宽带无线通信技术使用微弱的、持续时间极短的脉冲进行短距离通信。超宽带信号具有极宽的射频带宽和较低的中心频率，这种特性可使短距无线通信利用较低的发射功率进行高速传输。根据信号的频谱及其在高斯噪声中的传播性能，研究超宽带信号的减少衰减余量特性，给出不同进制波形的PPM调制超宽带信号比特误码率仿真分析。     

Statistically optimum AGC for Rayleigh-distributed signals of unknown strength (Corresp.)

Receiver gains are determined that minimize the average cost of excluding a Rayleigh-distributed signal from a receiver's finite dynamic range when a maximum-likelihood estimate of the unknown Rayleigh parameter is used. Optimum performance is independent of the unknown parameter and converges to "exact knowledge" as sample size increases.