Non-parametric adaptive locally asymptotically optimum detection in additive noise

In this paper, a new approach to non-parametric signal detection with independent noise sampling is presented. The present approach is based on the locally asymptotically optimum (LAO) methodology, which is valid for vanishingly small signals and very large sample sizes, and on semi-parametric statistics. Its unique feature and essential difference from other techniques is that LAO non-parametric detectors are optimum according to the Neyman-Pearson criterion by being asymptotically uniformly most powerful at false alarm level α (AUMP (α)) and adaptive in the sense that no loss in Fisher's information number is incurred when the underlying noise process is no longer parametrically defined. Accordingly, they are robust against deviations from the postulated noise model and, unlike other non-parametric detectors, are distribution-free under both hypotheses H₀ (“noise only present”) and H₁ (“signal and noise present”). Non-parametric LAO detectors are derived from an asymptotic stochastic expansion of the log-likelihood ratio for coherent and narrowband incoherent “on-off” signals. Moreover, under the present framework it is shown that, in direct contrast to already known results, the non-parametric sign detector is AUMP (α) and adaptive even for non-constant signal samples.     

Generating model of a sub-Rayleigh fading

The generating model of a narrow band random process with the envelope probability density function left shifted relative to the Rayleigh law is presented. The model is based on the interpretation of the process as a stationary output of a nonlinear dissipative system, excited by white Gaussian noise. This representation may be considered as an effective tool for simulating a wireless communication channel with essentially severe fading.     

A Bandwidth expanding scheme for communication channels with noiseless feedback in the presence of unknown jamming noise

This paper treats the problem of transmitting a Gaussian discrete-time Markov process over a time-discrete additive white Gaussian channel with noiseless feedback which is subjected to statistically unknown jamming noise (satisfying a given power constraint). The channel is used more than once during the interval between the production of successive source letters, and the jamming noise is either allowed to be correlated with the encoder output, or forced to be totally independent of it. The complete solution obtained in the paper under a minimax criterion indicates that the optimum encoder-decoder structures are linear,and the least favorable jamming noise is a Gaussian process.     

Discrete time robust detection of stochastic signals in non-Gaussian contaminated noise

For many applications in signal detection, imprecise knowledge of the underlying noise process often makes desirable the employment of a robust detector. In this paper we consider the discrete time detection of stochastic signals in white noise, where the univariate noise density is known perfectly only on an interval about the origin. We present a method to enhance the asymptotic performance of the detector by exploiting this knowledge, and at the same time preserve robustness properties of the detector to the remaining inexact knowledge of the univariate noise density via a saddlepoint condition. We then provide examples to show that improved performance is indeed obtained.     

A radar detector using quantized envelope samples

The detection of a sequence of echo pulses with random carrier phase is an important problem in radar. It is well known that, to a first approximation, the optimum receiver is a quadratic detector for small signal-to-noise ratios and is a linear detector for large signal-to-noise ratios. In this paper, a detector based on scaling and rounding envelope samples into digital words of only a few bits is presented and analyzed. This detector is essentially a generalization of a suboptimum detector known as a binary detector which performs postdetection integration by digital counting and is particularly useful in automatic detection systems. The asymptotic relative efficiencies of this detector relative to the quadratic detector and the linear detector are obtained. Also, non-asymptotic performance comparisons based on relative efficiency and on probability of detection are made. It is shown that this detector is more efficient than the other two detectors in most signal-to-noise ratio ranges. In addition, this detector is applicable when the noise environment is somewhat different from Gaussian.     

Through-wall radar imaging using UWB noise waveforms

This paper examines the results of our research on the use of ultrawideband noise waveforms for imaging objects behind walls. The advantages of using thermally generated noise as a probing signal are introduced. The technique of heterodyne correlation used to inject coherence in the random noise probing signal and to collapse the wideband reflected signal into a single frequency are presented. Central to successful imaging through building walls is the characterization of the wideband propagation properties of wall materials and these are discussed. The basic concepts of synthetic aperture radar image formation using noise waveforms and the unique problems associated with the random nature of the transmit waveform are analyzed. We also address issues related to locating, detection, and tracking humans behind walls, using new tools for human activity characterization, namely the Hilbert-Huang Transform approach. The results indicate that noise radar technology combined with modern signal processing approaches is indeed a viable technique for covert high-resolution imaging of obscured stationary and moving targets.     

Adaptive threshold to guarantee both detection and false alarm probabilities in multi-taper based spectrum sensing

The multi-taper spectrum (MTS) estimator enjoys a relatively low computational complexity and high estimation accuracy making it an attractive method for spectrum sensing in cognitive radio (CR) networks. However, it is difficult to guarantee both detection and false alarm probabilities when its design is based on fixed threshold, especially when the noise power fluctuates due to channel conditions. In this paper, a new adaptive threshold method to guarantee both detection and false alarm probabilities for MTS based spectrum sensing is proposed. By means of estimating noise power and signal power, the decision of adaptive threshold is able to adapt the noise fluctuation and achieve efficient trade-off between detection and false alarm probabilities. A closed form expression for the adaptive threshold is derived for both additive white Gaussian noise (AWGN) channel and multipath fading channel. Several metrics are employed to compare the performance of the proposed adaptive threshold method with that of the fixed threshold methods such as: error decision probability, detection probability, false alarm probability and throughput. The obtained results show that the proposed method achieves better spectrum efficiency and high throughput in comparison with the conventional fixed and adaptive threshold methods presented in the literature.     

一种改进的基于二维高阶累积量的自适应谱线增强算法

基于二维高阶累积量的自适应谱线增强算法的迭代步长很容易受到噪声干扰的影响，本文分析了基于二维高阶累积量的自适应谱线增强算法的特点，在此基础上提出了一种改进的基于二维高阶累积量的自适应谱线增强算法。计算机仿真结果表明，本文提出的算法对高斯白噪声和高斯色噪声都有很好的抑制作用，可以改善高斯噪声背景中小空间范围的二维信号信噪比。     

Variational Bayesian based adaptive PDA filter in scenarios with unknown detection probability and heavy-tailed process noise

For target tracking systems, the probability of detecting a target is difficult to determine, and the process noise often has non-Gaussian heavy-tailed characteristics owing to interference from outliers. To address the issues associated with single target tracking within clutters in scenarios with an unknown detection probability and heavy-tailed process noise, this paper presents a variational Bayesian-based adaptive probabilistic data association filter (VB-APDAF). The beta distribution, Pearson type VII distribution and multinomial distribution are used to model the detection probability, the process noise, and the association events, respectively. To guarantee the conjugation, a novel parameter estimation strategy is employed. In this strategy, the previous state is introduced in the state update process to construct the joint probability density function of parameters to be estimated and data set. The VB framework is used to estimate the target state, detection probability, and associated events. An experiment was performed under simulated conditions to demonstrate the effectiveness of the proposed filter.     

微弱信号检测技术的研究

微弱信号检测就是利用近代电子学和信号处理方法从噪声中提取有用信号,其关键在于抑制噪声,恢复、增加和提取有用信号.本文将从信号处理系统信噪比的改善来简单地论述微弱信号检测的原理,重点介绍了用相关检测法和取样积分法检测微弱信号的原理、方法和应用.     

Order Statistical Filtering: a Robust Method of Noise Estimation

The detection of a narrowband signal in a sonar environment with spatially uncorrelated white noise depends very much on the accuracy in estimating the noise power so that a threshold can be correctly fixed. The conventional MA method presents a large bias when operated in an environment where interfering signals exist in the neighborhood. Various types of nonlinear methods based on the use of order statistics are introduced and analyzed and are found to be much more robust.     

Error analysis of TAS/MRC in Rayleigh fading channel with non-Gaussian noise

Transmit antenna selection with maximal ratio combining at the receiver (TAS/MRC) is a promising technique that can be used to avoid the hardware complexity of multiple input multiple output (MIMO) system without jeopardizing the diversity gain. The generalized Gaussian distribution (GGD) is used to model different kinds of additive noise including Gaussian, Laplacian, uniform, and impulsive. In this paper, we study the bit error performance of TAS/MRC system assuming flat Rayleigh fading channels perturbed by additive white generalized Gaussian noise (AWGGN). To this end, we provide a closed form expression for the average bit error rate of coherent modulation techniques in terms of Mejier’s G function that is readily available in many commercial mathematical software packages like MATLAB and Mathematica. Moreover, we study the asymptotic behavior of the BER at high signal to noise ratio (SNR). Analytical results are verified by simulation.     

基于前景检测的视差优化算法

目前立体匹配算法分两类,传统的匹配算法通过计算两幅图的像素点相似程度,采用的是一种局部优先的方法。而当前的策略主要将问题转化为求解能量方程,进而对全局空间进行优化,提高匹配精度,获得更好的视差图。但是在实际应用过程中,由于光学失真和噪声,平滑表面的镜面反射,投影缩减,透视失真,低纹理和重复纹理等影响,导致误匹配或者找不到匹配点,从而得不到有效视差。然而在特定的场合,可以利用有限的有效视差,基于前景检测以及最小二乘法,优化得到较为完整的前景视差图。     

Identification of Gaussian process with switching noise mode and missing data

In traditional system identification methods, it is often assumed that the output data are corrupted by Gaussian white noise which is independent and identically distributed (i.i.d.). However, this assumption may lead to poor robustness since the noise characteristic often varies throughout the sampling process. In this work, output measurements affected by switching Gaussian noise are considered. In addition, a Markov chain model is utilized to describe the multi-mode behavior of the noises. Meanwhile, the collected data are usually incomplete in practice. Taking these circumstances into account, a new algorithm for Gaussian process regression (GPR) with switching noise mode and missing data is introduced. The parameters of the model are estimated by expectation maximization (EM) algorithm via conjugate gradient (CG) method. Two numerical examples along with a continuous stirred tank reactor simulation are employed to verify the effectiveness of the proposed algorithm. The superior performance is demonstrated by comparing the proposed algorithm with other existing relevant methods.     

The stochastic harmonic oscillator with linear damping

The moments of the stochastic harmonic oscillator are examined, in the presence of linear damping. The procedures we follow are those described in earlier papers for stochastic linear systems and for the undamping case. The noise is approximated from the white noise process and has small but finite correlation time. The first moment is shown to be bounded and tending to zero, as time approaches infinity. The second moment is also shown to be bounded, but only if the damping factor satisfies a proper condition. Results are compared with those found in the case where there is no damping and with those obtained in other works, using different methods.     

Order Estimation of Linear Time Invariant System

An order estimation of a linear time invariant system has been developed. The order estimation (the number of sets of significant system parameters) is based on function elimination filters. In presence of noise, the estimation procedure has been improved by using the composite hypothesis and the maximum likelihood ratio. Finally, the estimated system order can be used to estimate the system parameters. It is to be assumed that the investigated system is not overdumped, has no aliasing problems and the system input is “white noise”. The proposed method differs from other estimation methods because the system order can be found without knowledge about the system parameters.     

超宽带穿墙雷达压缩感知成像中chipping序列的设计

在超宽带穿墙雷达压缩感知成像中,常常使用Rademacher序列作为模拟信息转换器的chipping序列。而这种序列的功率谱类似于高斯白噪声的功率谱,它与回波信号的功率谱不匹配,导致低速采样前信号的信噪比偏低,最终影响了成像效果。文章从低速采样前的平均信噪比最大化入手,构建设计匹配chipping序列的优化算法以导出其成立的条件,给出一种使用马尔科夫链游程长度受限(RLL)序列来设计匹配chipping序列的解决方案。仿真结果表明,使用马尔科夫链RLL序列设计匹配chipping序列的成像结果优于Rademacher序列,其图像的信噪比提高2~3dB。     

The stochastic harmonic oscillator with finite correlation times

The moments of an undamped stochastic harmonic oscillator are examined using iteration procedures described in earlier papers. The noise is similar to white noise, but has finite correlation time which is taken to be less than the period of the oscillator. When the time of observation is finite, the first moment is explicitly given, attempting first-order approximation. For infinite time, we establish certain conditions which assure boundedness of the first and second moments. All the results obtained are applied to the white noise when its correlation time is considered finite.     

On the achievable tracking performance of NCSs over SNR limited channels

In this paper, the achievable tracking performance limitations of discrete-time, multi-input multi-output (MIMO) networked control systems (NCSs) are studied. The channel is modeled as an additive white Gaussian noise and signal-to-noise ratio (SNR) limited channel with feedback. Under this framework, the closed relationships among stabilization, tracking performance, and SNR limited are quantitatively revealed. Some new results a.erived according to the allpass factorization and Youla parameterization of two degrees of freedom controller. The results show that the best tracking performance is in connection with the unstable poles, non-minimum phase zeros of the system. It is also demonstrated that the tracking performance will be badly degraded by feedback channel noise and due to the SNR limited. Finally, a simulation example is presented to validate the conclusions.