On the estimation of error probability using generalized extreme-value theory (Corresp.)

The application of the generalized theory of extreme values is discussed. When the hypothesis of the theory is not strictly satisfied, as is inevitable in practice, we deal with an approximation that must be carefully used to yield accurate results. A numerical example provides some insight into this approximation problem.     

Robust transient signal detection using the oversampled Gaborrepresentation

The Gabor representation has been investigated previously as an effective and efficient means of detecting transient signals. The major problem associated with the most commonly used form of the Gabor representation is that it assumes that signals have integer arrival times and modulation frequencies-an assumption that, if violated, causes signal miss-match and deterioration in detector performance. The present correspondence presents the concept of oversampling the Gabor representation, which makes the representation more robust to certain types of signal mismatch. To show this, the performance of an oversampled Gabor detector is compared to that of critically sampled Gabor detectors. Gabor coefficients are calculated in the oversampled representation via the least squares method, which has been shown to be superior to the more commonly used biorthogonal function method in terms of corresponding detector performance     

Robust speech pulse detection using adaptive noise modelling

The problem of speech pulse detection with additive noise at a signal-to-noise ratio (SNR) as low as 0 and -6 dB is addressed. The noise is assumed to be reasonably stationary and correlated. Three techniques have been examined: the autoregressive analysis of noise; spectral density comparison; and the non-stationarity measure     

Robust fabric defect detection and classification using multiple adaptive wavelets

The wavelet transform has been widely used for defect detection and classification in fabric images. The detection and classification performance of the wavelet transform approach is closely related to the selection of the wavelet. Instead of predetermining a wavelet, a method of designing a wavelet to adapt to the detection or classification of fabric defects has been developed. For further improvement of the performance, the paper extends the adaptive wavelet-based methodology from the use of a single adaptive wavelet to multiple adaptive wavelets. For each class of fabric defect, a defect-specific adaptive wavelet was designed to enhance the defect region at one channel of the wavelet transform, where the defect region can be detected by using a simple threshold classifier. Corresponding to the multiple defect-specific adaptive wavelets, the multiscale edge responses to defect regions have been shown to be more efficient in characterising defects, which leads to a new approach to the classification of defects. In comparison with the single adaptive wavelet approach, the use of multiple adaptive wavelets yields better performance on defect detection and classification, especially for defects that are poorly detected by the single adaptive wavelet approach. The proposed method has been evaluated on the inspection of 56 images containing eight classes of fabric defects, and 64 images without defects. In defect detection, 98.2% detection rate and 1.5% false alarm rate were achieved, and in defect classification, 97.5% accuracy was achieved.     

Robust Wiener- Kolmogorov theory

A minimax formulation is considered for the problem of designing robust linear causal estimators of linear functions of discrete-time wide-sense stationary signals when knowledge of the signal and/or noise spectra is inexact. The solution is given (under mild regularity conditions) in terms of a least favorable pair of spectra, thus reducing the minimax problem to a direct maximization problem which in many cases can be solved easily. It is noted that this design method leads, in particular, to robustn-step predictors, robust causal filters, and robustn-lag smoothers. The method of design is illustrated by a thorough development of the special case of one-step noiseless prediction. Further, solutions are given explicitly for the problem of robust causal filtering of an uncertain signal in white noise, and numerical examples are given for this case which illustrate the effectiveness of this design.     

Robust detection in DS-CDMA

Robust single-user detection is employed in a direct sequence code-division multiple-access (DS-CDMA) system in which the noise process contains impulsive components. The breakdown point is computed for a mixture noise model. The bit error probability expressions are derived under a Gaussian mixture. The performance is also evaluated in the presence of power imbalance and asynchronous reception. Noise, rather than interference, is shown to be the primary obstacle in achieving good performance for certain practical signal power and user load levels. It is concluded that DS-CDMA employing a robust correlator receiver performs better than the conventional matched filter in an impulsive noise environment     

采用尺度空间理论的红外弱小目标检测方法

为了检测红外场景中尺寸大小变化的弱小目标,针对传统滤波方法中固定大小滤波核对此类特性目标检测表现出的不足,提出一种基于尺度空间理论的红外弱小目标检测方法。首先对弱小目标特性进行分析,提出采用点扩散函数形式的目标模型来描述弱小目标;采用固定自适应邻域的方法对原始红外图像进行预处理,抑制背景杂波,增强目标能量;依据尺度规范化后的拉普拉斯尺度空间对图像不同元素滤波响应的不同,获取图像中的可疑目标,利用可疑目标点与其周围像素的梯度关系得到可疑目标点的中心坐标,并据此得到其在图中的尺寸大小;对每个可疑目标划分一个自适应大小窗口,获取分割阈值,分割出真实目标。实验结果表明,该方法能较好地检测出弱小目标,且具有较低的虚警率。     

Robust detection in digital communications

This paper deals with the problem of communicating through unspecified noise. Detectors, robust against variations in the probability density function of the noise, are developed and discussed. The paper covers three issues. First, the relation between a distance measuring receiver and a correlating receiver in a general case is shown. Second, a theoretical method for the computation of an upper limit for the probability of symbol error is presented. This computation fits into the ordinary framework for computation of the error probability by changing the inverted noise density 2/N₀ to efficacy, ϵ. Efficacy is defined in the paper. Third, detectors based on M-, i.e., maximum likelihood type, and R-, i.e., rank, statistics are tested and compared for GMSK and π/4-shifted DQPSK. From numerical comparisons of the upper bounds and their simulated estimates for robust detectors, it is concluded that the loss in Gaussian noise is very small compared to the optimum quadratic detector. The gain, compared to a nonrobust receiver optimized to Gaussian noise, is around 0.5 to 2 dB for large SNR and around 2 to 4 dB for low SNR in impulsive noise. This offers new methods of significantly improving communication when the noise is unknown     

Robust detection of known signals

The problem of detection of known signals in additive noise is solved under the assumption that the unknown noise density is a member of some known family of symmetric densities. A general approach to the design of receivers that are asymptotically most robust is established. As an example, a detector is derived by applying the procedure to the special case obtained when the noise density family is defined byF=left{f left| int^{a}_{-a} f(x) dx = p, f mbox{symmetric} right}.Simulation results showing the detector's performance for small sample sizes are provided.     

Robust face detection and analysis

This paper presents a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. The two maps and local similarity map are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. The experimental results demonstrate its validity and robustness to identify faces under certain variations.     

Robust noise detection for speech detection and enhancement

A simple and robust method of reliably detecting stationary noise periods in a mobile telephony environment is presented. Such noise detection techniques are useful for updating parameters in adaptive voice activity detectors and for speech enhancement techniques, such as spectral subtraction, which require accurate noise models     

Robust location detection with sensor networks

We propose a novel framework for location detection with sensor networks, based on the theory of identifying codes. The key idea of this approach is to allow sensor coverage areas to overlap so that each resolvable position is covered by a unique set of sensors. In this setting, determining a sensor-placement with a minimum number of sensors is equivalent to constructing an optimal identifying code, an NP-complete problem in general. We, thus, propose and analyze new polynomial-time algorithms for generating irreducible (but not necessarily optimal) codes for arbitrary topologies. Our algorithms incorporate robustness properties that are critically needed in harsh environments. We further introduce distributed versions of these algorithms, allowing sensors to self-organize and determine a (robust) identifying code without any central coordination. Through analysis and simulation, we show that our algorithms produce nearly optimal solutions for a wide range of parameters. In addition, we demonstrate a tradeoff between system robustness and the number of active sensors (which is related to the expected lifetime of the system). Finally, we present experimental results, obtained on a small testbed, that demonstrate the feasibility of our approach.     

Robust feature detection for signal interception

The problem of detecting the presence of direct sequence spread-spectrum signals in noise is considered, and the detector consisting of a filter followed by a delay-and-multiply device is optimized with respect to the filter bandwidth and the delay to maximize robustness to errors in assumed values of pulse rate and carrier frequency. Output SNR is used for performance optimization, and the results are corroborated by evaluating receiver operating characteristics     

Robust detection with the gap metric

In a multipath communication channel, the optimal receiver is matched to the maximum likelihood (ML) estimate of the multipath signal. In general, this leads to a computationally intensive multidimensional nonlinear optimization problem that is not feasible in most applications. We develop a detection algorithm that avoids finding the ML estimates of the channel parameters while still achieving good performance. Our approach is based on a geometric interpretation of the multipath detection problem. The ML estimate of the multipath signal is the orthogonal projection of the received signal on a suitable signal subspace S. We design a second subspace G, which is the representation subspace, that is close to S but whose orthogonal projection is easily computed. The closeness is measured by the gap metric. The subspace G is designed by using wavelet analysis tools coupled with a reshaping algorithm in the Zak transform domain. We show examples where our approach significantly outperforms the conventional correlator receiver (CR) and other alternative suboptimal detectors     

Robust multiuser detection in non-Gaussian channels

In many wireless systems where multiuser detection techniques may be applied, the ambient channel noise is known through experimental measurements to be decidedly non-Gaussian, due largely to impulsive phenomena. The performance of many multiuser detectors can degrade substantially in the presence of such impulsive ambient noise. We develop robust multiuser detection techniques for combating multiple-access interference and impulsive noise in CDMA communication systems. These techniques are based on the M-estimation method for robust regression. Analytical and simulation results show that the proposed robust techniques offer significant performance gain over linear multiuser detectors in impulsive noise, with little attendant increase in computational complexity. We also develop a subspace-based technique for blind adaptive implementation of the robust multiuser detectors, which requires only the signature waveform and the timing of the desired user in order to demodulate that user's signal. The robust multiuser detection technique and its blind adaptive version can be applied to both synchronous and asynchronous CDMA channels     

Robust skin detection in real-world images

Human skin detection in images is desirable in many practical applications, e.g., human–computer interaction and adult-content filtering. However, existing methods are mainly suffer from confusing backgrounds in real-world images. In this paper, we try to address this issue by exploring and combining several human skin properties, i.e. color property, texture property and region property. First, images are divided into superpixels, and robust skin seeds and background seeds are acquired through color property and texture property of skin. Then we combining color, region and texture properties of skin by proposing a novel skin color and texture based graph cuts (SCTGC) to acquire the final skin detection results. Comprehensive and comparative experiments show that the proposed method achieves promising performance and outperforms many state-of-the-art methods over publicly available challenging datasets with a great part of hard images.     

Bayes estimation of the extreme-value reliability function

The authors obtain Bayes estimates of the reliability function of the extreme value distribution by using two Bayes approximation procedures: Lindley (1980), and Tierney and Kadane (1986). These estimates were compared to maximum-likelihood estimates (MLE) based on a Monte Carlo simulation study. Jeffreys invariant prior was used in the comparison for both Bayes procedures. The MLE are superior to either of the Bayes estimates, except for small values of t. The simpler Lindley Bayes procedure gives estimates with smaller root-mean-square error than estimates obtained by the Tierney and Kadane procedure except for large values of t. From a practical standpoint, the ML method is easiest to use and more accurate for the extreme value distribution than the two Bayes approximation procedures. Both Bayes procedures seem to perform equally. However, the Lindley method is easier to use with little loss of accuracy     

采用D-S证据理论的双色红外小目标融合检测

针对远距离低信噪比条件下目标检测难的实际问题,提出采用D-S证据理论的双色红外小目标融合检测方法。该方法首先采用统计检测方法对各传感器图像进行目标检测处理;接着采用“或”逻辑对各传感器的目标检测结果进行融合,以降低目标漏检的可能性;然后在各传感器图像中提取融合检测结果中各候选目标区域的多个图像特征作为进一步消除虚警的证据;最后采用D-S证据理论对各候选目标区进行基于多特征的目标融合识别处理,并将识别结果作为整个系统最终的目标检测输出。实验结果证明了该算法的有效性。     

Robust detection of signals in dependent noise

The robust detection of signals in additive dependent noise is considered. The solution to the finite-sample problem is obtained when the Bayes risk is used as the performance measure. For the multivariate densities involved we assume that they belong to an e-contamination model. The robust detection structure is shown to be optimum for the least-favorable density and is a censored version of the nominal likelihood ratio.     

Robust data fusion for multisensor detection systems

Minimax robust data fusion schemes for multisensor detection systems with discrete-time observations characterized by statistical uncertainty are developed and analyzed. Block, sequential, and serial fusion rules are considered. The performance measures used, and made robust with respect to the uncertainties, include the error probabilities of the hypothesis testing problem in the block fusion case and the error probabilities and expected numbers of samples or sensors in the sequential and serial fusion cases. For different sensor observation statistics, the minimax robust fusion rules are derived for two asymptotic cases of interest: when the number of sensors is large and when the number of times the fusion center collects the local (sensor) decisions is large. Moreover, for the case of identical sensor observation statistics and a large number of sensors, it is shown that there is no loss in optimality, if local tests using likelihood ratios and equal thresholds are used in the sequential fusion rule. In all situations, the robust decision rules at the sensors and the fusion center are shown to make use of likelihood ratios and thresholds that depend on the least-favorable probability distributions of the uncertainty class describing the statistics of sensor observations