Robust sequential detectors employing an order statistic prefilter

The authors propose robust detection schemes for detecting signals corrupted by additive non-Gaussian noise by employing an order statistic filter (OSF) as a preprocessor. The OSF can effectively suppress non-Gaussian noise components, but its output characteristics are not easy for mathematical manipulation due to its nonlinear operation. To alleviate the difficulty in the analytical design of the detector, the variance of the OSF is approximated by a piecewise linear model. The sequential detectors are designed using the sequential probability ratio test (SPRT) and truncated SPRT (TSPRT) schemes. The performance of the proposed detectors is compared to that of other robust detectors in terms of the sample size required for given false alarm and miss detection probabilities. Finally, analytical results are verified by computer simulation     

Optimal Noise Benefits in Neyman–Pearson and Inequality-Constrained Statistical Signal Detection

We present theorems and an algorithm to find optimal or near-optimal ldquostochastic resonancerdquo (SR) noise benefits for Neyman-Pearson hypothesis testing and for more general inequality-constrained signal detection problems. The optimal SR noise distribution is just the randomization of two noise realizations when the optimal noise exists for a single inequality constraint on the average cost. The theorems give necessary and sufficient conditions for the existence of such optimal SR noise in inequality-constrained signal detectors. There exists a sequence of noise variables whose detection performance limit is optimal when such noise does not exist. Another theorem gives sufficient conditions for SR noise benefits in Neyman-Pearson and other signal detection problems with inequality cost constraints. An upper bound limits the number of iterations that the algorithm requires to find near-optimal noise. The appendix presents the proofs of the main results.     

A differentially coherent PN code acquisition receiver for CDMAsystems

New differentially coherent detectors for acquisition of direct sequence spread-spectrum signals are introduced. These detectors are alternatives to the noncoherent detectors that have been considered almost exclusively in the past. The proposed detectors are suitable for commercial code-division multiple-access (CDMA) systems which operate with a relatively large noise floor and provide a surprisingly large signal-to-noise ratio (SNR) improvement over the noncoherent detectors of approximately 5 dB. Under the random code sequence assumption, an exact analysis of the differentially coherent detection performance for both full period correlation (FPC) and partial period correlation (PPC) is carried out. The detector performance in terms of detection and false alarm probabilities for both partial and full period correlations is investigated, and the results are compared with those of classical noncoherent detection. The mean acquisition time for both single-dwell and multiple-dwell acquisition schemes are compared with their noncoherent counterparts     

High robustness and reliability of fuzzy logic based positionestimation for sensorless switched reluctance motor drives

In many applications where motor drives are used, concern. Thus, a major consideration is the reliability of position estimation schemes when sensor less SR motor drive control is employed. Hence, in this paper, the robust of a fuzzy logic based angle estimation algorithm for the switched reluctance motor (SR) motor is described. It is shown using theoretical analysis and experimental results, that by using logic, the angle estimation scheme gains a high level of robustness and reliability. A theoretical and quantitative analysis of the noise and error commonly found in practical motor drives is given, and how this can affect SR motor position estimation. An analysis is also given on the concepts of robustness and reliability. It is shown that the fuzzy logic based scheme is robust to erroneous and noisy signals commonly found in motor drives     

Combining self-learning based super-resolution with denoising for noisy images

In this paper, we propose a new learning based joint Super-Resolution (SR) and denoising algorithm for noisy images. The individual processing of denoising and SR when super-resolving a noisy image has drawbacks such as noise amplification, blurring and SR performance reduction. In the proposed joint method, principal component analysis (PCA) based denoising is closely combined with a self-learning SR framework in order to minimize the SR visual quality degradation caused by noise. Experimental results show that the joint method achieves an SR image quality improvement in terms of noise and blurring, when compared with the state-of-the-art joint method and sequential combinations of individual denoising and SR.     

Nonparametric detection

This paper considers some of the simpler nonparametric detection schemes and compares their asymptotic relative efficiencies to those of detectors which are optimal in the Neyman-Pearson sense. In the one-input case, the nonparametric sign and Wilcoxon detectors are compared to the linear detector which is optimal for the detection of a dc signal of unknown amplitude in Gaussian noise. For two-input systems the nonparametric polarity coincidence correlator is compared to the system which is optimal for the detection of a common random Gaussian component in two-input Gaussian noises. The nonparametric detectors are shown to offer advantages in simplicity of implementation and in insensitivity to changes in input statistics while performing moderately well compared to the parametric detectors. More impressive results can be obtained with more complicated detectors utilizing nonlinear rank statistics.     

Sampling designs for the detection of signals in noise

Sampling designs for the detection of sure signals in Gaussian noise are considered. Both deterministic and random sampling schemes, using optimal and nonoptimal detectors, are presented and their performance is studied. The analytical results are supplemented by comparison of performance for small and large Sample size for some representative processes including the Gauss-Markov and Wiener processes.     

Assessment of averaging spatially correlated noise for 3-D radial imaging

Any measurement of signal intensity obtained from an image will be corrupted by noise. If the measurement is from one voxel, an error bound associated with noise can be assigned if the standard deviation of noise in the image is known. If voxels are averaged together within a region of interest (ROI) and the image noise is uncorrelated, the error bound associated with noise will be reduced in proportion to the square root of the number of voxels in the ROI. However, when 3-D-radial images are created the image noise will be spatially correlated. In this paper, an equation is derived and verified with simulated noise for the computation of noise averaging when image noise is correlated, facilitating the assessment of noise characteristics for different 3-D-radial imaging methodologies. It is already known that if the radial evolution of projections are altered such that constant sampling density is produced in k-space, the signal-to-noise ratio (SNR) inefficiency of standard radial imaging (SR) can effectively be eliminated (assuming a uniform transfer function is desired). However, it is shown in this paper that the low-frequency noise power reduction of SR will produce beneficial (anti-) correlation of noise and enhanced noise averaging characteristics. If an ROI contains only one voxel a radial evolution altered uniform k-space sampling technique such as twisted projection imaging (TPI) will produce an error bound ~35% less with respect to noise than SR, however, for an ROI containing 16 voxels the SR methodology will facilitate an error bound ~20% less than TPI. If a filtering transfer function is desired, it is shown that designing sampling density to create the filter shape has both SNR and noise correlation advantages over sampling k-space uniformly. In this context SR is also beneficial. Two sets of 48 images produced from a saline phantom with sodium MRI at 4.7T are used to experimentally measure noise averaging characteristics of radial imaging and good agreement with theory is obtained.     

Multiframe super-resolution based on half-quadratic prior with artifacts suppress

The multiframe super-resolution (SR) technique aims to obtain a high-resolution (HR) image by using a set of observed low-resolution (LR) images. In the reconstruction process, artifacts may be possibly produced due to the noise, especially in presence of stronger noise. In order to suppress artifacts while preserving discontinuities of images, in this paper a multiframe SR method is proposed by involving the reconstruction properties of the half-quadratic prior model together with the quadratic prior model using a convex combination. Moreover, by analyzing local features of the underlined HR image, these two prior models are combined by using an automatically calculated weight function, making both smooth and discontinuous pixels handled properly. A variational Bayesian inference (VBF) based algorithm is designed to efficiently and effectively seek the solution of the proposed method. With the VBF framework, motion parameters and hyper-parameters are all determined automatically, leading to an unsupervised SR method. The efficiency of the hybrid prior model is demonstrated theoretically and practically, which shows that our SR method can obtain better results from LR images even with stronger noise. Extensive experiments on several visual data have demonstrated the efficacy and superior performance of the proposed algorithm, which can not only preserve image details but also suppress artifacts.     

采用主分量分析方法研究随机共振

噪声能量向信号能量的转换是随机共振具有的独特优点,也是实际应用随机共振进行弱信号检测的基础。由于随机共振系统只在合适量的噪声输入时产生最大的信噪比输出(共振),过多、过少的噪声都会降低系统的性能,因此,如何有效地控制输入到随机共振系统中的噪声十分重要。本文首次提出采用主分量分析方法研究随机共振,其基本思路是分解信号和噪声能量到相互正交的主轴方向上,之后进行重构,通过控制重构主轴的数目(重构维数),获得最优量的输入噪声。实验结果证实了方法的有效性。     

热成像系统的噪声

要评估热成像系统的性能、噪声模型是很重要的，噪声的存在使得探测器的分辨能力下降，并限制了系统的动态性能，热成像系统通常通过限制背景噪声获得最佳灵敏度，此时主要的噪声源与光子到达的随意性有关，在没有其它噪声源的情况下，探测器输出振幅也有白噪声也有白噪声，可通过采样，过滤数字化等手段消除。阐述了几种噪声源以及这些噪声源与背景噪声的共同作用，同时分析了这些噪声对噪声等效温差（NETD）和最小可分辨温差（MRTD）的影响。     

Multi-frame super-resolution reconstruction based on mixed Poisson–Gaussian noise

In this paper, a non-blind multi-frame super-resolution (SR) model based on mixed Poisson–Gaussian noise (MPGSR) is proposed. Poisson noise arises from the stochastic nature of the photon-counting process. Readout noise and reset noise inherent to the readout circuitry can be modeled by an additive Gaussian noise. Therefore, a mixed Poisson–Gaussian noise model is more appropriate for real imaging system. Instead of deriving the data fidelity term from the perspective of error norms and the corresponding influence functions, we address the multi-frame SR problem based on a statistical noise model. The derived objective function is decomposed into sub-functions and solved by the alternating direction method of multipliers (ADMM) algorithm which allows using techniques of constrained optimization. The validation of the proposed MPGSR was performed quantitatively and qualitatively on natural and X-ray images. In comparison to the optimization-based and learning-based state-of-the-art methods, we have demonstrated the feasibility of MPGSR and the significance of applying a more appropriate noise model on the SR image reconstruction.     

A novel driving scheme for synchronous rectifier suitable for modules in parallel

In this paper, a short circuit phenomenon when dc-dc modules with self-driven synchronous rectifier (SR) run in parallel is described. Three existing driving schemes are analyzed and compared. A novel driving scheme for SR maintains the performance of SR as a self-driven scheme, while solving the problem of a short circuit when modules run in parallel. In addition, the amplitude of an SR driving voltage can be adjusted by using the proposed scheme. Therefore, the scheme is more attractive in the applications of comparatively high-voltage output. The principle of operation and the characteristics of the scheme are analyzed and verified on a 36-75V input 1.8-V/60-A output experimental prototype, whose size is 1/4 brick and efficiency is above 90% under rating conditions.     

Sideband noise of a large phase carrier in interferometric sensors

Interferometric sensors using balanced detectors and phase stabilisation schemes remain susceptible to laser intensity noise if phase carrier techniques are employed. An intensity stability of 4 × 10?6 is required for the low-frequency side-band noise to be equivalent to microradian phase shift sensitivity if the phase carrier has a peak phase shift of ?/2 rad.     

Spatio-temporal blind adaptive multiuser detection

We propose blind multiuser detection schemes with antenna arrays, which is based on signal subspace estimation. They are a multichannel extension of the decorrelating and minimum mean-square-error detectors, and therefore they share their immunity to near-far effects. The blind scheme may be seen as an extension of the results in of Wang and Poor (see IEEE Trans. Inform. Theory, vol.44, p.677-90, 1998). However, it is seen that compared with the latter results when spatial diversity is considered, the proposed spatio-temporal detectors offer, with little attendant increase in computational complexity, a better performance. A blind adaptive implementation based on a new orthogonal PAST (projection approximation subspace tracking) algorithm, which is shown to be efficient for subspace tracking, is proposed. Also, we develop a blind estimation of the spatial signature based on the orthogonality between noise and signal subspaces. It is seen that the blind adaptive multiuser detection and blind spatio-temporal signature estimation can he integrated jointly     

Noise Enhanced Nonparametric Detection

This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.      

Locally optimum detection in moving average non-Gaussian noise

Detection algorithms that are locally optimum Bayes, and also asymptotically optimum, are developed for both coherent and incoherent signaling for arbitrary interference and signal waveforms when the dependence in the noise samples is represented by a moving-average model. This leads to receiver structures, which are prewhitened versions of the locally optimum detectors in the independent case. A probability-of-error expression (in the ideal-observer symmetric case), the processing gain, and the minimum-detectable signal are derived in both cases. These demonstrate, by means of an expression comparing performance between this and the independent case, that for the same large sample size (n≫1), an improvement in performance is always achieved when the noise samples are dependent, without any additional complexity in receiver structure     

相关高斯噪声自适应非参量检测器

本文讨论了相关高斯噪声自适应非参量检测器。当输入噪声的采样满足独立、同分布(IID)条件时,不管分布是什么形式,非参量检测器均能保持虚警概率恒定(CFAR)。但是,许多类噪声并不能保持IID条件,检测器也就无法保持CFAR。本文提出一种简单、可行的自适应非参量检测器,能自动调节门限,使虚警概率(Pfa)趋于恒定。这种方法的关键在于用递归滤波器的输出值来测量噪声的相关系数(Pd),并由此改变秩值检测器的检测单元的权,使其Pfa趋于恒定。从而使在一定信噪比条件下发现概率(Pd)也趋于恒定。本文给出检测单元加权的非参量检测器的检测性能和渐近性能,然后还给出自适应非参量检测器的近似计算方法和计算机模拟结果。     

中高精度光纤陀螺的误差分析及抑制措施

光纤陀螺（FOG）是惯性导航系统中新发展起来的一代惯性测量元件,其性能深受光源、多功能光电集成芯片及光电探测器等光电器件的影响。文章阐述了中高精度光纤陀螺的主要噪声机理,对两个不同精度等级的陀螺进行了Allan方差分析。根据FOG噪声参数估计值判断出FOG中白噪声和分形噪声的含量,从而验证了FOG的精度级别,并给出了不同精度FOG的改进方案,为提高FOG精度提供了理论指导。     

Preemphasis/Deemphasis Effect on the Output SNR of SSB-FM

The effect of preemphasis/deemphasis on the noise power output of FM detectors for SSB-FM is studied. Two cases are considered: namely, when the modulating signal is assumed to be sinusoidal and when it is a Gaussian random process. For both cases the noise is assumed to be stationary and Gaussian. For a sinusoid modulating signal, the improvement of(S/N)_{0}for SSB-FM detectors is obtained when the modulation index β lies in the interval (0.1-12.0) with negligible improvements for higher β. For a Gaussian modulating signal, improved results are obtained for a modulation index in the interval (0.1-0.6).