Performance of the adaptive generalised matched subspace constant false alarm rate detector in non-Gaussian noise: an experimental analysis

The authors deal with the performance analysis of an adaptive version of the generalised matched subspace detector (GMSD) in compound-Gaussian clutter with unknown covariance matrix. The original GMSD was proposed to detect subspace signals in compound-Gaussian noise with known covariance matrix and ensures the constant false alarm rate (CFAR) property. In real situations, this assumption is unrealistic, which means that the covariance matrix must be estimated from training data. The authors use a robust estimate of the covariance matrix called the fixed-point estimate, recently proposed in the literature. The performance of the obtained adaptive detector, in terms of CFAR behaviour and probability of detection, is evaluated in the presence of real sea clutter data, collected by the McMaster IPIX radar.     

Knowledge-based recursive least squares techniques for heterogeneous clutter suppression

The design of knowledge-based adaptive algorithms has been dealt with for the cancellation of heterogeneous clutter. To this end, the application of the recursive least squares (RLS) technique has been revisited for the rejection of unwanted clutter, and modified RLS filtering procedures have been devised accounting for the spatial variation of the clutter power as well as of the disturbance covariance persymmetry property. Then the authors introduce the concept of knowledge-based RLS and explain how the a priori knowledge about the radar operating environment can be adopted for improving the system performance. Finally, the authors assess the benefits resulting from the use of knowledge-based processing both on simulated and on measured clutter data collected by the McMaster IPIX radar in November 1993     

转换观测卡尔曼滤波算法中转换统计量的一种改进计算方法

为了改进转换观测卡尔曼滤波算法的性能,提出了一种计算转换观测误差统计量(均值和协方差)的改进方法.有两组信息可用于计算这些统计量,即观测值和滤波器的一步预测值.首先,推导了滤波器球坐标状态一步预测误差的均值和协方差.然后,利用滤波器球坐标状态一步预测值更精确地计算了转换观测误差的统计量.最后,将改进的转换观测误差统计量...     

Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming

In adaptive ultrasound imaging, accurate estimation of the array covariance matrix is of great importance, and biases the performance of the adaptive beamformer. The more accurately the covariance matrix can be estimated, the better the resolution and contrast can be achieved in the ultrasound image. To this end, in this paper, we have used the forward-backward spatial averaging for array covariance matrix estimation, which is then employed in minimum variance (MV) weights calculation. The performance of the proposed forward-backward MV (FBMV) beamformer is tested on simulated data obtained using Field II. Data for two closely located point targets surrounded by speckle pattern are simulated showing the higher amplitude resolution of the FBMV beamformer in comparison to the forward-only (F-only) MV beamformers, without the need for diagonal loading. A circular cyst with a diameter of 6 mm and a phantom containing wire targets and two cysts with different diameters of 8 mm and 6 mm are also simulated. The simulations show that the FBMV beamformer, in contrast to the F-only MV, could estimate the background speckle statistics without the need for temporal smoothing, resulting in higher contrast for the FBMV-resulted image in comparison to the MV images. In addition, the effect of steering vector errors is investigated by applying an error of the sound speed estimate to the ultrasound data. The simulations show that the proposed FBMV beamformer presents a satisfactory robustness against data misalignment resulted from steering vector errors, outperforming the regularized F-only MV beamformer. These improvements are achieved without compromising the good resolution of the MV beamformer and resulted from more accurate estimation of the covariance matrix and consequently, the more accurate setting of the MV weights.     

Resolution Adaptive Fixed Rank Kriging

The spatial random effects model is flexible in modeling spatial covariance functions and is computationally efficient for spatial prediction via fixed rank kriging (FRK). However, the model depends on a class of basis functions, which if not selected properly, may result in unstable or undesirable results. Additionally, the maximum likelihood (ML) estimates of the model parameters are commonly computed using an expectation-maximization (EM) algorithm, which further limits its applicability when a large number of basis functions are required. In this research, we propose a class of basis functions extracted from thin-plate splines. The functions are ordered in terms of their degrees of smoothness with higher-order functions corresponding to larger-scale features and lower-order ones corresponding to smaller-scale details, leading to a parsimonious representation of a (nonstationary) spatial covariance function with the number of basis functions playing the role of spatial resolution. The proposed class of basis functions avoids the difficult knot-allocation or scale-selection problem. In addition, we show that ML estimates of the random effects covariance matrix can be expressed in simple closed forms, and hence the resulting FRK can accommodate a much larger number of basis functions without numerical difficulties. Finally, we propose to select the number of basis functions using Akaike’s information criterion, which also possesses a simple closed-form expression. The whole procedure, involving no additional tuning parameter, is efficient to compute, easy to program, automatic to implement, and applicable to massive amounts of spatial data even when they are sparsely and irregularly located. Proofs of the theorems and an R package autoFRK are provided in supplementary materials available online.     

系统识别过程中参数协差阵的先验估计

系统识别的损伤检测和估计是建立在参数估计上的。当考虑参数的随机特性时,必须对参数的方差和参数之间的相关系数作出估计。本文提出了参数协差阵的先验估计方法,对于同一单元内不同类型的参数,通过将参数分解为一系列统计独立的要素,利用函数协差阵与变量协差阵之间的关系求得参数的协差阵。不同单元的同一类型参数的相关系数通过对检测数据的数理统计或利用工程师的经验得到估计。     

Joint ML time-frequency synchronisation and channel estimation algorithm for MIMO-OFDM systems

The maximum-likelihood (ML) time-frequency synchronisation algorithm combined with channel estimation for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems in frequency selective fading channels is addressed. In the proposed algorithm, the authors use two steps to maximise an ML metric to obtain first the frequency offset and then timing. A fast Fourier transform algorithm is used to estimate the frequency offset. Using these two estimates, the channel is identified. A simple iterative algorithm is proposed to improve the frequency offset estimation. The performance of the proposed synchronisation approach, in terms of timing failure probability and mean square error of the estimated frequency offset and bit error rate, is compared with others in the literature. Comparison of simulation results with the Cramer-Rao lower bound clearly illustrates the accuracy of the proposed algorithm, which outperforms the state-of-the-art synchroniser devices in the open literature.     

Objective assessment of image quality. IV. Application to adaptive optics

The methodology of objective assessment, which defines image quality in terms of the performance of specific observers on specific tasks of interest, is extended to temporal sequences of images with random point spread functions and applied to adaptive imaging in astronomy. The tasks considered include both detection and estimation, and the observers are the optimal linear discriminant (Hotelling observer) and the optimal linear estimator (Wiener). A general theory of first- and second-order spatiotemporal statistics in adaptive optics is developed. It is shown that the covariance matrix can be rigorously decomposed into three terms representing the effect of measurement noise, random point spread function, and random nature of the astronomical scene. Figures of merit are developed, and computational methods are discussed.     

Parametric identification of two-port models in the frequencydomain

The authors treat the problem of parametric estimation of linear time-invariant dynamic two-port models (e.g. the short-circuit admittance matrix) from experimental data. A multivariate frequency-domain Gaussian maximum likelihood estimator is proposed to estimate the unknown coefficients occurring in the rational two-port model. It takes the perturbing noise of all the measured voltages and currents into account. The covariance matrix of the noise is assumed to be known, e.g. from measurements. The estimates and their covariance matrix are obtained as the result of an optimization procedure. The value of the minimized loss function and the covariance matrix of the estimates can be used to determine the model structure. The ability of the estimator to handle real measurement problems is demonstrated by means of experimental results. Using the estimated two-part parameters of an unloaded band-pass filter, it was possible to predict the transfer function of the loaded filter within an error of ±0.01 dB on the magnitude and ±0.1° on the phase     

噪声方差自适应修正的混合系统故障诊断方法

针对混合系统故障诊断问题,提出了一种模型噪声方差自适应修正的多模态故障诊断方法。首先,在粒子滤波的框架内将混合系统故障诊断建模为最优状态估计与跟踪问题,利用实时观察信息和各个模态先验的转移概率,估计最优的故障模态,并针对估计结果进行单独的建模分析;接着,根据平滑估计值和当前观测信息之间的相关性,建立噪声方差在线自适应检测机制,对模态噪声方差进行自适应更新,有效克服了模型噪声统计特性时变对滤波精度的影响,提升了算法的鲁棒性。最后,针对多种模态估计跟踪进行了充分的仿真分析,验证了本文方法的有效性和鲁棒性。     

Adaptive detectors with diagonal loading for airborne multi-input multi-output radar

The problem of adaptive target detection for airborne multi-input multi-output (MIMO) radars with space-time receivers in the presence of Gaussian interference (including clutter and noise) is studied. Previous work has assumed the interference covariance matrix to be known. The case with unknown covariance matrix is investigated here. By exploiting the low rank property of clutter subspace, generalised likelihood ratio test detector and adaptive matched filter detector with diagonal loading are suggested to improve the detection performance of MIMO radars in limited secondary data case. The closed-form detection probabilities and false alarm probabilities of the two proposed detectors are derived and numerically evaluated. Theoretical analysis and numerical results show the advantages of the proposed detectors.     

基于自适应子空间估计的DOA跟踪算法

运用特征子空间类高分辨方法的关键在于信号或噪声子空间的估计。实际上有些信号的统计特性通常随时间变化，为了得到参数的实时估计值，需要随时根据新的阵列接收数据对信号或噪声子空间进行更新?文中分析了一种自适应子空间估计算法，即MALASE(Maximum Likelihood Adaptive Subspaee Estimation)算法然后，把MALASE算法与最小范数(Mini—Norm)高分辨方位计算法相结合．并应用零点跟踪技术，提出了一种自适应Mini—Norm算法，可用于对时变的信号波达方向(DOA)进行跟踪估计。仿真结果验证了该算法具有较好的跟踪性能。     

Recursive parametric tests for multichannel adaptive signal detection

The problem of detecting a multichannel signal in spatially and temporally coloured disturbances is considered. The parametric Rao and parametric generalised likelihood ratio test detectors, recently developed by modelling the disturbance as a multichannel autoregressive (AR) process, have been shown to perform well with limited or even no range training data. These parametric detectors, however, assume that the model order of the multichannel AR process is known a priori to the detector. In practice, the model order has to be estimated by some model order selection technique. Meanwhile, a standard non-recursive implementation of the parametric detectors is computationally intensive since the unknown parameters have to be estimated for all possible model orders before the best one is identified. To address these issues, herein the joint model order selection, parameter estimation and target detection are considered. We present recursive versions of the aforementioned parametric detectors by integrating the multichannel Levinson algorithm, which is employed for recursive and computationally efficient parameter estimation, with a generalised Akaike Information Criterion for model order selection. Numerical results show that the proposed recursive parametric detectors, assuming no knowledge of the model order, yield a detection performance nearly identical to that of their non-recursive counterparts at significantly reduced complexity.     

Multiple sparse priors technique with optimized patches for brain source localization

Localizing brain neural activity using electroencephalography (EEG) neuroimaging technique is getting increasing response from neuroscience researchers and medical community. It is due to the fact that brain source localization has a variety of applications for diagnoses of various brain disorders. This problem is ill-posed in nature because an infinite number of source configurations can produce the same potential at the head surface. Recently, a new technique that is based on Bayesian framework, called the multiple sparse priors (MSP), was proposed as a solution to this problem. The MSP develops the solution for source localization using the current densities associated with dipoles in terms of prior source covariance matrix and sensor covariance matrix, respectively. Then, it uses the maximization of the cost function of the free energy under the assumption of a fixed number of hyperparameters or patches in order to obtain the elements of prior source covariance matrix. This research work aims to further enhance the maximization process of MSP with regard to the free energy by considering a variable number of patches. This will lead to a better estimation of brain sources in terms of localization errors. The performance of the modified MSP with a variable number of patches is compared with the original MSP using simulated and real-time EEG data. The results show a significant improvement in terms of localization errors.     

基于全相位预处理的时域多重信号分类波达方向估计方法

低信噪比下,针对宽带短脉冲情况下频域多重信号分类(MUSIC)中噪声子空间估计不稳定问题,提出一种基于全相位预处理的时域多重信号分类波达方向(DOA)估计方法。①对线列阵接收数据进行分组处理;②按搜索角度对各组数据进行相移预处理,并对各组数据预处理结果进行相加,得到一组新数据;③对线列阵接收数据在时域构建相移后的协方差矩阵,在更短数据长度下,稳定实现噪声子空间估计,并依据估计出的噪声子空间含有的正交特性,通过单位矩阵加法器得到相应空间谱估计值,实现波达方向估计。数值仿真和实测数据处理结果表明,相比频域MUSIC方法,该方法有效提高了线列阵接收数据协方差矩阵中信号含有量和信噪比,能够在更短数据长度情况下实现对噪声子空间的稳定估计,具有较好的稳定性和检测性能,提高了MUSIC方法在实际波达方向估计中的鲁棒性。     

Adaptive fractional‐order blood glucose regulator based on high‐order sliding mode observer

Type I diabetes is described by the destruction of the insulin‐producing beta‐cells in the pancreas. Hence, exogenous insulin administration is necessary for Type I diabetes patients. In this study, to estimate the states that are not directly available from the Bergman minimal model a high‐order sliding mode observer is proposed. Then fractional calculus is combined with sliding mode control (SMC) for blood glucose regulation to create more robustness performance and make more degree of freedom and flexibility for the proposed method. Then an adaptive fractional‐order SMC is proposed. The adaptive SMC protect controller against disturbance and uncertainties while the fractional calculus provides robust performance. Numerical simulation verifies that the proposed controllers have better performance in the presence of disturbance and uncertainties without chattering.Inspec keywords: variable structure systems, biochemistry, blood, robust control, medical control systems, observers, sugar, diseases, calculus, adaptive control, cellular biophysicsOther keywords: fractional‐order SMC, adaptive SMC, fractional calculus, robust performance, adaptive fractional‐order blood glucose regulator, insulin‐producing beta‐cells, exogenous insulin administration, diabetes patients, Bergman minimal model, mode control, blood glucose regulation, pancreas, type I diabetes, state estimation, high‐order sliding mode observer, sliding mode control, degree of freedom, numerical simulation     

AN APPROXIMATELY MINIMUM VARIANCE JACKKNIFE

Jackknifing is a nonparametric method of reducing bias in estimation procedures. The reduced-bias jackknife estimate is not, in general, a minimum variance (MV)estimate. The generalized jackknife is extended to allow the computation of jackknife estimates that are reduced in variance as compared with the usual jackknife estimate. The extended method is applicable in situations where there is information available on the covariance function of the given data set. This improved estimation procedure will then produce an approximately MV, reduced-bias estimate for any nonlinear function of the data. For linear combinations of the data, it is shown that the estimator reduces, as a special case, to an exactly MV, unbiased estimator.     

Maximum likelihood estimates of mean and variance of occupation radiation doses subjected to minimum detection levels

Data collection and its analysis in the field of nuclear safety is an important task in the sense that it powers the improvement of safety as well as reliability of the plant. Thus, occupational exposure data analysis is presented to measure the safety or reliability of radiation protection of a given facility. It also is required as a basic input in making decisions on radiation protection regulations and recommendations. A common practice in radiation protection is to record a zero for observation below minimum detection limit (MDL) doses, which leads to an underestimation of true doses and overestimation of the dose-response relationship. Exposure data (both external and internal) are collected by monitoring each individual and this kind of monitoring generally is graded as low-level monitoring. So, in such low-level monitoring, the occurrence of exposure below MDL invites statistical complications for estimating mean and variance because the data are generally censored, i.e observations below MDL are marked. In Type I censoring, the point of censoring (e.g. the detection limit) is 'fixed' a priori for all observations and the number of the censored observations varies. In Type II censoring, the number of censored observations is fixed a priori, and the point of censoring vary. The methodology generally followed in estimating mean and variance with these censored data was the replacement of missing dose by half the MDL. In this paper, authors have used the maximum likelihood estimation (MLE) approach for the estimation of mean and standard deviation. A computer code BDLCENSOR has been developed in which all these MLE-based advanced algorithms are implemented. In addition to the MLE-based method, an expectation maximisation algorithm has also been implemented. The code is written using Visual BASIC 6.0. The paper describes the details of the algorithms adopted for handling such censored data to estimate bias free mean and standard deviation.     

Three-dimensional reconstruction of the statistics of heterogeneous objects from a collection of one projection image of each object

An estimation problem for statistical reconstruction of heterogeneous three-dimensional objects from two-dimensional tomographic data (single-particle cryoelectron microscope images) is posed as the problem of estimating class probabilities, means, and covariances for a Gaussian mixture where both the mean and covariance are stochastically structured. Both discrete (i.e., classes) and continuous heterogeneity is included. A maximum likelihood solution computed by a generalized expectation-maximization algorithm is presented and demonstrated on experimental images of Flock House Virus.     

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.