HOSVD based multidimensional parameter estimation for massive MIMO system from incomplete channel measurements

The dominant multipath components for massive multiple-input multiple-output systems can be described using geometry-based channel models with R-dimensional (R-D) parameters. These parameters are crucial for channel correlation acquisition, which is a prerequisite for many technical challenges. In this paper, we consider higher-order singular value decomposition based R-D channel modeling parameter estimation from incomplete measurements. Incomplete higher-order orthogonality iteration algorithm can be utilized to solve the problem, which simultaneously achieves tensor recovery and tensor decomposition. After obtaining the signal or noise subspace, the parameters of interest can be estimated by using subspace methods.     

Subspace-based parameter estimation of symmetric noncausalautoregressive signals from noisy measurements

Symmetric noncausal auto-regressive signals (SNARS) arise in several, mostly spatial, signal processing applications. We introduce a subspace fitting approach for parameter estimation of SNARS from noise-corrupted measurements. We show that the subspaces associated with a Hankel matrix built from the data covariances contain enough information to determine the signal parameters in a consistent manner. Based on this result, we propose a multiple signal classification (MUSIC)-like methodology for parameter estimation of SNARS. Compared with the methods previously proposed for SNARS parameter estimation, our SNARS-MUSIC approach is expected to possess a better tradeoff between computational and statistical performances     

System parameter estimation with input/output noisy data andmissing measurements

An investigation is undertaken to examine the parameter estimation problem of linear systems when some of the measurements are unavailable (i.e., missing data) and the probability of occurrence of missing data is unknown a priori. The system input and output data are also assumed to be corrupted by measurement noise, and the knowledge of the noise distribution is unknown. Under the unknown noise distribution and missing measurements, a consistent parameter estimation algorithm [which is based on an l_p norm iterative estimation algorithm-iteratively reweighted least squares (IRLS)] is proposed to estimate the system parameters. We show that if the probability of missing measurement is less than one half, the parameter estimates via the proposed estimation algorithm will converge to the true parameters as the number of data tends to infinity. Finally, several simulation results are presented to illustrate the performance of the proposed l _p norm iterative estimation algorithm. Simulation results indicate that under input/output missing data and noise environment, the proposed parameter estimation algorithm is an efficient approach toward the system parameter estimation problem     

Model-based estimation of wind fields over the ocean from windscatterometer measurements. II. Model parameter estimation

For pt.I see ibid., vol.28, no.3, p.349-360 (1990). The feasibility of a model-based approach to wind field estimation is discussed. In this approach the parametric model for near-surface mesoscale wind fields developed in pt.I is used. The parameters of the model are estimated from the wind scatterometer measurements; the wind field estimate is then computed from the estimated model parameters. Unlike the traditional pointwise approach, this approach takes advantage of the inherent correlation in the winds at different sample points to estimate the wind field more accurately and resolve directional ambiguity. The accuracies of wind field estimates obtained using the traditional pointwise estimation scheme and the model-based approach using simulated scatterometer measurements are compared     

Chirp parameter estimation from a sample covariance matrix

This paper considers the problem of estimating the bandwidth and the center frequency of a linear chirp signal. The nonstationarity property of chirp signals implies that the signal has high rank and reduces the applicability of subspace-based algorithms significantly. However, the special structure of the sample covariance matrix invites the use of regular frequency estimation algorithms. Herein, we show how subspace-type algorithms may be modified to provide accurate signal parameter estimates for linear chirp signals at reasonable complexity. The root-MUSIC algorithm is used as an example     

Maximum-likelihood versus maximum a posteriori parameter estimation of physiological system models: the C-peptide impulse response case study

Maximum-likelihood (ML), also given its connection to least-squares (LS), is widely adopted in parameter estimation of physiological system models, i.e., assigning numerical values to the unknown model parameters from the experimental data. A more sophisticated but less used approach is maximum a posteriori (MAP) estimation. Conceptually, while ML adopts a Fisherian approach, i.e., only experimental measurements are supplied to the estimator, MAP estimation is a Bayesian approach, i.e., a priori available statistical information on the unknown parameters is also exploited for their estimation. In this paper, after a brief review of the theory behind ML and MAP estimators, we compare their performance in the solution of a case study concerning the determination of the parameters of a sum of exponential model which describes the impulse response of C-peptide (CP), a key substance for reconstructing insulin secretion. The results show that MAP estimation always leads to parameter estimates with a precision (sometimes significantly) higher than that obtained through ML, at the cost of only a slightly worse fit. Thus, a three exponential model can be adopted to describe the CP impulse response model in place of the two exponential model usually identified in the literature by the ML/LS approach. Simulated case studies are also reported to evidence the importance of taking into account a priori information in a data poor situation, e.g., when a few or too noisy measurements are available. In conclusion, our results show that, when a priori information on the unknown model parameters is available, Bayes estimation can be of relevant interest, since it can significantly improve the precision of parameter estimates with respect to Fisher estimation. This may also allow the adoption of more complex models than those determinable by a Fisherian approach.     

Joint state and parameter estimation for a target-directed nonlinear dynamic system model

We present a new approach to joint state and parameter estimation for a target-directed, nonlinear dynamic system model with switching states. The model, recently proposed for representing speech dynamics, is called the hidden dynamic model (HDM). The model parameters, subject to statistical estimation, consist of the target vector and the system matrix (also called "time-constants"), as well as parameters characterizing the nonlinear mapping from the hidden state to the observation. We implement these parameters as the weights of a three-layer feedforward multilayer perceptron (MLP) network. The new estimation approach is based on the extended Kalman filter (EKF), and its performance is compared with the traditional expectation-maximization (EM) based approach. Extensive simulation results are presented using both approaches and under typical HDM speech modeling conditions. The EKF-based algorithm demonstrates superior convergence performance compared with the EM algorithm, but the former suffers from excessive computational loads when adopted for training the MLP weights. In all cases, the simulated model output converges to the given observation sequence. However, only in the case where the MLP weights or the target vector are assumed known do the time-constant parameters converge to their true values. We also show that the MLP weights never converge to their true values, thus demonstrating the many-to-one mapping property of the feedforward MLP. We conclude that, for the system to be identifiable, restrictions on the parameter space are needed.     

Interaction of specialized cardiac conduction system with antiarrhythmic drugs: a simulation study

The use of antiarrhythmic drugs is common to treat heart rhythm disorders. Computational modeling and simulation are promising tools that could be used to investigate the effects of specific drugs on cardiac electrophysiology. In this paper, we study the multiscale effects of dofetilide, a drug that blocks IKr, from cellular to organ level paying special attention to its effect on heart structures, in particular the specialized cardiac conduction system (CCS). We include a model of the CCS in a patient-specific anatomical ventricular model and study the drug effects in simulations with and without a CCS. Results confirmed the expected effects of dofetilide at cellular level, increasing the action potential duration, and at organ level, prolonging the QT segment. Notable differences are shown between models with and without the CCS on action potential duration distributions. These techniques show the importance of heart heterogeneity and the global effects of the interaction of drugs with cardiac structures.     

Dynamic HIV/AIDS parameter estimation with application to a vaccine readiness study in southern Africa

This paper proposes a procedure of parameter estimation for all parameters of the three-dimensional HIV model. The least square based procedure uses standard optimization routines to allow parameter extraction for individual patients. It is shown how additional information from outside a measurement dataset can be included in the estimation routine to increase the reliability and accuracy of parameter estimates. A dataset from 44 patients of Southern Africa is analyzed to find the set point and the time until set point for these patients together with an estimate of the model parameters with confidence intervals for the cohort. The procedure is also applied to a long-term dataset of the HIV/AIDS progression to find possible variations in parameters.     

On channel parameter estimation in a space-time bit-interleaved-coded modulation system for multipath DS-CDMA uplink with receive diversity

In this paper, we consider iterative space-time multiuser detection and channel parameter estimation in a bit-interleaved coded modulation scheme for asynchronous direct-sequence code division multiple access (DS-CDMA) transmission over frequency selective, slowly fading channels. Accurate estimation of the channel parameters is critical as it has great impact on the overall BER performance. We present an iterative space-time multiuser (STMU) turbo detection and estimation scheme, based on space alternating generalized expectation maximization (SAGE) algorithm. This algorithm operates on the coded symbols by exchanging soft information between the detector and the estimator. We show through computer simulations that the proposed low complexity STMU receiver considerably outperforms conventional estimation schemes and achieves excellent performance, both in terms of BER and estimation error variance. Finally, we will consider different mapping strategies and investigate their impact on the performance and complexity of the estimator.     

线性调频信号快速检测与参数估计算法研究

讨论一种基于RAT的LFM信号快速检测算法。根据信号模糊函数的能量分布特性,首先采用二分法快速逼近信号能量中心分布区域,然后进行小范围高精度搜索,以精确估计其调频斜率。仿真结果表明,该算法在保持有效检测性能的条件下可大大降低运算量,显著提高对LFM信号的实时检测处理速度。     

利用C API实现基于RTX实时仿真系统的在线调参

介绍了基于Matlab/RTW(Real-time Workshop)和RTX(Real-time extension)构建实时仿真系统的方法;针对基于RTX的实时仿真系统不能直接进行在线调参的不足,提出了一种利用C API(C文件应用程序接口)实现在线调参的方法。经过实验证明,此仿真系统不仅具有很强的实时性,并且拥有良好的人机交互能力;另外,在线调参功能的实现使仿真试验的效率得到了大大的提高,而且还可以作为一种故障注入方法来考察模型的容错能力,是基于RTX实时仿真系统的一大改良。     

Risk estimation for nonparametric discrimination and estimation rules: A simulation study (Corresp.)

The designer of a nonparametric discrimination or estimation procedure is almost always interested in the conditional risk of his procedure, or nde, conditioned on the available data. Unfortunately,L_{n}, the risk conditioned on a data set containingnobservations, cannot be computed without exact knowledge of the underlying probability distribution functions. Since such knowledge is unavailable, the designer must be content with estimates ofL_{n}. Two such estimates are the deleted estimate,L_{n}^{D}, and the holdout estimate,L_{n}^{H}. This paper presents the results of an experimental study of these two estimates and compares these results with some recently obtained distribution.free theoretical results. Among other things, the experimental data indicates that fork-nearest neighbor rules in{bf R^{1}}with several examples of underlying distributions,P{|L_{n}-L^{D}_{n}| geq 2e^{-2 epsilon^{2}n}mbox{P{|}L_{n}-L^{H}_{n}| geq epsilon} 2e^{-2 epsilon^{2} sqrt{n}.}     

On-line spectral estimation of nonstationary time series based onAR model parameter estimation and order selection with a forgettingfactor

A new method for on-line spectral estimation of nonstationary time series via autoregressive (AR) model construction is proposed. The method consists of on-line parameter estimation based on the recursive least squares ladder estimation algorithm with a forgetting factor and on-line order determination based on AIC with some modifications. The effectiveness of the proposed method is demonstrated by computer simulation study and applying to the actual data of electroencephalogram (EEG)     

Nonlinear parameter estimation by linear association: applicationto a five-parameter passive neuron model

Linear associative memories (LAM) have been intensely used in the areas of pattern recognition and parallel processing for the past two decades. Application of LAM to nonlinear parameter estimation, however, has only been recently attempted. The process consists in converting the nonlinear function in the parameters into a set of linear algebraic equations. The nature of the linearized system and the factors influencing the accuracy of the parameter estimates have not yet been fully investigated. Here, LAM is applied to a nonlinear five-parameter model of the neuron. Ill-conditioning, which is often exhibited in LAM, is treated with the method of regularization as well as by the singular value decomposition (SVD). Simulation results indicate that the parameters estimated by LAM exhibit a remarkable robustness against additive white noise in comparison with the classical gradient optimization technique. Moreover, it is shown that regularization can be superior to SVD under certain conditions. The authors' results suggest that LAM can be used both as a noise reduction technique and as a stand-alone nonlinear parameter estimation algorithm. The comparison between LAM and a gradient technique show that, for this estimation problem, the LAM method can give more reliable estimates. Further improvements in estimation quality may still be achieved by the use of other forms of regularizing functions     

Theoretical investigation of the estimation of relative regional neuroreceptor concentration from a single SPECT or PET image

The validity of estimating changes in regional neuroreceptor concentration based upon a single ECT (emission computed tomography) image is examined to determine whether an image can be acquired at a time when changes in the observed regional radioactivity are much more sensitive to changes in receptor concentration than to changes in radioligand delivery. These sensitivities are defined as the normalized partial derivatives of the regional radioactivity signal (S) with respect to the total receptor (R(t)) and with respect to radioligand delivery (k(1)). Using computer simulations, it is found that ( partial differentials/ partial differentialR(t))/( S/R(t)) can be less than unity (receptor hyposensitivity), approximately equal to unity, or significantly greater than unity (receptor hypersensitivity). It is possible to find classes of parameter sets under which the receptor sensitivity is close to unity and the delivery sensitivity is of much lower magnitude. The results indicate that if the parameters for a given radioligand-neuroreceptor system can be established as belonging to one of these classes, then changes in regional neuroreceptor concentration can be estimated based upon a single ECT image.     

Direct least-squares estimation of spatiotemporal distributions from dynamic SPECT projections using a spatial segmentation and temporal B-splines

Artifacts can result when reconstructing a dynamic image sequence from inconsistent, as well as insufficient and truncated, cone beam single photon emission computed tomography (SPECT) projection data acquired by a slowly rotating gantry. The artifacts can lead to biases in kinetic model parameters estimated from time-activity curves generated by overlaying volumes of interest on the images. However, the biases in time-activity curve estimates and subsequent kinetic parameter estimates can be reduced significantly by first modeling the spatial and temporal distribution of the radiopharmaceutical throughout the projected field of view, and then estimating the time-activity curves directly from the projections. This approach is potentially useful for clinical SPECT studies involving slowly rotating gantries, particularly those using a single-detector system or body contouring orbits with a multidetector system. We have implemented computationally efficient methods for fully four-dimensional (4-D) direct estimation of spatiotemporal distributions from dynamic SPECT projection data. Temporal B-splines providing various orders of temporal continuity, as well as various time samplings, were used to model the time-activity curves for segmented blood pool and tissue volumes in simulated cone beam and parallel beam cardiac data acquisitions. Least-squares estimates of time-activity curves were obtained quickly using a workstation. Given faithful spatial modeling, accurate curve estimates were obtained using cubic, quadratic, or linear B-splines and a relatively rapid time sampling during initial tracer uptake. From these curves, kinetic parameters were estimated accurately for noiseless data and with some bias for noisy data. A preliminary study of spatial segmentation errors showed that spatial model mismatch adversely affected quantitative accuracy, but also resulted in structured errors (projected model versus raw data) that were easily detected in our simulations. This suggests iterative refinement of the spatial model to reduce structured errors as an area of future research.     

A simple monitor of a system with sudden parameter changes

Performance of a monitor using a geometric moving-average (GMA) of observations of a system subject to sudden changes is related to parameters describing some aspects of the changes and to parameters of the monitor. Monitor parameters to optimize performance are determined numerically for an example of practical interest.     

Application of neural algorithms for a real-time estimation of ozone profiles from GOME measurements

The thermal structure of trace gases, their distribution in the atmosphere, and their circulation mechanisms result from a complex interplay between radiative, physical, and dynamical processes. Neural-network algorithms can be a useful tool to face such complexities in retrieval operations. In this paper, their potentialities have been exploited to design real-time procedures for the estimation of vertical profiles of ozone concentration from spectral radiances measured by GOME, the first instrument of the European Space Agency capable of monitoring global distribution of ozone and other trace gases.