Improve Estimation Performance Research Articles

Sparse, Group-Sparse, and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems

Sparse, group-sparse and online channel estimation is conceived for millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We exploit the angular sparsity of the mmWave channel impulse response (CIR) to achieve improved estimation performance. First a sparse Bayesian learning (SBL)-based technique is developed for the estimation of each individual subcarrier's quasi-static channel, which leads to an improved performance versus complexity trade-off in comparison to conventional channel estimation. Then a novel group-sparse Bayesian learning (G-SBL) scheme is conceived for reducing the channel estimation mean square error (MSE). The salient aspect of our G-SBL technique is that it exploits the frequency-domain (FD) correlation of the channel's frequency response (CFR), while transmitting pilots on only a few subcarriers, thus it has a reduced pilot overhead. A low complexity (LC) version of G-SBL, termed LCG-SBL, is also developed that reduces the computational cost of the G-SBL significantly. Subsequently, an online G-SBL (O-SBL) variant is designed for the estimation of doubly-selective mmWave MIMO OFDM channels, which has low processing delay and exploits temporal correlation as well. This is followed by the design of a hybrid transmit precoder and receive combiner, which can operate directly on the estimated beamspace domain CFRs, together with a limited channel state information (CSI) feedback. Our simulation results confirms the accuracy of the analysis.

A partitioning strategy for improved state estimation performance in ill-conditioned power systems with hybrid measurement set

Considering the increase in power system size and the number of PMUs, the utilization of a computationally efficient yet accurate static state estimator is crucial. The fast-decoupled WLS estimator (FD-WLS) is the most common method employed in industrial applications, thanks to its computational efficiency and ease of implementation. However, it is known that FD-WLS fails in the presence of an ill-conditioned power system, e.g., a power system with high R/X ratio and large difference between measurement error variances. This paper proposes a partitioning strategy for ill-conditioned power systems. The proposed method forms two types of sub-systems, such that one can apply FD-WLS in the type-1 sub-systems, and conventional formulation of WLS should be applied in the type-2 sub-systems. The utilization of the proposed method enables a computational performance comparable to that of FD-WLS and an accuracy comparable to that of the centralized solution.

Effects of advice on experienced-based learning in adolescents and adults

Recent studies that compared effects of pre-learning advice on experience-based learning in adolescents and adults have yielded mixed results. Previous studies on this topic used choice tasks in which age-related differences in advice-related learning bias and exploratory choice behavior are difficult to dissociate. Moreover, these studies did not examine whether effects of advice depend on working memory load. In this preregistered study (in adolescents [13–15 years old] and adults [18–31 years old]), we addressed these issues by factorially combining advice and working memory load manipulations in an estimation task that does not require choices and hence eliminates the influence of known age-related differences in exploration. We found that advice guided participants’ initial estimates in both age groups. When advice was correct, this improved estimation performance, especially in adolescents when working memory load was high. When advice was incorrect, it had a longer-lasting effect on adolescents’ performance than on adults’ performance. In contrast to previous findings in choice tasks, we found no evidence that advice biased learning in either age group. Taken together, our results suggest that learning in an estimation task improves between adolescence and adulthood but that the effects of advice on learning do not differ substantially between adolescents and adults.

Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland

Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, and per capita GDP) on the Chinese mainland, we established a geographically weighted regression (GWR) model to analyze their impacts in different seasons and provinces. The GWR model successfully describes the spatial changes of the influence of potential risks, and shows greatly improved estimation performance compared with the ordinary linear regression (OLR) method. Our findings help to understand the seasonally and spatially relevant effects of natural environmental and socioeconomic factors on the I-HFMD, and can provide information to be used to develop effective prevention strategies against HFMD at different locations and in different seasons.

Improved inference for areal unit count data using graph-based optimisation

Spatio-temporal count data relating to a set of non-overlapping areal units are prevalent in many fields, including epidemiology and social science. The spatial autocorrelation inherent in these data is typically modelled by a set of random effects that are assigned a conditional autoregressive prior distribution, which is a special case of a Gaussian Markov random field. The autocorrelation structure implied by this model depends on a binary neighbourhood matrix, where two random effects are assumed to be partially autocorrelated if their areal units share a common border, and are conditionally independent otherwise. This paper proposes a novel graph-based optimisation algorithm for estimating either a static or a temporally varying neighbourhood matrix for the data that better represents its spatial correlation structure, by viewing the areal units as the vertices of a graph and the neighbour relations as the set of edges. The improved estimation performance of our methodology compared to the commonly used border sharing rule is evidenced by simulation, before the method is applied to a new respiratory disease surveillance study in Scotland between 2011 and 2017.

Semi-supervised support vector regression based on data similarity and its application to rock-mechanics parameters estimation

Rock-mechanics parameters such as Young’s modulus and Poisson’s ratio are critical to geomechanical analysis and resource exploration. Because these parameters come from laboratory measurement, they present some characteristics such as insufficient samples and contamination of outliers. In this paper, a novel semi-supervised support vector machine soft sensor is devised considering the characteristics of the parameters. First, it takes into account data similarity and selects labeled data set that are most similar to the continuous unlabeled data set at each iteration to improve estimation performance. Meanwhile, an outlier deletion algorithm is developed for a better similarity comparison. After that, a semi-supervised approach is presented for the estimation of rock-mechanics parameters, it can leverage continuous unlabeled data to train the model dynamically. Finally, the verification of our method is carried out on data set from UCI (University of California, Irvine) and several drilling sites. The results demonstrate that our method outperforms eight well-known methods in estimation accuracy.

Multimodal super-resolved q-space deep learning.

Super-resolvedq-space deep learning(SR-q-DL) has been developed to estimate high-resolution(HR) tissue microstructure maps from low-quality diffusion magnetic resonance imaging(dMRI) scans acquired with a reduced number of diffusion gradients and low spatial resolution, where deep networks are designed for the estimation. However, existing methods do not exploit HR information from other modalities, which are generally acquired together with dMRI and could provide additional useful information for HR tissue microstructure estimation. In this work, we extend SR-q-DL and propose multimodal SR-q-DL, where information in low-resolution(LR) dMRI is combined with HR information from another modality for HR tissue microstructure estimation. Because the HR modality may not be as sensitive to tissue microstructure as dMRI, direct concatenation of multimodal information does not necessarily lead to improved estimation performance. Since existing deep networks for HR tissue microstructure estimation are patch-based and use redundant information in the spatial domain to enhance the spatial resolution, the HR information in the other modality could inform the deep networks about what input voxels are relevant for the computation of tissue microstructure. Thus, we propose to incorporate the HR information from the HR modality by designing an attention module that guides the computation of HR tissue microstructure from LR dMRI. Specifically, the attention module is integrated with the patch-based SR-q-DL framework that exploits the sparsity of diffusion signals. The sparse representation of the LR diffusion signals in the input patch is first computed with a network component that unrolls an iterative process for sparse reconstruction. Then, the proposed attention module computes a relevance map from the HR modality with sequential convolutional layers. The relevance map indicates the relevance of the LR sparse representation at each voxel for computing the patch of HR tissue microstructure. The relevance is applied to the LR sparse representation with voxelwise multiplication, and the weighted LR sparse representation is used to compute HR tissue microstructure with another network component that allows resolution enhancement. All weights in the proposed network for multimodal SR-q-DL are jointly learned and the estimation is end-to-end. To evaluate the proposed method, we performed experiments on brain dMRI scans together with images of additional HR modalities. In the experiments, the proposed method was applied to the estimation of tissue microstructure measures for different datasets and advanced biophysical models, where the benefit of incorporating multimodal information using the proposed method is shown.

Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model

Estimating accurately airborne pollutant emissions source information (source strength and location) is important for achieving effective air pollution management or adequate emergency responses to accidents. Inversion method is one of the useful tools to identify the source parameters. The atmospheric dispersion scheme has been proven to be the key to determining the source inversion performance by influencing the accuracy of the dispersion models. Modifying the atmospheric dispersion scheme is an important potential method to improve the inversion performance, but this has not been studied previously. To fill this gap, a novel approach for parameter sensitivity analysis combined with an optimization method was proposed to improve the source inversion performance by optimizing empirical scheme. The dispersion coefficients σy and σz of the typical BRIGGS scheme under different atmospheric dispersion conditions were optimized and used for air pollutant dispersion and source inversion. The results showed that the prediction performance of the air pollutant concentrations was greatly improved with statistical indices |FB| and NMSE decreased by 0.22 and 2.07, respectively; FAC2 and R increased by 0.10, and 0.08, respectively. For source inversion, the results of the significance analysis suggested that the accuracy in the source strength and location parameter (x0) were both significantly improved by ∼271% (relative deviation reduced from 60.0% to 16.2%) and ∼121% (absolute deviation reduced from 27.6 to 12.5 m). The improvement of source strength inversion accuracy was more significant under unstable atmospheric conditions (stability class A, B, and C); the mean absolute relative deviation was reduced by 97.5%. These results can help to obtain more accurate source information and to provide reliable reference for air pollution managements or emergency response to accidents. This study provides a novel and versatile approach to improve estimation performance of pollutant emission sources and enhances our understanding of source inversion.

Generalized parallel coprime array for two-dimensional DOA estimation: A perspective from maximizing degree of freedom

Parallel arrays with coprime subarrays have shown its potential advantages for two dimensional direction of arrival (DOA) estimation. In this paper, by introducing two flexible coprime factors to enlarge the inter-element spacing of parallel uniform subarrays, we propose a generalized parallel coprime array (GPCA) geometry. The proposed geometry enjoys flexible array layouts by the coprime factors and enables to extend the array aperture to achieve great improvement of estimation performance. Meanwhile, we verify that GPCA always can obtain M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> degrees of freedom (DOFs) in co-array domain via 2M sensors after optimization, which outperforms sparse parallel array geometries, such as parallel coprime array (PCA) and parallel augmented coprime array (PACA), and is the same as parallel nested array (PNA) with extended aperture. The superiority of GPCA geometry has been proved by numerical simulations with sparse representation methods.

Mathematical Analysis of Line Intersection and Shortest Distance Algorithms

The time of arrival (TOA) trilateration is one of the representative location detection technologies (LDT) that determines the true location of a mobile station (MS) using a unique intersection point of three circles based on three radii corresponding to distances between MS and base stations (BSs) and center coordinates of BSs. Since the distance between MS and BS is estimated by using the number of time delays, three circles based on the estimated radii are generally increased and they may not meet at a single point, resulting in the location estimation error. In order to compensate this estimation error and to improve estimation performance, we present two advanced TOA trilateration localization algorithms with detail mathematical expressions. The considered algorithms are the shortest distance algorithm, which calculates an average of three interior intersection points among an entire six intersection points from three intersecting circles, and the line intersection algorithm, which calculates an intersection point of three lines connecting two intersection points of two circles among the three circles, as the estimated location of the MS. In this paper, we present both algorithms with detailed mathematical expressions. The computer simulation results are provided to compare the location estimation performance of both algorithms. In addition, in this paper, mathematical analysis is provided to indicate the relation between the line intersection algorithm and the shortest distance algorithm. In this analysis, we verify that line equations based on the intersection points obtained from the shortest distance algorithm are identical to those obtained from the line intersection algorithm.

WITHDRAWN: Iterative SIC algorithm based detection for multiuser MIMO SC-FDMA uplink system

Abstract In this article, an iterative successive interference cancellation (SIC) method is investigated for uplink multiple input multiple output (MIMO) single carrier frequency division multiple access (SC-FDMA). The real challenge of wireless communication is reliable symbol detection in MIMO SC-FDMA system. An iterative SIC detection method is a prominent technique which acquires low complexity and near -optimal performance for MIMO systems. MIMO SC-FDMA system with renowned multiuser linear data estimation methods are minimum mean square error (MMSE) and zero forcing (ZF) which are frequently employed in single-input single- output detector scheme owing to its affordable estimation and noteworthy performance improvement. Nevertheless, this technique performs matrix inverse calculation whose matrix size related to set of subcarriers utilized in the scheme, generally, it has around thousands. In realistic scenario, matrix inversion operations turn to very high the computational complexity. Therefore, in this contribution proposes an iterative SIC algorithm which achieves near optimal performance with lower complexity. The performance of linear receiver scheme and iterative SIC algorithm is simulated for diverse antennas configuration in MIMO SC-FDMA systems. As a results illustrate that iterative SIC algorithm outperforms conventional linear detection techniques. The analysis of the new detection approach observes good performance and the reduced complexity detection in MIMO SC-FDMA systems.

Intelligent State Estimation for Continuous Fermenters Using Variational Bayesian Learning

Despite rapid sensor technology developments, monitoring a biological process using regular sensor measurements is challenging, making the process very difficult to characterize. Designing an optimal estimator is an attractive alternative to soft-sensing for such complicated hybrid systems. In this article, the variational Bayesian learning algorithms are proposed to estimate the continuous fermenters' actual states. Special attention is given to the random transition probability matrix (TPM), which is a prerequisite to improving estimation performance. Under the assumption of a time-invariant but random TPM, the Dirichlet distribution is utilized to specify the property of TPM. We then estimate it together with the system state and modal state to approximate the conditional posterior joint distribution. Testing the proposed algorithms using the fermenter model shows that the variational Bayesian learning algorithm can satisfactorily estimate conditions and track TPM in high accuracy.

DOA estimation of coherently distributed sources in massive MIMO systems with unknown mutual coupling

In this paper, a direction of arrival (DOA) estimation algorithm for coherent distributed (CD) sources considering unknown mutual coupling in massive MIMO systems is proposed. By utilizing an enhanced sample covariance matrix (SCM) that matches well with massive MIMO systems, the proposed algorithm constructs a perturbed model and further exploits the sparse total east squares (STLS) technique to achieve DOA and angular spread estimation. With the DOA and angular spread estimates, the mutual coupling coefficients are given by solving the constrained minimization problem. Compared with the other state-of-the-art algorithms, the proposed algorithm not only can provide improved DOA estimation performance, but also can conquer the deficient scenario in a much easier way. Theoretical performance analysis and numerical examples validate the effectiveness of the proposed algorithm.

One-Step Asynchronous Data Fusion DLMS Algorithm

In recent years distributed estimation has attracted much attention. In traditional distributed algorithms, each node performs data fusion over synchronous data, which causes lots of time consumptions in the actual situations and estimation performance degradation. To deal with this problem, we propose a new one-step asynchronous data fusion strategy in distributed estimation algorithms. Moreover, the proposed algorithms with or without measurement data sharing are studied to provide different asynchronous cooperation strategies. In particular, the convergence behavior of the proposed asynchronous fusion algorithms is analyzed, and why asynchronous fusion can improve estimation performance and reduce time consumptions are also analyzed. The effectiveness of the proposed algorithms is demonstrated through some illustrative examples. Simulation results show that the proposed algorithms considerably outperform the traditional DLMS algorithms and LMS algorithm.

Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning

Online battery capacity estimation is a critical task for battery management system to maintain the battery performance and cycling life in electric vehicles and grid energy storage applications. Convolutional Neural Networks, which have shown great potentials in battery capacity estimation, have thousands of parameters to be optimized and demand a substantial number of battery aging data for training. However, these parameters require massive memory storage while collecting a large volume of aging data is time-consuming and costly in real-world applications. To tackle these challenges, this paper proposes a novel framework incorporating the concepts of transfer learning and network pruning to build compact Convolutional Neural Network models on a relatively small dataset with improved estimation performance. First, through the transfer learning technique, the Convolutional Neural Network model pre-trained on a large battery dataset is transferred to a small dataset of the targeted battery to improve the estimation accuracy. Then a contribution-based neuron selection method is proposed to prune the transferred model using a fast recursive algorithm, which reduces the size and computational complexity of the model while maintaining its performance. The proposed model is capable of achieving fast online capacity estimation at any time, and its effectiveness is verified on a target dataset collected from four Lithium iron phosphate battery cells, and the performance is compared with other Convolutional Neural Network models. The test results confirm that the proposed model outperforms other models in terms of accuracy and computational efficiency, achieving up to 68.34% model size reduction and 80.97% computation savings.

Complex-Valued Channel Attention and Application in Ego-Velocity Estimation With Automotive Radar

Attention mechanisms have been widely integrated with various neural networks to boost performance. However, when an attention mechanism was applied to a radar ego-velocity estimation network, the importance of carefully handling the amplitude and phase of complex-valued tensor was revealed. Therefore, in this study, we present a self-attention mechanism designed to handle complex-valued tensors in order to capture the rich contextual relationships implied within amplitude and phase. To exploit the advantages of complex-valued attention (CA), we evaluated its impact while performing ego-velocity estimation tasks based on radar data, whose amplitude and phase are related to the electromagnetic scattering of the target being observed. Radars are suitable sensors for such tasks as they are capable of long-range detection and instantaneous velocity measurement under variable weather and lighting conditions. In particular, we coupled our CA module with complex-valued neural networks, known to be particularly powerful for handling wave phenomena. The proposed method exhibits robust estimation performance, regardless of whether the Doppler ambiguity problem occurs and eliminates the dependence on preprocessing stages, including target detection and static target indication. Furthermore, it achieves improved stability during training via geometrical constraint regularization, and implicitly allows velocity conversion between the sensor and vehicle frames, even if the mount information of the sensor was not provided. Finally, ablation experiments conducted on extensive real-world datasets show noticeable improvement in estimation performance.

No Free Lunch when Estimating Simulation Parameters

In this paper, we have estimated the parameters of 41 simulation models to find which of 9 estimation algorithms performs better. Unfortunately, no single algorithm was the best for all or even most of the models. Rather, five main results emerge from this research. First, each algorithm was the best estimator for at least one parameter. Second, the best estimation algorithm varied not only between models but even between parameters of the same model. Third, each estimation algorithm failed to estimate at least one identifiable parameter. Fourth, choosing the right algorithm improved estimation performance by more than quadrupling the number of model runs. Fifth, half of the agent-based models tested could not be fully identified. We therefore argue that the testing performed here should be done in other applied work and to facilitate this we would like to share the R package 'freelunch'.

Probabilistic Modeling and Inference for Sequential Space-Varying Blur Identification

The identification of parameters of spatially variant blurs given a clean image and its blurry noisy version is a challenging inverse problem of interest in many application fields, such as biological microscopy and astronomical imaging. In this paper, we consider a parametric model of the blur and introduce an 1D state-space model to describe the statistical dependence among the neighboring kernels. We apply a Bayesian approach to estimate the posterior distribution of the kernel parameters given the available data. Since this posterior is intractable for most realistic models, we propose to approximate it through a sequential Monte Carlo approach by processing all data in a sequential and efficient manner. Additionally, we propose a new sampling method to alleviate the particle degeneracy problem, which is present in approximate Bayesian filtering, particularly in challenging concentrated posterior distributions. The considered method allows us to process sequentially image patches at a reasonable computational and memory costs. Moreover, the probabilistic approach we adopt in this paper provides uncertainty quantification which is useful for image restoration. The practical experimental results illustrate the improved estimation performance of our novel approach, demonstrating also the benefits of exploiting the spatial structure the parametric blurs in the considered models.

Deep Learning-Based Channel Estimation for Massive-MIMO With Mixed-Resolution ADCs and Low-Resolution Information Utilization

In this paper, we propose two deep-learning based uplink channel estimation approaches that can utilize not only high-resolution-ADC-quantized but also low-resolution-ADC-quantized received pilot signals to improve estimation performance for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems. In each approach, low-resolution-ADC-quantized received pilot signals are utilized with one of three different schemes, i.e., High-resolution quantized pilot + All low-resolution quantized pilot(High + All), High-resolution quantized pilot + Argument of low-resolution quantized pilot (High + Arg) or High-resolution quantized pilot + Modulus of low-resolution quantized pilot (High + Mod). All three schemes include the intact quantized pilot signals at high-resolution antennas, but the quantized pilot signals at low-resolution ADCs are exploited differently in each scheme. Modified selective-input prediction deep neural network (Modified SIP-DNN) is developed to predict more realistic channels and test the effectiveness of the utilization scheme. To achieve further performance improvement, a deep neural network (DNN) based two-stage network is proposed where the recovering DNN (RC-DNN) in the first stage forms a coarse estimation for channels at antennas with low-resolution ADCs and the refining DNN (Ref-DNN) in the second stage outputs a refined estimation for channels at all antennas. Simulation results show that our proposed approaches outperform state-of-the-art channel estimation method especially when most antennas are equipped with low-resolution ADCs.

A quantified approach of predicting suitability of using the Unscented Kalman Filter in a non-linear application

A mathematical framework to predict the Unscented Kalman Filter (UKF) performance improvement relative to the Extended Kalman Filter (EKF) using a quantitative measure of non-linearity is presented. It is also shown that the range of performance improvement the UKF can attain, for a given minimum probability depends on the Non-linearity Indices of the corresponding system and measurement models. Three distinct non-linear estimation problems are examined to verify these relations. A launch vehicle trajectory estimation problem, a satellite orbit estimation problem and a re-entry vehicle position estimation problem are examined to verify these relations. Using these relations, a procedure is suggested to predict the estimation performance improvement offered by the UKF relative to the EKF for a given non-linear system and measurement without designing, implementing and tuning the two Kalman Filters.