Journal articles on the topic 'Recursive window estimation'
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Yu, Lei, Yong-li Zhang, Meng-di Yuan, Rui-qing Liu, and Qi Zhang. "Recursive Method in Modal Parameter Identification of Aerospace Structures under Non-Gaussian Noise." Shock and Vibration 2020 (June 5, 2020): 1–12. http://dx.doi.org/10.1155/2020/2946709.
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Operational modal parameter identification is a tough problem in aerospace engineering due to the complex mechanics environment, various noises, and limited computational resources. In this paper, a novel, recursive, robust, and high-efficiency modal parameter identification approach is proposed for this issue. The kernelized time-dependent autoregressive moving average (TARMA) model is adopted to model the nonstationary responses, a recursive estimator is established based on the maximum correntropy criterion, and sliding-window technique is applied to fix the computational complexity, which ensures the approach its estimation accuracy, robustness, and high efficiency. Finally, steps of the identification procedure and model selection are presented. An experimental scheme is proposed for validation, and the proposed approach is comparatively assessed against the classical recursive pseudo-linear regression TARMA method via Monte Carole tests of a time-varying experimental system. The results of the comparative study demonstrate that the proposed method achieves similar estimation accuracy and higher computation efficiency under the Gaussian environment. Moreover, a superior estimation accuracy and enhanced robustness are rendered under additive non-Gaussian impulsive noise.
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Gao, Wei, Jingchun Li, Guangtao Zhou, and Qian Li. "Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems." Journal of Navigation 68, no. 1 (August 15, 2014): 142–61. http://dx.doi.org/10.1017/s0373463314000484.
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This paper considers the estimation of the process state and noise parameters when the statistics of the process and measurement noise are unknown or time varying in the integration system. An adaptive Kalman Filter (AKF) with a recursive noise estimator that is based on maximum a posteriori estimation and one-step smoothing filtering is proposed, and the AKF can provide accurate noise statistical parameters for the Kalman filter in real-time. An exponentially weighted fading memory method is introduced to increase the weights of the recent innovations when the noise statistics are time varying. Also, the innovation covariances within a moving window are averaged to correct the noise statistics estimator. Experiments on the integrated Strapdown Inertial Navigation System (SINS)/ Doppler Velocity Log (DVL) system show that the proposed AKF improves the estimation accuracy effectively and the AKF is robust in the presence of vigorous-manoeuvres and rough sea conditions.
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Amin, M. G. "A comparison between two measures of convergence in recursive-window based spectrum estimation." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 8 (1990): 1457–59. http://dx.doi.org/10.1109/29.57580.
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Gupta, Rangan, and Christian Pierdzioch. "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers." Energies 14, no. 14 (July 10, 2021): 4173. http://dx.doi.org/10.3390/en14144173.
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We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.
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Wang, Song. "Windowed Least Square Algorithm Based PMSM Parameters Estimation." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/131268.
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Stator resistance and inductances ind-axis andq-axis of permanent magnet synchronous motors (PMSMs) are important parameters. Acquiring these accurate parameters is usually the fundamental part in driving and controlling system design, to guarantee the performance of driver and controller. In this paper, we adopt a novel windowed least algorithm (WLS) to estimate the parameters with fixed value or the parameter with time varying characteristic. The simulation results indicate that the WLS algorithm has a better performance in fixed parameters estimation and parameters with time varying characteristic identification than the recursive least square (RLS) and extended Kalman filter (EKF). It is suitable for engineering realization in embedded system due to its rapidity, less system resource possession, less computation, and flexibility to adjust the window size according to the practical applications.
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SHIN, JAEHYUN, YONGMIN ZHONG, JULIAN SMITH, and CHENGFAN GU. "ADAPTIVE UNSCENTED KALMAN FILTER FOR ONLINE SOFT TISSUES CHARACTERIZATION." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740014. http://dx.doi.org/10.1142/s0219519417400140.
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Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt–Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.
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Yang, Ruo-Nan, Wei-Tao Zhang, and Shun-Tian Lou. "Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC." Wireless Communications and Mobile Computing 2020 (October 24, 2020): 1–17. http://dx.doi.org/10.1155/2020/8396930.
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In order to track the changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is prior to estimate channel impulse response adaptively. In this paper, we proposed an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weight the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of third-order tensor which consists of the weighted OFDM data symbols. To reduce the computational load, we adopt a recursive singular value decomposition method for tensor decomposition; then, the channel parameters can be estimated adaptively. Simulation results validate the effectiveness of the proposed algorithm under diverse signalling conditions.
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Zhou, Zebo, Bofeng Li, and Yunzhong Shen. "A Window-Recursive Approach for GNSS Kinematic Navigation Using Pseudorange and Doppler Measurements." Journal of Navigation 66, no. 2 (November 20, 2012): 295–313. http://dx.doi.org/10.1017/s0373463312000549.
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In kinematic Global Navigation Satellite Systems (GNSS) navigation, the Kalman Filter (KF) solution relies, to a great extent, on the quality of the dynamic model that describes the moving object's motion behaviour. However, it is rather difficult to establish a precise dynamic model that only connects the previous state and the current state, since these high-order quantities are usually unavailable in GNSS navigation receivers. To overcome such limitations, the Window-Recursive Approach (WRA) that employs the previous multiple states to predict the current one was developed in Zhou et al., (2010). Its essence is to adaptively fit the moving object's motion behaviour using the multiple historical states in a short time span. Up to now, the WRA method has been performed only using GNSS pseudorange measurements. However, in GNSS navigation fields, the strength of pseudorange observation model is usually weak due to various reasons, e.g., multi-path delay, outliers, insufficient visible satellites. As an important complementary measurement, Doppler can be used to aid Position and Velocity (PV) estimation. In this contribution, implementation of WRA will be developed using the pseudorange and Doppler measurements. Its corresponding state transition matrix is constructed based on the Newton's Forward Difference Extrapolation (NFDE) and Definite Integral (DI) methods for the efficient computation. The new implementation of WRA is evaluated using the real kinematic vehicular GNSS data with two sampling rates. The results show that: (i)aided by GNSS Doppler measurement, the new implementation of WRA significantly improves the accuracy compared with the pseudorange-only WRA.(ii)In high sampling rate, the WRA works best in the case of 2 epochs in time window, while in the low sampling rate, it obtains better solutions if more epochs involved in time window.(iii)Compared with KF with constant velocity dynamic model, the WRA demonstrates better in the self-adaptation and validity.(iv)As a benefit of WRA itself, the NFDE/DI-based state transition matrix for WRA can be previously computed offline without increasing the computation burdens.
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Duzinkiewicz, Kazimierz, and Mietek A. Brdys. "SET MEMBERSHIP ESTIMATION OF PARAMETERS AND VARIABLES IN DYNAMIC NETWORKS BY RECURSIVE ALGORITHMS WITH MOVING MEASUREMENT WINDOW." IFAC Proceedings Volumes 38, no. 1 (2005): 21–26. http://dx.doi.org/10.3182/20050703-6-cz-1902.01544.
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Yang, Ruo-Nan, Wei-Tao Zhang, and Shun-Tian Lou. "Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems." Wireless Communications and Mobile Computing 2020 (July 2, 2020): 1–9. http://dx.doi.org/10.1155/2020/2508130.
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In order to track a changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is a priority to estimate channel impulse response adaptively. In this paper, we propose an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weigh the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of a third-order tensor, which consists of the weighted OFDM data symbols. By preserving the Khatri-Rao product, we used a recursive least squares solution to update the estimated subspace at each time instant, then the channel parameters can be estimated adaptively, and the algorithm achieves superior convergence performance. Simulation results validate the effectiveness of the proposed algorithm.
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Cai, X., Y. S. Ding, and S. Y. Li. "Convergent Properties of Riccati Equation with Application to Stability Analysis of State Estimation." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/2367042.
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Since the recursive nature of Kalman filtering always results in a growing size of the optimization problem, state estimation is usually realized by use of finite-memory, receding horizon, sliding window, or “frozen” techniques, which causes difficulties on stability analysis. This paper proposes a novel method on selection of an initial covariance matrix and a horizon for the Kalman filter to make sure that a sequence of the closed-loop Kalman filters are stable as time-invariant filters at subsequent time instant. Convergent properties of Riccati Difference Equation (RDE) are first exploited. Based on these properties, sufficient conditions for stability of a sequence of Kalman filters are obtained. Compared with the existent literature, the convergent properties and the stability conditions are less conservative since they provide analytic results and are applicable to more common cases where the RDEs are not monotonic.
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Rubino, Nicola. "IN- AND OUT-OF-SAMPLE PERFORMANCE OF NONLINEAR MODELS IN INTERNATIONAL PRICE DIFFERENTIAL FORECASTING IN A COMMODITY COUNTRY FRAMEWORK." EURASIAN JOURNAL OF ECONOMICS AND FINANCE 9, no. 2 (2021): 107–27. http://dx.doi.org/10.15604/ejef.2021.09.02.004.
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This paper presents an analysis of a group of small commodity-exporting countries' price differentials relative to the US dollar. Using unrestricted self-exciting threshold autoregressive models (SETAR), we evaluate the sixteen national Consumer Price Indexes (CPI) differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is estimated through calculation of mean absolute errors measures based on the monthly rolling window and recursive forecasts, and this estimation is extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive nonlinear autoregressive model (AAR), and a simple neural network model (NNET). Our preliminary results confirm the presence of some form of nonlinearity in most of the analyzed countries. The parsimonious AR(1) model does not appear to perform any worse than any nonlinear model in the rolling sample exercise. However, in terms of a longrun equilibrium driven by purchasing power parity, its validity is undermined by the results of the recursive estimates and the outcome of the Diebold-Mariano type tests, which favor generally the Heckscher commodity points theory. As a policy advice to commodity-exporting countries, we find no apparent reason to suggest commodity export price pegging as a generalized foreign exchange policy.
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Bavdekar, Vinay A., Jagadeesan Prakash, Sachin C. Patwardhan, and Sirish L. Shah. "A Moving Window Formulation for Recursive Bayesian State Estimation of Systems with Irregularly Sampled and Variable Delays in Measurements." Industrial & Engineering Chemistry Research 53, no. 35 (August 25, 2014): 13750–63. http://dx.doi.org/10.1021/ie5009585.
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Porsani, Milton J., Bjørn Ursin, and Michelângelo G. Silva. "Dynamic estimation of reflectivity by minimum-delay seismic trace decomposition." GEOPHYSICS 78, no. 3 (May 1, 2013): V109—V117. http://dx.doi.org/10.1190/geo2012-0077.1.
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Spiking deconvolution corrects for the effect of the seismic wavelet, assumed to be minimum delay, by applying an inverse filter to the seismic trace to get an estimate of reflectivity. To compensate for propagation and absorption effects, one may use time-varying deconvolution, in which a different inverse filter is computed and applied for each output sample position. We modified this procedure by estimating a minimum-delay wavelet for each time-sample position of the seismic trace. This gives a decomposition of the seismic trace as a sum of minimum-delay wavelets, each multiplied by a reflectivity coefficient. The data vector is equal to a lower triangular wavelet matrix, with element 1 on the diagonal, multiplied by the seismic reflectivity vector. Recursive solution of this equation provides an estimate of reflectivity. The reflectivity estimation is a single-trace process that is sensitive to nonwhite noise, and it does not take into account lateral continuity of reflections. Therefore, we have developed a new data processing strategy by combining it with adaptive singular value decomposition (SVD) filtering. The SVD filtering process is applied to the data in two steps: (1) in a sliding spatial window on NMO-corrected CMP or common shot gathers (2) next, after local dip estimation and correction, on local patches in common-offset panels. After the SVD filtering, we applied the new reflectivity estimation procedure. The SVD filtering removes noise and improves lateral continuity, whereas the reflectivity estimation increases the high-frequency content in the data and improves vertical resolution. The new data processing strategy was successfully applied to land seismic data from northeast Brazil. Improvements in data quality are evident in prestack data panels, velocity analysis, and the stacked section.
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Parag, Kris V. "Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves." PLOS Computational Biology 17, no. 9 (September 7, 2021): e1009347. http://dx.doi.org/10.1371/journal.pcbi.1009347.
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We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.
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Drachal, Krzysztof. "Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R." Signals 1, no. 1 (July 6, 2020): 47–99. http://dx.doi.org/10.3390/signals1010004.
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The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.
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Cywicka, Dominika, Agnieszka Jakóbik, Jarosław Socha, Daryna Pasichnyk, and Adrian Widlak. "Modelling bark thickness for Scots pine (Pinus sylvestris L.) and common oak (Quercus robur L.) with recurrent neural networks." PLOS ONE 17, no. 11 (November 16, 2022): e0276798. http://dx.doi.org/10.1371/journal.pone.0276798.
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Variation of the bark depends on tree age, origin, geographic location, or site conditions like temperature and water availability. Most of these variables are characterized by very high variability but above of all are also affected by climate changes. This requires the construction of improved bark thickness models that take this complexity into account. We propose a new approach based on time series. We used a recurrent neural network (ANN) to build the bark thickness model and compare it with stem taper curves adjusted to predict double bark thickness. The data includes 750 felled trees from common oak and 144 Scots pine—trees representing dominant forest-forming tree species in Europe. The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built recurrent ANN and calculated bark thickness along the stem. We tested different network structures with one- and two-time window delay and three learning algorithms—Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate Gradient. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error as well as graphical analysis of observed and estimated values. The results show that recurrent ANN is a universal approach that offers the most precise estimation of bark thickness at a particular stem height. The ANN recursive model had an advantage in estimating trees that were atypical for height, as well as upper and lower parts on the stem.
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Bouhamed, Omar, Manar Amayri, and Nizar Bouguila. "Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning." Sensors 22, no. 9 (April 21, 2022): 3186. http://dx.doi.org/10.3390/s22093186.
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Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unfortunately, a major problem in that context is the difficulty to collect training data. This situation inspired us to rethink the occupancy prediction problem, proposing the use of an original principled approach based on occupancy estimation via interactive learning to collect the needed training data. Following that, the collected data, along with various features, were fed into several algorithms to predict future occupancy. This paper mainly proposes a weakly supervised occupancy prediction framework based on office sensor readings and occupancy estimations derived from an interactive learning approach. Two studies are the main emphasis of this paper. The first is the prediction of three occupancy states, referred to as discrete states: absence, presence of one occupant, and presence of more than one occupant. The purpose of the second study is to anticipate the future number of occupants, i.e., continuous states. Extensive simulations were run to demonstrate the merits of the proposed prediction framework’s performance and to validate the interactive learning-based approach’s ability to contribute to the achievement of effective occupancy prediction. The results reveal that LightGBM, a machine learning model, is a better fit for short-term predictions than known recursive neural networks when dealing with a limited dataset. For a 24 h window forecast, LightGBM improved accuracy from 38% to 50%, which is an excellent result for non-aggregated data (single office).
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Sewell, Daniel K., and Aaron Miller. "Simulation-free estimation of an individual-based SEIR model for evaluating nonpharmaceutical interventions with an application to COVID-19 in the District of Columbia." PLOS ONE 15, no. 11 (November 10, 2020): e0241949. http://dx.doi.org/10.1371/journal.pone.0241949.
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The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees. Rcode for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.
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Kim, Pyung Soo. "Finite Memory Structure Filtering and Smoothing for Target Tracking in Wireless Network Environments." Applied Sciences 9, no. 14 (July 18, 2019): 2872. http://dx.doi.org/10.3390/app9142872.
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In this paper, a state estimation problem is considered for a target tracking scheme in wireless network environments. Firstly, a unified algorithm of finite memory structure (FMS) filtering and smoothing is proposed for a discrete-time state-space model. As shown in the terminology unified, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint. Hence, the unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate using only finite measurements and inputs on the most recent window. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness, which cannot be obtained by the recursive infinite memory structure (IMS) filtering such as Kalman filter. The on-line computational complexity of the proposed unified algorithm is discussed. Secondly, as an application of the proposed unified algorithm, a target tracking scheme in wireless network environments is considered via computer simulations for moving target’s accelerations of various shapes. The proposed unified algorithm-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Due to intrinsic robustness and deadbeat properties, the proposed unified algorithm-based scheme can outperform the existing IMS filtering-based scheme when acceleration suddenly changes.
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Kennedy, Hugh L. "Digital Filter Designs for Recursive Frequency Analysis." Journal of Circuits, Systems and Computers 25, no. 02 (December 23, 2015): 1630001. http://dx.doi.org/10.1142/s0218126616300014.
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Digital filters for recursively computing the discrete Fourier transform (DFT) and estimating the frequency spectrum of sampled signals are examined, with an emphasis on magnitude-response and numerical stability. In this tutorial-style treatment, existing recursive techniques are reviewed, explained and compared within a coherent framework; some fresh insights are provided and new enhancements/modifications are proposed. It is shown that the replacement of resonators by (non-recursive) modulators in sliding DFT (SDFT) analyzers with either a finite impulse response (FIR), or an infinite impulse response (IIR), does improve performance somewhat; however, stability is not guaranteed as the cancellation of marginally stable poles by zeros is still involved. The FIR deadbeat observer is shown to be more reliable than the SDFT methods, an IIR variant is presented, and ways of fine-tuning its response are discussed. A novel technique for stabilizing IIR SDFT analyzers with a fading memory, so that all poles are inside the unit circle, is also derived. Slepian and sum-of-cosine windows are adapted to improve the frequency responses for the various FIR and IIR DFT methods.
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Xiong, Xiaoxia, Long Chen, and Jun Liang. "Vehicle Driving Risk Prediction Based on Markov Chain Model." Discrete Dynamics in Nature and Society 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/4954621.
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A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state.
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Yang, Zanru, Le Chung Tran, Farzad Safaei, Anh Tuyen Le, and Attaphongse Taparugssanagorn. "Real-Time Step Length Estimation in Indoor and Outdoor Scenarios." Sensors 22, no. 21 (November 3, 2022): 8472. http://dx.doi.org/10.3390/s22218472.
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In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is “sliding” forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.
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O'NEILL, T. J., J. H. W. PENM, and R. D. TERRELL. "THE SEQUENTIAL ESTIMATION OF SUBSET VAR WITH FORGETTING FACTOR AND INTERCEPT VARIABLE." International Journal of Theoretical and Applied Finance 07, no. 08 (December 2004): 979–95. http://dx.doi.org/10.1142/s0219024904002803.
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In this paper we propose a forward time update algorithm to recursively estimate subset vector autoregressive models (including an intercept term) with a forgetting factor, using the exact window case. The proposed recursions cover, for the first time, subset vector autoregressive models (VAR) with a forgetting factor and an intercept variable. We then present two applications. In the first application we apply the proposed estimation algorithm to the quarterly aluminium prices on the London Metal Exchange. The findings show that the proposed algorithm can improve the forecasting performance. In the second application a bivariate system investigates the relationship between the Australian's All Ordinaries Share Price Index (SPI) futures and BHP share price (BHP). The proposed algorithm also introduces the Monte Carlo Integration approach into the proposed algorithm to generate error bands for the impulse responses. These results confirm that the SPI Granger causes BHP, but not vice versa.
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Xia, Linlin, Qingyu Meng, Deru Chi, Bo Meng, and Hanrui Yang. "An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation." Sensors 19, no. 9 (April 29, 2019): 2004. http://dx.doi.org/10.3390/s19092004.
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The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.
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Moiseev, A. A. "Improved rank selection algorithm." Radio industry (Russia) 31, no. 1 (April 7, 2021): 37–44. http://dx.doi.org/10.21778/2413-9599-2021-31-1-37-44.
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Problem statement. A rank algorithm for selecting radio emission modes for operation in conditions of heterogeneity of sources and complex interference conditions, including the possible presence of mutual interference, is synthesized.Objective. The synthesis purpose is to ensure the independence of mode recognition from particular features of radio emission observation. Algorithm input is the primary signal processing result that includes such estimations as pulses durability, frequency and amplitude dynamics, and absolute variations. Primary decision statistics are formed using these values: observable signal base and relation variations of frequency and amplitude. Secondary statistics are formed based on primary ones using median and recursive or maximum and recursive smoothing. Each of the decision statistics in the multi-threshold procedure is transformed into a row of ranks, the size of which corresponds to the number of recognized modes. In aggregate, these lines form a ranking table (matrix) with colons representing recognized modes’ discrete descriptions. Fluent observation processing includes rank formation for used decision statistics. Mode recognition is performed either following a ranking table or using an additional voting procedure 2/3. An alternative approach consists of constructing the Manhattan mismatch metric of the current and reference ranks and making a decision on the criterion of the minimum mismatch metric.Results. Mode recognition performed on results of this comparison using unbalance metrics minimum criterion. Thresholds in frames of the ranking procedure are formed heuristically at ranking table formation. They are then used at fluent rank formation for observable modes. The performed numerical experiment shows that maximal and recursive filtration provides an errorless selection of all observable modes. This filtration represents the composition of maximum selection in sliding window and subsequent recursive first-order filtration. An additional advantage of this filtration is a simpler maximum selection in comparison with the median one. In perspective, it can provide increased operating speed.Practical implications. Performed consideration shows that rank selection is worthwhile at the observation of heterogeneous irradiation sources. Algorithm strength is decision simplicity in a complex situation. Additional algorithm advantage is the possibility of extending alternative irradiation modes and, hence, for more representative data sets.
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Xue, Bing, Yunbin Yuan, Han Wang, and Haitao Wang. "Evaluation of the Integrity Risk for Precise Point Positioning." Remote Sensing 14, no. 1 (December 29, 2021): 128. http://dx.doi.org/10.3390/rs14010128.
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Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning technology due to its high precision and flexibility. However, the vulnerability of PPP brings a safety risk to its application in the field of life safety, which must be evaluated quantitatively to provide integrity for PPP users. Generally, PPP solutions are processed recursively based on the extended Kalman filter (EKF) estimator, utilizing both the previous and current measurements. Therefore, the integrity risk should be qualified considering the effects of all the potential observation faults in history. However, this will cause the calculation load to explode over time, which is impractical for long-time missions. This study used the innovations in a time window to detect the faults in the measurements, quantifying the integrity risk by traversing the fault modes in the window to maintain a stable computation cost. A non-zero bias was conservatively introduced to encapsulate the effect of the faults before the window. Coping with the multiple simultaneous faults, the worst-case integrity risk was calculated to overbound the real risk in the multiple fault modes. In order to verify the proposed method, simulation and experimental tests were carried out in this study. The results showed that the fixed and hold mode adopted for ambiguity resolution is critical to an integrity risk evaluation, which can improve the observation redundancy and remove the influence of the biased predicted ambiguities on the integrity risk. Increasing the length of the window can weaken the impact of the conservative assumption on the integrity risk due to the smoothing effect of the EKF estimator. In addition, improving the accuracy of observations can also reduce the integrity risk, which indicates that establishing a refined PPP random model can improve the integrity performance.
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Hoan, Nguyen Thanh, Nguyen Van Dung, Ho Le Thu, and Hoa Thuy Quynh. "Developing land-cover driver model for estimating the intensity of surface urban heat islands using landsat 8 satellite imagery." VIETNAM JOURNAL OF EARTH SCIENCES 41, no. 3 (May 20, 2019): 201–15. http://dx.doi.org/10.15625/0866-7187/41/3/13829.
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It is of utmost importance to understand and monitor the impact of urban heat islands on ecosystems and overall human health in the context of climate change and global warming. This research was conducted in a tropical city, Hanoi, with a major objective of assessing the quantitative relationships between the composition of the main land-cover types and surface urban heat island phenomenon. In this research, we analyzed the correlation between land-cover composition, percentage coverage of the land cover types, and land surface temperature for different moving window sizes or urban land management units. Landsat 8 OLI (Operational Land Imager) satellite data was utilized for preparing land-cover composition datasets in inner Hanoi by employing the unsupervised image clustering method. High-resolution (30m) land surface temperature maps were generated for different days of the years 2016 and 2017 using Landsat 8 TIRS (Thermal Infrared Sensor) images. High correlations were observed between percentage coverage of the land-cover types and land surface temperature considering different window sizes. A new model for estimating the intensity of surface urban heat islands from Landsat 8 imagery is developed, through recursively analyzing the correlation between land-cover composition and land surface temperature at different moving window sizes. This land-cover composition-driven model could predict land surface temperature efficiently not only in the case of different window sizes but also on different days. The newly developed model in this research provides a wonderful opportunity for urban planners and designers to take measures for adjusting land surface temperature and the associated effects of surface urban heat islands by managing the land cover composition and percentage coverage of the individual land-cover types.
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Gunter, Ulrich, Irem Önder, and Egon Smeral. "Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?" Forecasting 2, no. 3 (June 29, 2020): 211–29. http://dx.doi.org/10.3390/forecast2030012.
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This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.
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Sarotte, Camille, Julien Marzat, Hélène Piet Lahanier, Marco Galeotta, and Gérard Ordonneau. "Fault Detection and Isolation with Fluid Mechanics Constraints For Cryogenic Combustion Bench Cooling Circuit." Annual Conference of the PHM Society 10, no. 1 (September 24, 2018). http://dx.doi.org/10.36001/phmconf.2018.v10i1.507.
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This paper presents the design of a Fault Detection and Isolation scheme to improve the reliability of a cryogenic engine test bench operation, focusing specifically on its cooling circuit. The proposed fault detection consists in an extended unknown input observer, a cumulative sum algorithm and an exponentially moving average chart. A dynamic parity space approach is then proposed to isolate one or two simultaneous faults in the cooling circuit. The initial system model, for each line composing the cooling circuit, is augmented with constraints based on the mass flow rate continuity and the energy conservation for the overall system. Time delays in the transients are accounted for by recursive equations over a sliding window. The method allows settling adaptive thresholds that avoid pessimistic decision about the continuation of tests while detecting and isolating faults in the transient and permanent states of the system. The model structure and the estimation method were validated on the real Mascotte test bench (ONERA/CNES) data. The fault detection and isolation scheme was validated in realistic simulations.
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Shen, Chan, and Roger Klein. "RECURSIVE DIFFERENCING FOR ESTIMATING SEMIPARAMETRIC MODELS." Econometric Theory, August 18, 2022, 1–23. http://dx.doi.org/10.1017/s0266466622000329.
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Controlling the bias is central to estimating semiparametric models. Many methods have been developed to control bias in estimating conditional expectations while maintaining a desirable variance order. However, these methods typically do not perform well at moderate sample sizes. Moreover, and perhaps related to their performance, nonoptimal windows are selected with undersmoothing needed to ensure the appropriate bias order. In this paper, we propose a recursive differencing estimator for conditional expectations. When this method is combined with a bias control targeting the derivative of the semiparametric expectation, we are able to obtain asymptotic normality under optimal windows. As suggested by the structure of the recursion, in a wide variety of triple index designs, the proposed bias control performs much better at moderate sample sizes than regular or higher-order kernels and local polynomials.
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Anjaiah, Kanche, Pradipta Kishore Dash, and Mrutyunjaya Sahani. "A new protection scheme for PV-wind based DC-ring microgrid by using modified multifractal detrended fluctuation analysis." Protection and Control of Modern Power Systems 7, no. 1 (March 8, 2022). http://dx.doi.org/10.1186/s41601-022-00232-3.
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AbstractThis paper presents fault detection, classification, and location for a PV-Wind-based DC ring microgrid in the MATLAB/SIMULINK platform. Initially, DC fault signals are collected from local measurements to examine the outcomes of the proposed system. Accurate detection is carried out for all faults, (i.e., cable and arc faults) under two cases of fault resistance and distance variation, with the assistance of primary and secondary detection techniques, i.e. second-order differential current derivative $$\left( {\frac{{d^{2} I_{3} }}{{dt^{2} }}} \right)$$ d 2 I 3 d t 2 and sliding mode window-based Pearson’s correlation coefficient. For fault classification a novel approach using modified multifractal detrended fluctuation analysis (M-MFDFA) is presented. The advantage of this method is its ability to estimate the local trends of any order polynomial function with the help of polynomial and trigonometric functions. It also doesn’t require any signal processing algorithm for decomposition resulting and this results in a reduction of computational burden. The detected fault signals are directly passed through the M-MFDFA classifier for fault type classification. To enhance the performance of the proposed classifier, statistical data is obtained from the M-MFDFA feature vectors, and the obtained data is plotted in 2-D and 3-D scatter plots for better visualization. Accurate fault distance estimation is carried out for all types of faults in the DC ring bus microgrid with the assistance of recursive least squares with a forgetting factor (FF-RLS). To verify the performance and superiority of the proposed classifier, it is compared with existing classifiers in terms of features, classification accuracy (CA), and relative computational time (RCT).
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Zhang, Jiangmin, Zengshou Dong, and Hui Shi. "Real-time remaining useful life prediction based on adaptive kernel window width density." Measurement Science and Technology, June 20, 2022. http://dx.doi.org/10.1088/1361-6501/ac7a91.
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Abstract Remaining useful life (RUL) prediction plays an important role in improving the availability and productivity of systems. To improve the accuracy of real-time remaining useful life prediction during system operation, we propose a modeling method for real-time remaining useful life prediction based on adaptive kernel window width density. Firstly, a non-parametric kernel density estimation real-time remaining useful life prediction model is proposed and a window width model with adaptive kernel window width density is established by introducing a local density factor in the window width selection. The local density of sample points is calculated by the k-nearest neighbor distance, and the kernel density estimation is performed by adaptively selecting the window width value according to the local density of sample points in the region of nonuniform distribution of sample points. As the monitoring data changes in real time, the kernel density estimates of known samples are used to recursively update the kernel density estimates of new samples. Moreover, the logarithmic transformation of random variables and space mapping are used in the establishment of the remaining useful life prediction model. The model of logarithmic kernel diffeomorphism transformation is established to solve the boundary shift problem of kernel estimation in the prediction for improving the prediction accuracy. Finally, the validity of the method is verified through case studies and the accuracy of the model is judged using evaluation quasi-measures.
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Cao, Lei, Stephen J. Kohut, and Blaise deB Frederick. "Estimating and mitigating the effects of systemic low frequency oscillations (sLFO) on resting state networks in awake non-human primates using time lag dependent methodology." Frontiers in Neuroimaging 1 (January 19, 2023). http://dx.doi.org/10.3389/fnimg.2022.1031991.
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AimResting-state fMRI (rs-fMRI) is often used to infer regional brain interactions from the degree of temporal correlation between spontaneous low-frequency fluctuations, thought to reflect local changes in the BOLD signal due to neuronal activity. One complication in the analysis and interpretation of rs-fMRI data is the existence of non-neuronal low frequency physiological noise (systemic low frequency oscillations; sLFOs) which occurs within the same low frequency band as the signal used to compute functional connectivity. Here, we demonstrate the use of a time lag mapping technique to estimate and mitigate the effects of the sLFO signal on resting state functional connectivity of awake squirrel monkeys.MethodsTwelve squirrel monkeys (6 male/6 female) were acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Rs-fMRI data was preprocessed using an in-house pipeline and sLFOs were detected using a seed regressor generated by averaging BOLD signal across all voxels in the brain, which was then refined recursively within a time window of −16–12 s. The refined regressor was then used to estimate the voxel-wise sLFOs; these regressors were subsequently included in the general linear model to remove these moving hemodynamic components from the rs-fMRI data using general linear model filtering. Group level independent component analysis (ICA) with dual regression was used to detect resting-state networks and compare networks before and after sLFO denoising.ResultsResults show sLFOs constitute ~64% of the low frequency fMRI signal in squirrel monkey gray matter; they arrive earlier in regions in proximity to the middle cerebral arteries (e.g., somatosensory cortex) and later in regions close to draining vessels (e.g., cerebellum). Dual regression results showed that the physiological noise was significantly reduced after removing sLFOs and the extent of reduction was determined by the brain region contained in the resting-state network.ConclusionThese results highlight the need to estimate and remove sLFOs from fMRI data before further analysis.