Strong Measurement Noise Research Articles

Particle Filter-Based Fault Detection for Toolface Measurement of Rotary Steerable Systems

In this paper, the problem of accelerometer fault detection is studied for the rotary steerable drilling tool system (RSDTS) which is modeled by a nonlinear stochastic system. Owing to the strong vibration in the drilling process, RSDTSs are inevitably influenced by strong measurement noise, which brings in substantial difficulties in the fault detection. Firstly, a modified particle filtering algorithm is proposed to improve the accuracy of state estimation. Next, by means of the moving average method, the residual which approximatively obeys the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution is designed when the estimation error is small enough. According to the property of the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> distribution, the window length of the moving average method is determined. Moreover, a fault detection algorithm is presented, and false alarm rate and missed detection rate are quantitatively analyzed for nonlinear stochastic systems. Finally, the effectiveness of the developed fault detection method is verified by simulation and experiment on the prototype of the RSDTS.

Trajectory planning and tracking for four‐wheel steering vehicle based on differential flatness and active disturbance rejection controller

SummaryTo address the difficulties of under‐actuation, nonlinearity, strong coupling, disturbances and measurement noise in four‐wheel steering (4WS) vehicle tracking control, this paper presents an innovative control strategy based on differential flatness and linear active disturbance rejection controller (LADRC). The flatness property of 4WS vehicle is derived from a simplified dynamic model that ignores some complicated dynamics and external disturbances. The impact of these neglected components on the system is treated as part of the total‐disturbance, which is estimated by the observer and compensated by LADRC. Three flatness‐based control schemes, including feedback linearization controller with PID corrective term, traditional LADRC and LADRC with robust generalized proportional integral observer (RGPIO), are designed and investigated in this paper. Moreover, an overtaking scenario is selected as an application example for the tracking controllers, and the corresponding trajectory planner is designed. The tracking performances of these schemes for the overtaking trajectory are simulated on a three‐degree‐of‐freedom dynamic model and a more complete dynamic model of the 4WS vehicle, respectively. Results show the effectiveness of these schemes, especially the excellent tracking performance of LADRC with RGPIO in the presence of strong external disturbances and measurement noise.

Differential Flatness-Based, Full-Order Nonlinear Control of a Modular Multilevel Converter (MMC)

Modular multilevel converter (MMC) is an attractive topology in industrial applications such as high-voltage direct current (HVdc) transmission system where fast power control is required. The most conventional control method for MMC uses cascaded structures, where the power transmission and internal energy dynamic (outer loops) is limited by the “frequency separation” constraints since the dynamic choice of the currents (inner loops) must consider the tradeoff between rapidity and robustness. In this article, we present a new control method based on a differential flatness theory. The main interest of the proposed control is the possibility to obtain a very high dynamic performance of the MMC power flow, even under noisy measurements and parametric disturbances. Using the well-known average model of the MMC, a flat output is proposed, proving the differential flatness property of the MMC. Trajectory tracking controllers of ac grid active and reactive power are proposed using input-state full-order linearization. Simulation results, including losses corresponding to a realistic case of on-shore, point-to-point HVdc interconnection, are presented to show the performance of the proposed control scheme even with strong measurement noise.

Incorporating Latent Constraints to Enhance Inference of Network Structure

A complex network is a model representation of interactions within technological, social, information, and biological networks. Oftentimes, we are interested in identifying the underlying network structure from limited and noisy observational data, which is a challenging problem. Here, to address this problem, we propose a novel and effective technique that incorporates latent structural constraints into binary compressed sensing. We show high accuracy and robust effectiveness of our proposed method by analyzing artificial small-world and scale-free networks, as well as two empirical networks. Our method requires a relatively small number of observations and it is robust against strong measurement noise. These results suggest that incorporating latent structural constraints into an algorithm for identifying the underlying network structure improves the inference of connections in complex networks.

Comment on “ferroelectricity-free lead halide perovskites” by A. Gómez, Q. Wang, A. R. Goñi, M. Campoy-Quiles and A. Abate, Energy Environ. Sci., 2019, 12, 2537

This comment analyzes pitfalls when investigating piezoresponse and ferroelectricity in organic-metal halide perovskite thin films.

3D CNN-Based Semantic Labeling Approach for Mobile Laser Scanning Data

In this paper, we introduce a 3D convolutional neural network (CNN)-based method to segment point clouds obtained by mobile laser scanning (MLS) sensors into nine different semantic classes, which can be used for high definition city map generation. The main purpose of semantic point labeling is to provide a detailed and reliable background map for self-driving vehicles (SDV), which indicates the roads and various landmark objects for navigation and decision support of SDVs. Our approach considers several practical aspects of raw MLS sensor data processing, including the presence of diverse urban objects, varying point density, and strong measurement noise of phantom effects caused by objects moving concurrently with the scanning platform. We also provide a new manually annotated MLS benchmark set called SZTAKI CityMLS , which is used to evaluate the proposed approach, and to compare our solution to various reference techniques proposed for semantic point cloud segmentation. Apart from point level validation we also present a case study on Lidar-based accurate self-localization of SDVs in the segmented MLS map.

Simplified Tsunami Modeling and Waveform Reconstruction With GNSS-R Observations

This paper investigates Tsunami modeling, waveform reconstruction and parameter estimation using noisy sea surface height measurements observed by a satellite-carried global navigation satellite system (GNSS) sensor. Wavelet theory is used to mitigate the strong measurement noise. Tsunami modeling is considered and Cramer–Rao lower bound is derived for estimation of Tsunami shape parameters, wave height and wavelength. Real Tsunami data observed by altimetry satellites are used for performance evaluation and the results demonstrate the efficiency of the proposed approach.

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach.

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

Parameter-free resolution of the superposition of stochastic signals

This paper presents a direct method to obtain the deterministic and stochastic contribution of the sum of two independent stochastic processes, one of which is an Ornstein–Uhlenbeck process and the other a general (non-linear) Langevin process. The method is able to distinguish between the stochastic processes, retrieving their corresponding stochastic evolution equations. This framework is based on a recent approach for the analysis of multidimensional Langevin-type stochastic processes in the presence of strong measurement (or observational) noise, which is here extended to impose neither constraints nor parameters and extract all coefficients directly from the empirical data sets. Using synthetic data, it is shown that the method yields satisfactory results.

HISTORY: An Efficient and Robust Algorithm for Noisy 1-Bit Compressed Sensing

We consider the problem of sparse signal recovery from 1-bit measurements. Due to the noise present in the acquisition and transmission process, some quantized bits may be flipped to their opposite states. These sign flips may result in severe performance degradation. In this study, a novel algorithm, termed HISTORY, is proposed. It consists of Hamming support detection and coefficients recovery. The HISTORY algorithm has high recovery accuracy and is robust to strong measurement noise. Numerical results are provided to demonstrate the effectiveness and superiority of the proposed algorithm.

Shape anomaly detection under strong measurement noise: An analytical approach to adaptive thresholding.

We suggest an analytical approach to the adaptive thresholding in a shape anomaly detection problem. We find an analytical expression for the distribution of the cosine similarity score between a reference shape and an observational shape hindered by strong measurement noise that depends solely on the noise level and is independent of the particular shape analyzed. The analytical treatment is also confirmed by computer simulations and shows nearly perfect agreement. Using this analytical solution, we suggest an improved shape anomaly detection approach based on adaptive thresholding. We validate the noise robustness of our approach using typical shapes of normal and pathological electrocardiogram cycles hindered by additive white noise. We show explicitly that under high noise levels our approach considerably outperforms the conventional tactic that does not take into account variations in the noise level.

Stochastic Time Series with Strong, Correlated Measurement Noise: Markov Analysis in N Dimensions

An extension and generalization of a recently presented approach for the analysis of Langevin-type stochastic processes in the presence of strong measurement noise is presented. For a stochastic process in N dimensions which is superimposed with strong, exponentially correlated, Gaussian distributed, measurement noise it is possible to extract the strength and the correlation functions of the noise as well as polynomial approximations of the drift and diffusion functions of the underlying process.

Evaluating strong measurement noise in data series with simulated annealing method

Many stochastic time series can be described by a Langevin equation composed of a deterministic and a stochastic dynamical part. Such a stochastic process can be reconstructed by means of a recently introduced nonparametric method, thus increasing the predictability, i.e. knowledge of the macroscopic drift and the microscopic diffusion functions. If the measurement of a stochastic process is affected by additional strong measurement noise, the reconstruction process cannot be applied. Here, we present a method for the reconstruction of stochastic processes in the presence of strong measurement noise, based on a suitably parametrized ansatz. At the core of the process is the minimization of the functional distance between terms containing the conditional moments taken from measurement data, and the corresponding ansatz functions. It is shown that a minimization of the distance by means of a simulated annealing procedure yields better results than a previously used Levenberg-Marquardt algorithm, which permits a rapid and reliable reconstruction of the stochastic process.

Analysis of stochastic time series in the presence of strong measurement noise

An alternative approach for the analysis of Langevin-type stochastic processes in the presence of strong measurement noise is presented. For the case of Gaussian distributed, exponentially correlated, measurement noise it is possible to extract the strength and the correlation time of the noise as well as polynomial approximations of the drift and diffusion functions of the underlying Langevin equation.

Extracting strong measurement noise from stochastic time series: Applications to empirical data

It is a big challenge in the analysis of experimental data to disentangle the unavoidable measurement noise from the intrinsic dynamical noise. Here we present a general operational method to extract measurement noise from stochastic time series even in the case when the amplitudes of measurement noise and uncontaminated signal are of the same order of magnitude. Our approach is based on a recently developed method for a nonparametric reconstruction of Langevin processes. Minimizing a proper non-negative function, the procedure is able to correctly extract strong measurement noise and to estimate drift and diffusion coefficients in the Langevin equation describing the evolution of the original uncorrupted signal. As input, the algorithm uses only the two first conditional moments extracted directly from the stochastic series and is therefore suitable for a broad panoply of different signals. To demonstrate the power of the method, we apply the algorithm to synthetic as well as climatological measurement data, namely, the daily North Atlantic Oscillation index, shedding light on the discussion of the nature of its underlying physical processes.

Tracking noisy limit cycle oscillation with nonlinear filters

For engineering systems, the dynamic state estimates provide valuable information for the detection and prediction of failure due to noise and vibration. From this perspective, nonlinear filtering techniques are applied to the problem of state estimation of dynamical systems undergoing noisy limit cycle oscillation. Specifically, the extended Kalman filter, ensemble Kalman filter and particle filter are used to track the limit cycle oscillations of a Duffing oscillator using noisy observational data. The noisy limit cycle oscillations feature highly non-Gaussian trends. The efficiency and limitations of the extended Kalman filter, ensemble Kalman filter and particle filter in tracking limit cycle oscillations are examined with respect to the model and measurement noise and sparsity of measurement data. For the limit cycle oscillations considered here, it is demonstrated that the ensemble Kalman filter and particle filter outperform the extended Kalman filter in the presence of sparse observational data or strong measurement noise. For moderate measurement noise and frequent measurement data, the ensemble Kalman filter and particle filter perform equally well in comparison to the extended Kalman filter.

Nonlinear filters for chaotic oscillatory systems

This paper examines and contrasts the feasibility of joint state and parameter estimation of noise-driven chaotic systems using the extended Kalman filter (EKF), ensemble Kalman filter (EnKF), and particle filter (PF). In particular, we consider the chaotic vibration of a noisy Duffing oscillator perturbed by combined harmonic and random inputs ensuing a transition probability density function (pdf) of motion which displays strongly non-Gaussian features. This system offers computational simplicity while exhibiting a kaleidoscope of dynamical behavior with a slight change of input and system parameters. An extensive numerical study is undertaken to contrast the performance of various nonlinear filtering algorithms with respect to sparsity of observational data and strength of model and measurement noise. In general, the performance of EnKF is better than PF for smaller ensemble size, while for larger ensembles PF outperforms EnKF. For moderate measurement noise and frequent measurement data, EKF is able to correctly track the dynamics of the system. However, EKF performance is unsatisfactory in the presence of sparse observational data or strong measurement noise.

Reconstruction of Complex Dynamical Systems Affected by Strong Measurement Noise

This Letter reports on a new approach to properly analyze time series of dynamical systems which are spoilt by the simultaneous presence of dynamical noise and measurement noise. It is shown that even strong external measurement noise as well as dynamical noise which is an intrinsic part of the dynamical process can be quantified correctly, solely on the basis of measured time series and proper data analysis. Finally, real world data sets are presented pointing out the relevance of the new approach.

Atmospheric characterization in the presence of strong additive measurement noise

The phase structure function and the phase-slope structure function have been used to characterize atmospheric-turbulence parameters—parameters that are important in the performance evaluation and ultimately in the design of adaptive optics systems for astronomical imaging applications and for laser beam propagations. We have extended the method to account for the effects of strong beam-path-induced aberrations and additive measurement noise. A stable noise-suppressant algorithm is devised in which the estimation is done sequentially: first the atmospheric-turbulence parameters are estimated independently, and then the noise-variance parameter is estimated. The developed theory is applied to the Shack–Hartmann wave-front-sensor data collected in the recently completed Airborne Laser Extended Atmospheric Characterization Experiment for very-long-horizontal-path (between 60 and 100 km) laser beam propagations. The estimated r0 values are in general agreement with the values obtained from the high-bandwidth scintillometer analysis on data taken in the same experiment. In addition, if the atmospheric turbulence spectrum can be described by a simple power law, then the data reduction with use of the developed estimation equations, which allow arbitrary atmospheric-turbulence power-law values, indicates that deviation from Kolmogorov turbulence is negligible.