measurement noise

At the conclusion of a noise annoyance survey in air-conditioned offices a new parameter was proposed. The aim was to define a noise index well correlated with subjective ratings of noise annoyance and able to account for the imbalance of the noise spectrum. As the RC Mark II procedure for rating the noise of HVAC systems in buildings introduced the “quality assessment index”(QAI), which provides a measure of the spectral imbalance of a noise, the combination of this parameter with the RC criterion number (which only measures noise ...

Robust calibration of option valuation models to quoted option prices is non-trivial but crucial for good performance. A framework based on the state-space formulation of the option valuation model is introduced. Non-linear (Kalman) filters are needed to do inference since the models have latent variables (e.g. volatility). The statistical framework is made adaptive by introducing stochastic dynamics for the parameters. This allows the parameters to change over time, while treating the measurement noise in a statistically consistent way and using all data efficiently. The performance and computational efficiency of standard and iterated extended Kalman filters (EKF and IEKF) are investigated. These methods are compared to common calibration such as weighted least squares (WLS) and penalized weighted least squares (PWLS). A simulation study, using the Bates model, shows that the adaptive framework is capable of tracking time varying parameters and latent processes such as stochastic ...

To realize high-precision attitude stabilization of a flexible spacecraft in the presence of complex disturbances and measurement noises, an iterative learning disturbance observer (ILDO) is presented in this paper. Firstly, a dynamic model of disturbance is built by augmenting the integral of the lumped disturbance as a state. Based on it, ILDO is designed by introducing iterative learning structures. Then, comparative analyses of ILDO and traditional disturbance observers are carried out in frequency domain. It demonstrates that ILDO combines the advantages of high accuracy in disturbance estimation and favorable robustness to measurement noise. After that, an ILDO based composite controller is designed to stabilize the spacecraft attitude. Finally, the effectiveness of the proposed control scheme is verified by simulations.

Abstract-In electrical railway systems there is often a need of detecting or/and predicting harmonic signals contained in measurement data for vehicle control or monitoring purpose. An efficient on-line estimation method for such applications is the Kalman filter technique. However, ...

A fuzzy logic system (FLS) with a new sliding window defuzzifier is developed for damage detection. The effect of changes in the damage evaluation parameter (frequency) due to uncertainty in material properties is explored and the results of the probabilistic analysis are used to ...

Unmanned Aerial Vehicles (UAVs) are playing an important role both in military as well as civilian applications. However, their role in civilian applications is hampered by the lack of adequate guidelines and operational requirements. In this paper a set of flight, operational and performance requirements for the use of fixed wing UAVs in civilian applications are collated from several resources

Abstract: The aim of this work is to compare Dynamic Data Reconciliation techniques by both theoretical tools and numerical simulations. Most nonlinear dynamic data reconciliation methods have studied cases where the input variables are constant over long periods of time separated by simple step changes. This scenario often occurs in process control; however, it imposes strong limitations on the method’s applicability. In this work the dynamic data reconciliation algorithms are compared by the application to the cases where the input variables are ramps or slow sinusoidal functions or any smooth signal. The model-based Kalman filter is compared with Liebman’s Dynamic Data Reconciliation Technique and a hybrid approach is proposed.

... Rayan Kutty PP , Sreenivasa Murthy A. Dept. ... the signal is degraded in few samples and restoration is only required at these places) and 'global degradation' which affects the entire waveform (Broad band noise or hiss, pitch variation effects in the play back mechanism like wow ...

We propose novel necessary and sufficient conditions for a sensing matrix to be “s-good ” – to allow for exact ℓ1-recovery of sparse signals with s nonzero entries when no measurement noise is present. Then we express the error bounds for imperfect ℓ1-recovery (nonzero measurement noise, nearly s-sparse signal, near-optimal solution of the optimization problem yielding the ℓ1recovery) in terms of the characteristics underlying these conditions. Further, we demonstrate (and this is the principal result of the paper) that these characteristics, although difficult to evaluate, lead to verifiable sufficient conditions for exact sparse ℓ1-recovery and to efficiently computable upper bounds on those s for which a given sensing matrix is s-good. We establish also instructive links between our approach and the basic concepts of the Compressed Sensing theory, like Restricted Isometry or Restricted Eigenvalue properties. 1

Abstract-In electrical railway systems there is often a need of detecting or/and predicting harmonic signals contained in measurement data for vehicle control or monitoring purpose. An efficient on-line estimation method for such applications is the Kalman filter technique. However, ...

Currently, there are technology barriers inhibiting speech processing systems working under extreme noisy conditions. The emerging applications of speech technology, especially in the fields of wireless communications, digital hearing aids or speech recognition, are ...

The Semiconductor Laser Group within RLE addresses science and technology questions that arise during the development and application of new device technologies. During the last year the research results from the group have included: • Models for noise in various semiconductor lasers were developed. The devices studied included: o Surface emitting lasers o Quantum cascade lasers o Mode-locked semiconductor lasers.

The major concentration of this study is to describe the structure of a C++/MEX solution for robust detection and delineation of arterial blood pressure (ABP) signal events. Toward this objective, the original ABP signal was pre-processed by application of à trous discrete wavelet transform (DWT) to extract several dyadic scales. Then, a sliding window with fixed length was moved on the appropriately selected scale. In each slid, mean, variance, Skewness and Kurtosis values of the excerpted segment were superimposed to generate a newly defined multiple higher order moments (MHOM) metric to be used as the detection decision statistic (DS). Then, after application of an adaptive-nonlinear transformation for making the DS baseline static, the histogram parameters of the enhanced DS were used for regulation of the α-level Neyman–Pearson classifier aimed for false alarm probability (FAP)-bounded delineation of the ABP events. The proposed method was applied to all 18 subjects of the MIT-...

ABSTRACT We describe the fabrication of an intrinsic fiber Fabry-Pérot interferometer meant to be used in white light interferometry sensing experiments. It requires the elaboration of a cavity exhibiting a finesse of about 2, and therefore, cavity mirrors with a reflection coefficient of about 23%. The different steps involved in the fabrication of the cavity are addressed. We discuss the optical properties of the e-beam deposited TiO2 and SiO2 multilayer mirrors used for the fabrication of the cavity. In this part, a convenient method for measuring the complex refractive index of the materials is proposed, as well as an in situ monitoring technique used to accurately control the film properties. We next study the evolution of the mirror reflectivity with the splicing procedure, and we propose a modeling of the mirror composition after splicing. Then, the fabrication of a 20-mm-long Fabry-Pérot cavity exhibiting a visibility of 0.5 is presented. In this section, an efficient signal processing is proposed to reduce any measurement noise.

Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand cal...

Registration of a rigid body is an essential step in many machine vision tasks. This paper presents performance analysis of the iterative closest point method implemented for dimensional inspection of free form surfaces where the set of measured points is registered to a NURBS model. The results were obtained through Monte Carlo simulation. A large number of experiments were performed

Fault diagnosis is a major problem in industrial systems, and is of primary interest for mobile and industrial robotics where electric motors are used. In this paper fault diagnosis with the use of the Kalman filter is compared to fault diagnosis based on particle filter. The Kalman filter assumes linear model representation and Gaussian measurement noise whereas the particle filter is suitable for nonlinear models and does not make any assumption on the measurement noise distribution. The performance of the proposed methodology is tested through simulation experiments.

The land surface albedo is a key parameter influencing the climate near the ground. Therefore, it must be determined with sufficient accuracy. In this paper, a statistical inversion method is presented in support of the application of kernel-based Bi-directional Reflectance Distribution Function (BRDF) models for the calculation of the surface albedo. The method provides the best linear unbiased estimations (BLUE) of the BRDF model coefficients for an arbitrary number of available angular measurements. When the number of measurements exceeds the number of the estimated coefficients, the QR decomposition method is proposed to improve the ill-conditional features of the inversion matrix. In other cases, the singular value decomposition (SVD) method is suggested. The proposed inversion method is innovative in that it provides confidence intervals for each of the BRDF model coefficients with a prescribed significance expressed by a probability level. Five candidate kernel-driven BRDF models were used in the present simulation study: Li–Sparse, Roujean, Li–Sparse–Wanner, Li–Dense and Walthall. A ground-based reflectance measurement data set including 11 surface types forms the background for the inversion experiments. The results show a strong dependence on the solar zenith angle (SZA) and on the land cover type (LCT) for all candidate models. Owing to this, none model could be recommend in a general manner. The Li–Sparse and the Li–Sparse–Wanner models performed the best for the grass and wheat LCT, while the Roujean model appeared as a favorite for the pine and deciduous forests. The implementation of the confidence interval technique shows that the BRDF model coefficients can be retrieved with an uncertainty of 20–30%, and somewhat greater in the case of forest. The measured angular reflectance curves lie, as a rule, within the uncertainty bands related to the 5% significance level (95% probability). The corresponding albedo estimates can be characterized by an absolute uncertainty of 1–2% in the visible band and 5–10% in the near infrared band, or by 10–30% in relative terms. The reflectance measurements at low SZA values are preferable for BRDF model inversion for the grassland and crop, while medium range of SZA seems to provide more information on forest features. For the majority of LCT, the results of BRDF model inversion seem to be less reliable when considering multi-angular measurements for various SZA than for a single SZA.

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 ...

Target tracking with Kalman filters is hampered by target maneuvering and unknown process and measurement noises. We show that moving data windows may be used to analyze state and measurement error sequences, determining robust estimates of bias and covariance. For steps in the system forcing functions and non-Gaussian measurement errors, the robust estimators yield improvements over linear bias and covariance estimators. Extensive simulations compare conventional, linear adaptive, and robust adaptive average step responses of a first-order system filter. Quantities examined are state estimate, state error, process and measurement covariance estimates, Kalman gain, and input step estimate.

In this paper, a mixed robust feedback linearization with linear GH controller is applied to a non linear quadrotor unmanned aerial vehicle. An actuator saturation and constrain on state space output are introduced to analyse the worst case of control law design. The results show that the overall system becomes robust when weighting functions are chosen judiciously. Performance issues of