An extended state observer with adjustable bandwidth for measurement noise

In this paper,a bandwidth-adjustable extended state observer(ABESO)is proposed for the systems with measurement noise.It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise,which conflicts with observation accuracy.Therefore,we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system.The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error.When the tracking error decreases,the bandwidth decreases to suppress the noise,otherwise the bandwidth does not change.It is proven that the error dynamics are bounded and converge in finite time.The relationship between the upper bound of the estimation error and the scaling factor is given.When the scaling factor is less than 1,the ABESO has higher estimation accuracy than the linear extended state observer(LESO).Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments.The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.

Noise analysis and measurement of time delay and integration charge coupled device

Time delay and integration （TDI） charge coupled device （CCD） noise sets a fundamental limit on image sensor performance, especially under low illumination in remote sensing applications. After introducing the complete sources of CCD noise, we study the effects of TDI operation mode on noise, and the relationship between different types of noise and number of the TDI stage. Then we propose a new technique to identify and measure sources of TDI CCD noise employing mathematical statistics theory, where theoretical analysis shows that noise estimated formulation converges well. Finally, we establish a testing platform to carry out experiments, and a standard TDI CCD is calibrated by using the proposed method. The experimental results show that the noise analysis and measurement methods presented in this paper are useful for modeling TDI CCDs.

Particle Velocity Sensor and Its Application in Near-Field Noise Scanning Measurement

The Particle Velocity Sensor (PVS) is a kind of acoustic transducer which measures the particle velocity directly with figure-of-eight directivity. This paper proposes a near-field noise scanning technology based on the research of PVS, pressure-particle velocity (P-U) probe, and its application in noise source identification. Firstly, the principle and characteristics of PVS are presented. Secondly, a P-U probe is designed on the basis of PVS development. Finally, the noise measurement experiment for a single source is arranged and conducted. The result shows that the proposed P-U probe performs well in near-field noise source identification and localization.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference

This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise.Based on the cubature Kalman filter,we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise.The system states and the statistics of skew t noise distribution,including the shape matrix,the scale matrix,and the degree of freedom(DOF)are estimated jointly by employing variational Bayesian(VB)inference.The proposed method is validated in a target tracking example.Results of the simulation indicate that the proposed nonlinear filter can perform satisfactorily in the presence of unknown statistics of measurement noise and outperform than the existing state-of-the-art nonlinear filters.

Precision measurement and suppression of low-frequency noise in a current source with double-resonance alignment magnetometers

Low-noise high-stability current sources have essential applications such as neutron electric dipole moment measurement and high-stability magnetometers. Previous studies mainly focused on frequency noise above 0.1 Hz while less on the low-frequency noise/drift. We use double resonance alignment magnetometers(DRAMs) to measure and suppress the low-frequency noise of a homemade current source(CS) board. The CS board noise level is suppressed by about 10 times in the range of 0.001-0.1 Hz and is reduced to 100 n A/√Hz at 0.001 Hz. The relative stability of CS board can reach2.2 × 10^(-8). In addition, the DRAM shows a better resolution and accuracy than a commercial 7.5-digit multimeter when measuring our homemade CS board. Further, by combining the DRAM with a double resonance orientation magnetometer,we may realize a low-noise CS in the 0.001-1000 Hz range.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Consensus for High-Order Linear Multi-Agent Systems with Unknown but Bounded Measurement Noises

The staticε-consensus problem for a high-order linear multi-agent system is studied over a connected undirected communication topology,in which the state measurements of neighbors are affected by unknown but bounded(UBB)noises.Using the dead-zone function and binomial coefficients,we propose a distributed consensus protocol.Under this protocol,all agents achieve staticε-consensus,i.e.,the first components of the states for each agent reachε-consensus,and the remaining components reach agreement at zero.Numerical examples illustrate the validity of the theoretical results.

Assessment of Indoor and Outdoor Noise Pollution at a University Hospital Based on Acoustic Measurements and Noise Mapping

This paper describes an analysis of the sound pressure levels at the exterior façade and inside the Hospital de Clínicas of the Federal University of Paraná. Measurements were taken at a total of 45 points, 24 at the exterior facade and 21 points inside the hospital on all the floors of the main building and in the maternity building. These 45 measurements were used to calculate the acoustic map of the hospital, which is located on General Carneiro Street, in the city of Curitiba, state of Paraná, Brazil. A comparison of the measured outdoor sound pressure levels against those established by Curitiba Municipal Law No. 10625 revealed that they all exceeded the daytime limit of 55 dB(A) permitted for areas around hospitals. The indoor sound pressure measurements and the noise levels for acoustic comfort established by the Brazilian technical standard ABNT NBR 10152 were compared, indicating that all the measured points, including those in the neonatal intensive care unit, exceeded the established limit of 35 dB(A) to 45 dB(A).

Static filtering and collocation in successive steps for measurements with colored noises

The theory of static filtering in successive steps with colored noises is ample scope for its application in the procedure of GPS data processing. A majority of error and round-cycle distinctness in the measurements of GPS carrier phase can be eliminated through three times of differential calculations of stations, satellites and epochs, in which the cycle beatings became isolated values. The observation error of three-differentials in the intervals between epochs results in block diagonal matrixes in the covariance matrixes, so that the desired matrixes occupy too large memory and the processing time is too long. Therefore, by using the theory of static filtering in successive steps with colored noises, the interrelation can be eliminated and can save the processing time and memory.

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

Advancements in Sound Reflection and Airborne Sound Insulation Measurement on Noise Barriers

The in-situ measurement of sound reflection and airborne sound insulation characteristics of a noise barrier in Europe are currently performed following the CEN/TS 1793-5 European standard guidelines (last revision published in 2003 [1]). After some years a large number of barriers measured, the original method has been significantly enhanced and validated in the frame of the EU funded QUIESST project, WP3 [2]. The sound reflection measurement method has been improved using a square 9-microphone grid not rigidly connected to the loudspeaker, an optimized alignment algorithm of free-field and reflected impulse responses, including fractional step shifts and least squares estimation of the best relative position, and a correction for geometrical divergence and sound source directivity. Each single measurement is then validated by means of the Reduction Factor calculation. The airborne sound insulation measurement method has not been markedly changed since 2003, because the procedure is robust and easily applicable as it is, but some problems may still be encountered when measuring highly insulating noise barriers, due to a poor signal to noise ratio of the transmitted impulse response. In those cases it is difficult to realize just after the measurement whether the obtained data are valid or not. A method, applicable on site, to overcome this problem is described here. It is based on the Signal to Noise Ratio estimation of critical parts of the acquired impulse responses and gives a strong validation criterion.

INFORMATION FUSION STEADY-STATE WHITE NOISE DECONVOLUTION ESTIMATORS WITH TIME-DELAYED MEASUREMENTS AND COLORED MEASUREMENT NOISES

White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.

Dense coding capacity in correlated noisy channels with weak measurement

Capacity of dense coding via correlated noisy channel is greater than that via uncorrelated noisy channel.It is shown that the weak measurement and reversal measurement need to further improve their quantum dense coding capacity in correlated amplitude damping channel,but this improvement is very small in correlated phase damping channel and correlated depolarizing channel.

Quantum nondemolition measurements of a flux qubit coupled to a noisy detector

We theoretically study the quantum nondemolition measurements of a flux qubit coupled to a noisy superconduct- ing quantum interference device （SQUID）. The obtained analytical results indicate that the measurement probability is frequency-dependent in a short time scale and has a close relationship with the measurement-induced dephasing. Furthermore, when the detuning between the driven and bare resonator equals the coupling strength, we can obtain the maximum measurement rate that is determined by the character of the noise in the SQUID. Finally, we analysed the mixed effect caused by coupling between the non-diagonal term and the external variable. It is found that the initial information of the qubit is destroyed due to quantum tunneling between the qubit states.

An adaptive noise attenuation method for edge and amplitude preservation

Noise intensity distributed in seismic data varies with different frequencies or frequency bands; thus, noise attenuation on the full-frequency band affects the dynamic properties of the seismic reflection signal and the subsequent seismic data interpretation, reservoir description, hydrocarbon detection, etc. Hence, we propose an adaptive noise attenuation method for edge and amplitude preservation, wherein the wavelet packet transform is used to decompose the full-band seismic signal into multiband data and then process these data using nonlinear anisotropic dip-oriented edge-preserving fi ltering. In the fi ltering, the calculated diffusion tensor from the structure tensor can be exploited to establish the direction of smoothing. In addition, the fault confidence measure and discontinuity operator can be used to preserve the structural and stratigraphic discontinuities and edges, and the decorrelation criteria can be used to establish the number of iterations. These parameters can minimize the intervention and subjectivity of the interpreter, and simplify the application of the proposed method. We applied the proposed method to synthetic and real 3D marine seismic data. We found that the proposed method could be used to attenuate noise in seismic data while preserving the effective discontinuity information and amplitude characteristics in seismic refl ection waves, providing high-quality data for interpretation and analysis such as high-resolution processing, attribute analysis, and inversion.

SMART STRUCTURES USING PIEZOELEMENTS FOR ACTIVE VIBRATION AND NOISE SUPPRESSION IN AVIATION AND AEROSPACE

Smart material and structure (SMS) is a challenging novel technique for the 21 century especially in fields of aviation and aerospace. Vibration and noise suppression smart structure is an important branch of SMS. There are several typical structures such as the cabin of an airplane, space station, the solar board of satellite and the rotor blade of a helicopter, of which the vibrations and radiation noises have bad influences on precise equipments and aiming systems. In order to suppress vibrations and noises of these structures, several algorithms are applied to the models which simulate the structures. Experiments are performed to suppress vibrations and noises by bonding sensors and actuators to the structures at the optimized locations and using computer based measurement and control systems. For the blade vibration control of a helicopter, a non contact method of signal transmission by magneto electric coupling is discussed. The experimental results demonstrate that the methods used for active control are effective.

一种基于Measurement Studio计算动态信号1/3倍频程谱的方法

在声学中,1/3倍频程谱反映了声源的能量分布情况。利用对信号1/3倍频程谱分析的结果,选择适当的吸声系数的材料,以达到良好的降噪效果。通过利用Measurement Studio中的Visual Basic组件,提供了一种快速、准确、可视化的计算1/3倍频程谱的方法。