Sensor Saturation Research Articles

Finite-horizon H∞ filtering for time-varying delay systems with randomly varying nonlinearities and sensor saturations

This paper mainly focuses on the H∞ filtering problem for a class of discrete time-varying systems with delays and randomly varying nonlinearities and sensor saturations. Two sets of binary switching sequences taking values of 1 and 0 are introduced to account for the stochastic phenomena of nonlinearities and sensor saturations which occur and influence the dynamics of the system in a probabilistic way. To further reflect the realities of transmission failure in the measurement, missing observation case is also considered simultaneously. By appropriately constructing a time-varying Lyapunov function and utilizing the stochastic analysis technique, sufficient criteria are presented in terms of a set of recursive linear matrix inequalities (RLMIs) under which the filtering error dynamics achieves the prescribed H∞ performance over a finite horizon. Moreover, at each time point k, the time-varying filter parameters can be solved iteratively according to the explicit solutions of the RLMIs. Finally, a numerical simulation is exploited to demonstrate the effectiveness of the proposed filter design scheme.

DETECTION OF SOOT PARTICLES USING A RESISTIVE TRANSDUCER BASED ON THERMOPHORESIS

A soot transducer based on the measurement of the electrical resistance value of the soot layer deposited on a finger structure was tested at different thermophoretic potentials. The soot, similar to that issued from the Diesel engines, in terms of particle number and size distribution, was obtained by the incomplete burning of propane and subsequent dilution. The decrease of the resistance value in time was directly proportional with the temperature gradient. Thus a soot transducer is aimed to be used in real Diesel exhaust, to monitor the particle release in the ambient air. An important practical aspect concerns the possibility to regenerate the sensorial system, when their resistance reaches a certain value corresponding to the sensor saturation. The soot can be removed by the burn off in atmosphere containing oxygen. The catalytic effect of platinum deposited on the finger structure as small isolated islands on the surface, was investigated. It was pointed out that the platinum islands are catalytically active, determining the soot removal at temperatures lower by 90 o C than in the case of the blank substrate.

Fuzzy-Model-Based Fault Detection for a Class of Nonlinear Systems With Networked Measurements

This paper is concerned with the fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements where there are significant uncertainties on information. A unified model is proposed to capture four sources of these uncertainties, namely, the sensor saturation, the signal quantization, the general medium access constraint, and the multiple packet dropouts. A simultaneous consideration of these issues reflects the practical networked systems much more closely than the existing works. The goal of this paper is to design a fault detector such that, for all unknown input, control input, and uncertain information, the estimation error between the residual and the fault is minimized. Using the switched system approach and some stochastic analyses, a sufficient condition for the existence of desired fault detector is established and the fault detector gains are computed by solving an optimization problem. Two numerical examples are given to show the effectiveness of the proposed design.

H∞ state estimation for complex networks with uncertain inner coupling and incomplete measurements.

In this paper, the H∞ state estimation problem is investigated for a class of complex networks with uncertain coupling strength and incomplete measurements. With the aid of the interval matrix approach, we make the first attempt to characterize the uncertainties entering into the inner coupling matrix. The incomplete measurements under consideration include sensor saturations, quantization, and missing measurements, all of which are assumed to occur randomly. By introducing a stochastic Kronecker delta function, these incomplete measurements are described in a unified way and a novel measurement model is proposed to account for these phenomena occurring with individual probability. With the measurement model, a set of H∞ state estimators is designed such that, for all admissible incomplete measurements as well as the uncertain coupling strength, the estimation error dynamics is exponentially mean-square stable and the H∞ performance requirement is satisfied. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem that can be easily solved using the semidefinite program method. Finally, a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.

Asynchronous H∞ filtering of switched time-delay systems with network induced random occurrences

This work studies the asynchronous H∞ filtering problem for a class of discrete-time switched time-delay systems. The word “asynchronous” means that the switching of the filters has a lag to the switching of system modes due to the network induced random occurrences, which includes both the sensor saturations and the missing measurements. First, new results on the regional stability and l2 gain analysis for the underlying system are given by allowing the Lyapunov-like function (LLF) to increase with a random probability during the unmatched period of the switching mode and the filter. Then, a regional H∞ filter with a modified mode dependent ellipsoid constraint is designed such that the filtering error dynamics is asymptotically stable in the mean square sense with a given disturbance attenuation level. Finally, a numerical example is given to verify the efficiency of the proposed method.

The Active Fire FRP Estimation: Study on Sentinel-3/SLSTR

The estimation of thermal parameters belonging to active fires, such as fire radiative power (FRP), is a recommended method to determine fire severity. A review of the state of the art provides several semiempirical methods to estimate the FRP. On the other hand, the Sentinel-3/SLSTR instrument has two spectral channels dedicated to active-fire observation, which allows fire monitoring in a wide variety of conditions not affected by sensor saturation. This contribution analyzes these technical approaches, which are to be applied on the future Sentinel-3/Sea and Land Surface Temperature Radiometer (SLSTR) sensor in order to clarify its requirements and restrictions.

A new online fault detection method based on PCA technique

In this paper, we suggest an extension of a previous study in Recursive Singular Spectrum Analysis (RSSA) (Hongli & Hui-Jun (2012) Fault detection for Markovian jump systems with sensor saturations and randomly varying non-linearities. IEEE Trans. Circuits Syst. I: Regul. Pap., 59, 2354–2362) to an online method for fault detection. This extended method is based on first-order perturbation (FOP) theory where the eigenvalues and eigenvectors of the foregoing covariance matrix are updated taking into account the effect of new acquired data which are considered as perturbation in the actual covariance matrix. This proposed diagnosis method is entitled ‘recursive principal component analysis based on FOP’ (RPCA-FOP) and is compared with other PCA techniques existing in literature such as the conventional PCA and the sliding window PCA where the average computation time, the missed detection rate and the false alarm rate are evaluated for each method.

Distributed average filtering for sensor networks with sensor saturation

This study addresses the distributed average set-membership filtering of spatially varying processes using sensor networks. The system under consideration contains sensor saturation in the presence of unknown-but-bounded process and measurement noise in the sensor network. The so-called distributed average set-membership filtering is defined to quantify bounded consensus regarding the estimation error. A sufficient condition for distributed average set-membership filtering parameter design is established in terms of a set of time-varying linear matrix inequalities. A recursive algorithm is developed for computing the estimator, controller gains and the ellipsoid that guarantees to contain the true state. Simulation results are provided to demonstrate the effectiveness of the proposed method.

State estimation for discrete-time delayed neural networks with fractional uncertainties and sensor saturations

In this paper, the state estimation problem is investigated for a new class of discrete-time delayed neural networks with fractional uncertainties and sensor saturations. The activation functions are described by the sector-like nonlinearities that are more general than the commonly used Lipschitz ones. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. An optimization method is employed to deal with the fractional uncertainties presented in the neural networks. The estimator is expected to be robust against parameter uncertainties and tolerant against the sensor saturations. It is shown that the estimator gains can be derived in terms of the delay bounds, uncertainty structures and saturation levels. The desired state estimator is designed via the semi-definite programme method that can be easily implemented by using available software. Finally, a numerical example is applied to demonstrate the effectiveness of the proposed state estimation approach.

Distributed filtering in sensor networks with randomly occurring saturations and successive packet dropouts

SUMMARY This paper is concerned with the distributed filtering problem for a class of nonlinear systems with randomly occurring sensor saturations (ROSS) and successive packet dropouts in sensor networks. The issue of ROSS is brought up to account for the random nature of sensor saturations in a networked environment of sensors, and accordingly, a novel sensor model is proposed to describe both the ROSS and successive packet dropouts within a unified framework. Two sets of Bernoulli distributed white sequences are introduced to govern the random occurrences of the sensor saturations and successive packet dropouts. Through available output measurements from not only the individual sensor but also its neighboring sensors, a sufficient condition is established for the desired distributed filter to ensure that the filtering dynamics is exponentially mean-square stable and the prescribed performance constraint is satisfied. The solution of the distributed filter gains is characterized by solving an auxiliary convex optimization problem. Finally, a simulation example is provided to show the effectiveness of the proposed filtering scheme. Copyright © 2013 John Wiley & Sons, Ltd.

Reliable H-infinity filtering for linear systems with sensor saturation

In this paper, a new reliable H-infinity filter design problem is proposed for a class of continuous-time systems with sensor saturation and failures. Attention is focused on the analysis and synthesis problems of a full order reliable H-infinity filter such that the filtering error dynamics is asymptotically stable with a guaranteed disturbance rejection attenuation level γ. It is shown that the filtering error dynamics obtained from the original system plus the filter can be modeled by a linear system with sector bounded nonlinearity. The design conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs). These conditions are then considered in a convex optimization problem with LMIs constraints in order to design an optimal reliable H-infinity filter. A numerical example is given to illustrate the effectiveness of the proposed results.

Palladium–Silver Mesowires for the Extended Detection of H2

Palladium-silver mesowires are prepared by electrochemical decoration of graphite step-edges with a good control of the alloy composition and wire diameter. As-prepared arrays are used for hydrogen sensing and demonstrate extended detection capabilities up to the whole concentration range of H(2) depending on the alloy composition. At low silver content, low hydrogen concentration is detected but the sensing window is narrow because of sensor saturation. The sensing window is advantageously extended to higher hydrogen concentrations for quantitative measurements up to pure H(2) flows with Ag-rich alloys. The mechanism responsible for these behaviors implies the statistical distribution in surface composition rather than the structural characteristics and stability domains of the corresponding hydride phases.

Filtering for Discrete Fuzzy Stochastic Time-Delay Systems with Sensor Saturation

This paper addresses the filtering problem for discrete fuzzy stochastic systems with time-varying delay and sensor saturation. Random noise depending on state and external disturbance is also taken into account. A decomposition approach is employed to solve the characteristic of sensor saturation. The scaled small gain (SSG) theorem is extended to the stochastic systems, which is employed to handle with the time-varying delay by transforming the original system into the form of an interconnected system consisting of two subsystems. By the proposed Lyapunov-Krasovskii function, the scaled small gains of the subsystems are analyzed, respectively. Sufficient conditions for the stochastic stability of the filtering error system with a prescribed level are established such that the gains of the filter can be obtained explicitly. Finally, simulation results are presented to demonstrate the effectiveness of the proposed approach.

Nonfragile Gain-Scheduled Control for Discrete-Time Stochastic Systems with Randomly Occurring Sensor Saturations

This paper is devoted to tackling the control problem for a class of discrete-time stochastic systems with randomly occurring sensor saturations. The considered sensor saturation phenomenon is assumed to occur in a random way based on the time-varying Bernoulli distribution with measurable probability in real time. The aim of the paper is to design a nonfragile gain-scheduled controller with probability-dependent gains which can be achieved by solving a convex optimization problem via semidefinite programming method. Subsequently, a new kind of probability-dependent Lyapunov functional is proposed in order to derive the controller with less conservatism. Finally, an illustrative example will demonstrate the effectiveness of our designed procedures.

Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey

Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.

Robust Filtering for Networked Stochastic Systems Subject to Sensor Nonlinearity

The problem of network-based robust filtering for stochastic systems with sensor nonlinearity is investigated in this paper. In the network environment, the effects of the sensor saturation, output quantization, and network-induced delay are taken into simultaneous consideration, and the output measurements received in the filter side are incomplete. The random delays are modeled as a linear function of the stochastic variable described by a Bernoulli random binary distribution. The derived criteria for performance analysis of the filtering-error system and filter design are proposed which can be solved by using convex optimization method. Numerical examples show the effectiveness of the design method.

3D magnetometer for a dilution refrigerator

In this report, we describe a development of a three dimensional system for measurements of magnetic field at a wide temperature range of 300K-4K. The system is based on 8 AMR sensors and allows for control of the magnetic environment in a dilution refrigerator during the cool down of a superconducting processor. With a low noise signal processing electronics and a special sensor saturation circuit, a magnetic induction resolution below of 1 nT was achieved.

Automatic Suppression of Intense Monochromatic Light in Electro-Optical Sensors

Electro-optical imaging sensors are widely distributed and used for many different tasks. Due to technical improvements, their pixel size has been steadily decreasing, resulting in a reduced saturation capacity. As a consequence, this progress makes them susceptible to intense point light sources. Developments in laser technology have led to very compact and powerful laser sources of any wavelength in the visible and near infrared spectral region, offered as laser pointers. The manifold of wavelengths makes it difficult to encounter sensor saturation over the complete operating waveband by conventional measures like absorption or interference filters. We present a concept for electro-optical sensors to suppress overexposure in the visible spectral region. The key element of the concept is a spatial light modulator in combination with wavelength multiplexing. This approach allows spectral filtering within a localized area in the field of view of the sensor. The system offers the possibility of automatic reduction of overexposure by monochromatic laser radiation.

Fault Detection for Markovian Jump Systems With Sensor Saturations and Randomly Varying Nonlinearities

This paper addresses the fault detection problem for discrete-time Markovian jump systems with incomplete knowledge of transition probabilities, randomly varying nonlinearities and sensor saturations. For the Markovian mode jumping, the transition probability matrix is allowed to have partially unknown entries, while the cases with completely known or completely unknown transition probabilities are also investigated as two special cases. The randomly varying nonlinearities and the sensor saturations are introduced to reflect the limited capacity of the communication networks resulting from the noisy environment, probabilistic communication failures, measurements of limited amplitudes, etc. Two energy norm indices are used for the fault detection problem in order to account for, respectively, the restraint of disturbance and the sensitivity of faults. The purpose of the problem addressed is to design an optimized fault detection filter such that 1) the fault detection dynamics is stochastically stable; 2) the effect from the exogenous disturbance on the residual is attenuated with respect to a minimized <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm; and 3) the sensitivity of the residual to the fault is enhanced by means of a maximized <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm. The characterization of the gains of the desired fault detection filters is derived in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programme method. Finally, a simulation example is employed to show the effectiveness of the fault detection filtering scheme proposed in this paper.

Combining airborne lidar and Landsat ETM+ data with photoclinometry to produce a digital elevation model for Langjökull, Iceland

An incomplete airborne lidar survey of Langjökull, Iceland's second largest ice cap (˜900 km2) and the surrounding area was undertaken in August 2007. Elevation data were interpolated between the lidar swaths using the technique of photoclinometry (PC), which relates Sun-parallel slope angles to image brightness. A Landsat Enhanced Thematic Mapper Plus (ETM+) image from March 2002 was used for this purpose. Different bands and band combinations were assessed and Band 4 (760–900 nm) was found to be the most appropriate. Parameters in the slope–brightness equation were derived empirically by comparing the image brightness with lidar elevation data in a 4 km × 4 km region in the centre of the ice cap. This relationship was then used to calculate the slopes, and, by integration between tie points of known lidar elevation, the elevations of the 30 m pixels that were not surveyed by lidar. The root-mean-square (RMS) precision (repeatability) of lidar elevations was 0.18 m and the accuracy was estimated to be 0.25 m. The 68.3% quantile of absolute difference relative to lidar (analogous to root-mean-square error (RMSE)) of all interpolated areas where PC assumptions are met was 5.44 m (4.66 m and 8.73 m for on- and off-ice areas, respectively). Where one or more PC assumptions were not met (e.g. self-shading, sensor saturation), the 68.3% quantile of absolute difference relative to lidar was 27.89 m (18.52 m on the ice cap and 32.91 m off-ice). These accuracies were applicable to 63%, 31%, and 6% of the ice cap and 59%, 28%, and 13% of the final digital elevation model (DEM), respectively. The area-weighted average 68.3% quantiles were 2.89 m for the ice cap and 6.75 m for the entire DEM. The PC technique applied to satellite imagery is a useful and appropriate method for interpolating a lidar survey of an ice cap.