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Integrated motion measurement illustrated by a cantilever beam
Abstract
The combination of inertial sensors and satellite navigation receivers like those of GPS (Global Positioning System) represents a very typical integrated navigation system. Integrated navigation is the most common example of integrated motion measurement determining the translational and angular position, velocity, and acceleration of a vehicle. Traditionally, this object is assumed to be a rigid body and the signals of its closely spaced sensors are referenced to a single point of the structure. During periods of low vehicle dynamics such common navigation systems typically show stability problems due to a loss of observability of some of the motion variables.
The range of applications for integrated navigation systems can be expanded due to the continuously increasing performance of data processing and cheap sensors. Further, it can be shown that the stability of such a navigation system (i. e. of the motion observer employed for the system, typically a Kalman filter) can be sustained by distributing appropriately additional sensors over the vehicle structures at distinct locations. This comprises the compensation of drift effects of the system by adding sensors that are drift-free and the guarantee of the observability of all estimated motion components. Large structures like airplanes, space stations, skyscrapers, and tower cranes with distributed sensors, however, have to take the flexibility of the structure into account. This includes an appropriate kinematical model of the structure. In this case, the theory of integrated systems has to be expanded to flexible structures. On the other hand, the additional system information obtained can be used not only for vehicle guidance but also for structural control.
Within this work individual kinematical models especially of a cantilever beam, idealizing e.g. the wing of an airplane, are developed and investigated with regard to the observability of the motion variables to guarantee a stable integrated system behaviour. Finally, the application and verification of integrated measurement systems for flexible structures is shown by experiments.
... In another approach, a non-classical micro-beam is studied to find that measuring the moment at the root of the beam produces an exactly observable system [7]. For truncated finite dimensional representations of such mechanical continuum systems,Örtel [8] considered optimal placement of an accelerometer, gyro, and strain gauge on a cantilever beam with respect to a penalty function designed to minimize the contributions of the fourth and fifth modes while promoting the first three modes. Work by Menuzzi [9] used finite element analysis modeling to place piezoelectric elements on a cantilever beam, optimizing the trace of the linear observability Gramian via topology optimization that measures sensitivity through the derivative of the Lyapunov equation. ...
... The truncated system, Σ n , is LTI, so the analytical observability Gramian can be calculated with (9) and the observability matrix with (8). ...
... Proof: Assume we have a single measurement and denote c ′ as first n elements of c i in (7), so y = c ′ 0 and the observability matrix (8) can be calculated as ...
Working from an observability characterization based on output energy sensitivity to changes in initial conditions, we derive both analytical and empirical observability Gramian tools for a class of continuum material systems. Using these results, optimal sensor placement is calculated for an Euler-Bernoulli cantilever beam for the following cases: analytical observability for the continuum system and analytical observability for a finite number of modes. Error covariance of an Unscented Kalman Filter is determined for both cases and compared to randomly placed sensors to demonstrate effectiveness of the techniques.
... Wagner [3] worked on a general framework for distributed sensors and flexible structures, with preceding publications dealing with improving the accuracy by arranging several IMUs over the structure of the navigated body [4] or by improving the system architecture [5]. Building on this, Örtel proposed an integrated motion measurement system (IMMS) for flexible structures [6] with sensor placement approaches, simulative proof, and a small scale experimental validation of a clamped cantilever beam [7]. This included the expansion of kinematical models of the structure and a comparison of peripheral sensor types [8]. ...
... The flexure of the wing at any time can then be represented by a superposition of each involved unit deformation ( = 1, … , Γ) times the current value of the associated deformation variable as indicated in Figure 3, with Γ being thus the number of considered unit-deformations. A useful but not mandatory choice for unit-deformations are the modeshapes of the structure [6][7][8]. ...
... The measurements of the additional peripheral accelerometers or gyroscopes expand the input vector . In general the number of peripheral sensors must be equal or bigger than the number of considered unit deformations Γ to solve the resulting system of equations [6][7][8]. ...
The fundamental principle of integrated motion measurement (IMM) is integrated navigation with inertial sensors combined with, for example, a satellite navigation receiver. Accordingly, IMM makes use of the specific advantages of complementary sensors and blends them in a filter algorithm. To meet requirements in advanced motion measurements, for instance, for structural health monitoring or for structural control purposes, the approach of a single rigid body like in classical navigation no longer holds for IMM. As a solution, additional degrees of freedom (DOFs) for the moving structure are introduced, which represent deformations and allow the navigated body to be treated as a flexible structure. To cover a variety of flexible deformation shapes, a sufficient number of distributed inertial sensors is required. To restrict accumulating errors of these sensors, additional structural measurements for aiding are necessary. The signals of the inertial sensors and the aiding measurements are fused by an extended Kalman filter (EKF) to obtain an optimal estimation of the usual navigation states, extended by the deformation variables. The contribution presents the preparation of the experimental validation of an IMM system for a flexible structure, which represents an idealization of a wing or rotor blade by a movable beam. The mechanical and electrical setup of a test rig is described. Furthermore, the simulation of the test configuration, that is the model of a test beam with a variety of distributed sensors for generating artificial measurements as a test reference , is discussed. Finally, for an optimal sensor placement, the two methods of effective independence and maximization of modal energy are compared and experimentally tested for different amounts of additional flexible DOFs.
... For example, in references [22,23], the specific points in the measurement bridge where the sensors cannot be installed are used to obtain the vibration information of the measured object by fusing acceleration sensors and resistive strain gauges, and the laser displacement sensors are used as reference sensors to achieve the estimation of the unmeasurable points. In reference [24], to improve the control accuracy, the motion displacement of a flexible beam is obtained through the resistance strain gauge, acceleration sensor and gyroscope as the system feedback inputs. However, the local sensors in such applications exhibit low measurement accuracy and are not suitable for the ultra-precise motion systems. ...
This paper proposes a POI displacement estimation method based on the functional optical fiber sensor and the phase modulation principle to improve the POI displacement estimation accuracy. First, the relation between the object deformation and the optic fiber lightwave phase is explained; the measurement principle of functional optical fiber sensor based on the heterodyne interference principle and its layout optimization method is proposed, and a POI displacement estimation model is presented based on the data approach. Secondly, a beam is taken as the simulation object, the optimal position and length of the optical fiber sensor are determined based on its simulation data. Finally, the experimental device is designed to verify the effectiveness of the POI displacement estimation method based on the optic fiber sensors. The frequency-domain plot of the signals shows that the optical fiber sensors can express the flexible deformation of the analyzed object well. The POI displacement estimation model with the fiber optic sensor signals as one of the inputs is constructed. Through estimating the test data, the error using the optical fiber sensor-based POI displacement estimation method proposed in this paper reduces by more than 61% compared to the rigid body-based assumption estimation method.
... Inertial methods, accelerometry being most common, capture the dynamic motions of a structure and, through time-integration or the assumption of harmonic motion, produce estimated velocities and positions (Ortel et al., 2013). The most common application is experimental modal analysis, where the band-limited nature of inertial measurements is easily accounted-for. ...
Shape sensing can provide insight into the structural health and operating conditions of slender engineered lifting and control surfaces in aerospace and maritime applications. Shape sensing often relies upon digital image correlation, inertial measurement, or inverse finite element methods, which can be impractical in applications involving real-time reconstructions, static and dynamic deformations, or dynamic mode shape identification, especially outside of controlled laboratory environments. This work describes ongoing efforts to design, build, and validate a low-cost and robust tool for real-time shape sensing. The sensor consists of a simple aluminum spar, instrumented with strategically placed strain gauges. A kinematic model is used to reconstruct bi-axial bending and torsional displacements along the spar. The model is validated against FEM simulations and canonical analytical solutions. A prototype of the spar is benchmarked using a motion capture system. The pre-calibrated errors are correlated with the direction of bending, permitting a directionally compensative calibration scheme. After calibration of the spar and kinematic model, validation errors are 2.18% in bending magnitude and 0.97° in bending direction. This work addresses the emerging need for new low-cost sensors for structural health monitoring in environments where other sensing methods do not perform well.
... Inertial methods, accelerometry being most common, captures the dynamic motions of a structure and, through time-integration or the assumption of harmonic motion, relates them to dynamic deflections [6]. The most common application is experimental modal analysis, where the band-limited nature of inertial measurements is easily accounted-for. ...
Shape sensing refers to methods for inferring the deflections of flexible bodies. These deflections give important insight into the structural health and operating conditions of slender engineered surfaces in aerospace and maritime application. Shape sensing is often achieved using digital image correlation, inertial measurement, or inverse finite element method. These methods can be impractical in applications requiring real-time monitoring, static and dynamic deformation reconstruction, or dynamic mode shape identification, especially in settings outside of the lab, where poor optical access, limited knowledge of the structure's material properties, or harsh working environments present obstacles. The work presented describes ongoing efforts to design, build, and validate a low-cost and robust tool used for accurate shape sensing in real-time. The tool consists of a spar of simple geometry, instrumented with strategically placed strain gauges. A kinematic model is used to infer bi-axial bending and torsional displacements along the spar. The design of the shape sensing spar along with an enhanced kinematic model used for the reconstruction of bending and torsional deformations. The model is validates against FEM simulations and canonical analytical solutions. A prototype of the spar is benchmarked using an established commercially available motion capture system. While the results show god qualitative agreement, several error sources are identified. Average errors of 8.4% and 0.59˚in59˚in bending and torsion are present for the uncalibrated spar. The errors were found to be correlated with the direction of bending which can be used to inform future calibration to improve the spar performance. This work contributes to the emerging need for new low-cost structural health monitoring sensors in harsh or extreme environments where other sensing methods do not perform well.
... Conventional motors such as servo motor and stepper motor could no longer satisfy the current high requirements [3,4]. To solve this problem, many approaches have been explored by means of force which is converted through strain in materials, such as piezoelectric [5], magnetostrictive [6][7][8], shape-memory alloy [9,10], inchworm [11,12], surface tension [13,14], and thermal expansion [15,16]. Among them, due to the outstanding advantages of compact size, large output power, high precision, quick response and no magnetic field, piezoelectric components have been widely used [17,18]. ...
... In the previous work related to the integrated motion measurement approach for the one dimensional clamped beam by Örtel [52,53], EI and the penalty function approaches are used to find the optimal sensor locations. The penalty function method is an constrained optimization based approach, which minimizes the disturbance from non-target modes and makes sure that all target modes in the kinematical model are excited. ...
Complex lightweight structures tend to be significantly flexible and lightly damped,
where structural vibrations are often the source of performance degradation. Vibration
based real-time tasks like active damping or health monitoring of such
structures typically rely on an estimation of the disturbance response of the structure,
using only few number of sensor signals available. An appropriate method for
realizing this estimate is through measuring structural motions using distributed
sensors deployed in the structure of interest.
Combining different type of sensors to take advantage of the positive characteristics
of each instrument is often called sensor fusion and is the basis of an integrated
navigation system. A classical integrated navigation system can be established in
general as a state observer design and usually determines the spatial rigid body
motion of the structure or vehicle considered. However, extending such integrated
systems to flexible structures using distributed structural sensors is possible, which
was already attempted effectively for one dimensional continua, i.e., a flexible
beam. The theory needs to be extended to three-dimensional lightweight structures
to estimate the modal states of vibration. All such systems can be generalized
as integrated motion measurement systems.
In the scope of this thesis, an observer based on integrated motion measurement
method will be developed and simulated for the Stratospheric Observatory for
Infrared Astronomy (SOFIA). The performance of the large airborne telescope
structure of SOFIA is affected by vibrations induced mainly by aeroacoustic disturbances.
Distributed inertial sensors are used to obtain such motion signals with a
good time resolution, which are aided by strain gauges as long term reliable signals.
The sensor fusion approach to be developed here consists of a continuous-discrete
extended Kalman filter. Besides the sensor signals to be fused, the filter requires a
suitable kinematical model, which in turn determines the mechanical meaning of
ix
x
the measurements. The kinematic model will be reduced using a modal approach,
i.e., the kinematical model of SOFIA includes only vibration modes having high
contribution to the image jitter at the focal plane of the telescope.
There are no mass and stiffness properties needed directly in this approach. However,
the approximate knowledge of the modal properties of the structure is necessary
for the implementation of this method. A finite element model is chosen as
a basis to extract such modal properties.
This work also addresses several variations of distributed sensor systems depending
on the type of inertial sensors (accelerometers, gyros) and also the number of
target modes. Simulation results demonstrate the potential of the approach and
its feasibility in complex lightweight structures.
The paper presents a multi-objective optimization strategy for a multi-type sensor placement for Structural Health Monitoring (SHM) of long span bridges. The problem is formulated for simultaneous placement of strain sensors and accelerometers (heterogeneous network) based on application demands for SHM system. Modal Identification (MI) and Accurate Mode Shape Expansion (AMSE) were chosen as the application demands for SHM. The optimization problem is solved through the use of integer Genetic Algorithm (GA) to maximize a common metric to ensure adequate MI and AMSE. The performance of the joint optimization problem solved by GA is compared with other established methods for homogenous sensor placement. The results indicate that the use of a multi-type sensor system can improve the quality of SHM. It has also been demonstrated that use of GA improves the overall quality of the sensor placement compared to other methods for optimization of sensor placement.
The research presents a multi-objective optimisation problem for a multi-type sensor placement for Structural Health Monitoring (SHM) on a long span bridge. The problem is formulated for simultaneous placement of strain sensors and accelerometers (heterogeneous network) based on the
application demands for SHM system. The primary demands for SHM are Modal Identification (MI) and Accurate Mode Shape Expansion (AMSE). The optimisation problem is solved through the use of Integer Genetic Algorithm (GA) to maximise a common metric to ensure adequate MI and AMSE. The performance
of the joint optimisation problem solved by GA is compared with other established methods for homogenous sensor placement. The results indicate that the use of a multi-type sensor system improves the quality of SHM and the use of GA improves the overall quality of the sensor placement compared
to other methods for optimisation of sensor placement.
Integrated navigation, as an example of integrated motion measurement systems, is typically based on inertial sensors (accelerometers, gyros) and GPS and is commonly used for vehicle guidance. Traditionally, the vehicle of integrated navigation systems is assumed to be a rigid body. However, extending these integrated systems to elas-tic structures is possible. The considered continuum, in particular a flexible beam, is an attempt to obtain motion measurements of an aerofoil of a flying aircraft with large structural dimensions. Integrated systems considered here in general are fusing different measuring methods by using their benefits and blinding out their disadvan-tages. For instance, gyros and accelerometers are used to obtain reliable signals with a good time resolution. On the other hand, aiding sensors like radar units and strain gauges are known to be long-term accurate. The kernel of the integrated systems presented in this article consists, however, of an continuous-discrete extended Kalman filter estimating the motion state of the structure. Besides the sensor signals to be amalgamated, the filter requires a kinematical model of the vehicle motion considered, which has to be developed individually. This kinematical model describes the standardised dynamics of the vehicle by means of specific forces, i.e. accelerations, and of rotational velocities. Thus, there are no dynamometers or mass and stiffness properties needed in this approach. Diverse kinematical models of the beam were developed and evaluated, using model reduction in regard of the natural modes. Based on simulation the paper shows this approach, appropriate sensor sets, and estimated motion results of a flexible beam.
In this paper we consider structure-preserving model reduction of second-order systems using a balanced truncation approach. Several sets of singular values are introduced for such systems, which lead to different concepts of balancing and different second-order balanced truncation methods. A comparison of these methods with other second-order balanced truncation techniques is presented. We also show that, in general, none of the existing structure-preserving balanced truncation methods for second-order systems preserves stability in the reduced models. Numerical examples are given that demonstrate the properties of the new methods.
Precision displacement measurement is a critical element to achieve active control for high-precision (e.g. 5 μm) flexible line boring. Since current measurement methods are difficult to apply in this application, a new measurement method has been developed. This paper presents a new strain-gauge-based method for the high-precision vibration measurement of a beam. This method is based on the fact that the vibration displacement can be expressed in terms of an infinite number of vibration modes and be related to the measured strains through the strain–displacement relationship. By placing multiple sets of strain gauges, multiple modes can be taken into account to achieve high-precision measurement. Both simulation and experimental results were obtained and are provided for validation. From these results, it can be concluded that this new measurement method is applicable to high-precision flexible line boring, enabling active control to compensate for the tool-tip displacement to achieve a high-precision machining process.
Die vorliegende Arbeit beschäftigt sich mit Reduktionsverfahren für lineare, zeitinvariante Systeme zweiter Ordnung. Die hierfür betrachteten Verfahren beruhen auf dem Abgleich charakteristischer Parameter des ursprünglichen und des reduzierten Systems und erhalten die zweite Ordnung Struktur des Modells. Beim ersten Verfahren wird eine Projektion direkt auf das Originalsystem angewendet. Die Berechnung der Projektionsmatrizen mit Hilfe eines erweiterten Arnoldi- oder Lanczos-Algorithmus erfolgt im so genannten Krylov-Unterraum zweiter Ordnung. Bei der zweiten Methode wird das System in den Zustandsraum konvertiert, dort mit Hilfe einer Krylov-Unterraummethode reduziert und schließlich mit einer Ähnlichkeitstransformation wieder in ein System zweiter Ordnung überführt. Bei einer Ordnung des reduzierten Systems von Q können im SISO Fall mit der ersten Methode Q, mit der rechenaufwändigeren zweiten Methode hingegen bis zu 2Q Parameter zur Übereinstimmung gebracht werden.
The combination of inertial sensors and satellite navigation receivers has led to versatile low-cost integrated navigation systems. They are generally based on the assumption of a rigid vehicle, and on the assumption that all motion sensors are close together. The potential of modern microprocessors and the availability of inexpensive sensors suggest giving up this limitation. Accordingly, the range of application of integrated navigation systems enlarges considerably. However, such a step requires the generalization of the theoretical basis of the corresponding system design. The thesis deals with this subject.
Initially, the principle of integrated navigation systems is introduced by means of simple examples. In addition, the essential attribute is presented that serves as feature to tell apart different classes of application. It consists of two kinematical models adapted to the motion cases considered. Using real flight test data, a comparison between classical and newer approaches for the data fusion of inertial sensors and satellite navigation receivers follows. Purpose of this assessment is the selection of the basis for the intended generalization.
The generalization procedure is performed in two steps: At first the assumption of a rigid body remains, the sensors however are now partly distributed over the whole vehicle structure. The extension of the kinematical models, which have not to be restricted to vehicles but can also consider for example robots, forms the following second step. It is based on the description of vehicle structures with rigid and elastic areas. By means of simulation, the functioning of such generalized integrated systems is demonstrated.
--- Die Kombination von Inertialsensorik und Satellitennavigationsempfängern hat zu flexiblen und kostengünstigen integrierten Navigationssystemen geführt. Hierbei wird generell von der Vorstellung eines starren Fahrzeugs ausgegangen, dessen Bewegungssensoren zudem räumlich eng benachbart sind. Moderne, leistungsfähige Methoden der Datenverarbeitung und die Verfügbarkeit kostengünstiger Messgeber legen nahe, diese Einschränkung fallen zu lassen und damit den Anwendungsbereich integrierter Navigationssysteme erheblich zu erweitern. Dazu ist allerdings eine Verallgemeinerung der theoretischen Grundlage zur Auslegung derartiger Systeme erforderlich, womit sich die vorliegende Abhandlung befasst.
Zunächst wird an einfachen Beispielen das Grundprinzip integrierter Navigationssysteme vorgestellt und auf das wesentliche Unterscheidungsmerkmal verschiedener Anwendungsklassen hingewiesen, das in zwei jeweils verwendeten, angepassten kinematischen Modellen besteht. Anschließend folgt mit Hilfe von Flugversuchsdaten eine Bestandsaufnahme zu klassischen und neueren Varianten der Kopplung von Inertial- und Satellitennavigation, um diejenige auszuwählen, die sich für die beabsichtigte Generalisierung am besten eignet.
Die Verallgemeinerung wird in zwei Stufen durchgeführt: Zunächst bleibt die Annahme des starren Fahrzeugs noch bestehen, ein Teil der Sensoren wird jedoch so weit über die Struktur verteilt, wie es die Abmessungen zulassen. Die Erweiterung der kinematischen Modelle, das sich nicht nur auf Fahrzeuge beschränken müssen, sondern auch zum Beispiel Roboter berücksichtigen können, stellt dann den zweiten Schritt dar. Als Ausgangspunkt hierzu dient die Beschreibung von Strukturen mit starren und mit elastischen Teilbereichen. Mit Hilfe von Simulationsrechnungen wird die Funktionsfähigkeit derartiger integrierter Systeme gezeigt.
"Introduction to Optimum Design " is the most widely used textbook in engineering optimization and optimum design courses. It is intended for use in a first course on engineering design and optimization at the undergraduate or graduate level within engineering departments of all disciplines, but primarily within mechanical, aerospace and civil engineering. The basic approach of the text is to describe an organized approach to engineering design optimization in a rigorous yet simplified manner, illustrate various concepts and procedures with simple examples, and demonstrate their applicability to engineering design problems. Formulation of a design problem as an optimization problem is emphasized and illustrated throughout the text. Excel and MATLAB are featured throughout as learning and teaching aids. The 3rd edition has been reorganized and enhanced with new material, making the book even more appealing to instructors regardless of the level they teach the course. Examples include moving the introductory chapter on Excel and MATLAB closer to the front of the book and adding an early chapter on practical design examples for the more introductory course, and including a final chapter on advanced topics for the purely graduate level course. Basic concepts of optimality conditions and numerical methods are described with simple and practical examples, making the material highly teachable and learnable.Applications of the methods for structural, mechanical, aerospace and industrial engineering problems.Introduction to MATLAB Optimization Toolbox.Optimum design with Excel Solver has been expanded into a full chapter.Practical design examples introduce students to usage of optimization methods early in the book. New material on several advanced optimum design topics serves the needs of instructors teaching more advanced courses.
The backbone of aiding inertial navigation systems by satellite navigation receivers is the data fusion of the subsystem outputs. Its conventional mechanization emerged from the classical, necessary aiding of inertial navigation systems and is based on linearized error models. Recent approaches therefore use directly the nonlinear kinematical equations of the rigid body motion. These methods lead to a simpler system structure as well as to smaller estimation error variances. To verify this statement, the paper presents a systematization of the different fusion schemes and a comparison between results of postprocessed flight test data.
A method of sensor placement for the purpose of on-orbit modal
identification and test-analysis correlation is presented. The method is
an extension of the Effective Independence method presented in past work
to include the effects of a general representation of measurement noise.
Sensor noise can be distributed nonuniformly throughout the structure as
well as correlated between sensors. The only restriction is that the
corresponding noise covariance intensity matrix is positive definite.
The technique presented offers a fast and efficient approach for
reducing a relatively large initial candidate sensor location set to a
much smaller optimum set which retains the linear independence of the
target modes and maintains the determinant of the Fisher Information
Matrix resulting in improved modal response estimates. The noise
covariance intensity matrix which has been introduced into the method
can be thought of as a sensor weighting matrix which modifies the shape
of the target modes. The mode shape coefficients are modified based upon
the noise levels at the sensor locations. Inclusion of the noise model
results in higher ranking of sensor locations with low noise levels and
suppression of sensor locations with high noise levels. A criterion is
also presented which can be used during the course of the sensor
placement analysis to determine how many sensors are required to
maintain a desired level of signal-to-noise ratio over all the target
modes. Simple numerical examples are presented which clearly demonstrate
the ideas and trends presented in the paper.
An algorithm has been developed to determine the full deformation field of a cantilever honeycomb plate under arbitrary loading conditions. The algorithm utilizes strain information from a set of sixteen fiber Bragg grating sensors mounted on the plate so that all sensors measure strains along the clamped-free direction. The sensors were interrogated using a wavelength division multiplexing scheme. A two-dimensional polynomial function which represents the strain field was created using a least-squares surface-fitting algorithm. This function was integrated twice with the known boundary conditions applied to yield the deformation field for the plate. Finite-element comparisons were performed to test the accuracy of the strain-displacement algorithm. Single-point loading tests were also experimentally performed to further verify the accuracy of the algorithm.
During the last years integrated navigation systems based on gyros,accelerometers, and satellite navigation receivers became powerful,favourably priced devices for the guidance of aircraft and ships.Comparable equipment using especially wheel sensors exists for cars. Thekernel of such systems is a Kalman filter estimating the relevantvehicle motion. The filter design in turn requires a kinematical modelto settle on the motion components considered and to describe themechanical meaning of the measurements employed. Up to now, usual modelsrepresent a single rigid body with two, three or six degrees of freedom.
The assumption of a solitary rigid body reflects mainly classicalnavigation requirements, it is not a consequence of the basic concept ofintegrated navigation systems. In principle, determining the motion ofmechanical systems with other or with additional degrees of freedom ispossible if appropriate kinematical models and suitable sensorarrangements are available.
Based on the theory of integrated navigation systems, the paperdescribes the fundamentals of the design of integrated motionmeasurement systems for multibody structures. The approach isfurthermore illustrated by the example of a double pendulum with amovable inertial support and equipped with microelectromechanical gyrosand accelerometers as well as with radar units. The attachment of thesensors demonstrates that a measurement system layout does typically notrequire considerable modifications of already existing mechanicalassemblies. The performance of the integrated system for the pendulum isdemonstrated by means of simulated and of experimental sensor signals.
We compare two methods for monitoring the dynamic response of tall buildings to wind loading, using data from a 280-m-high
building in Singapore. The first method is based on accelerometer measurements. The second method is based on the Global Positioning
System (GPS) technology. The GPS can in principle detect absolute displacements with lower frequencies than the accelerometers,
but the GPS positioning signal is usually very noisy. We propose a systematic procedure for modeling the stochastic and systematic
components of the GPS displacement time series and for extracting the weak structural response from the dominant noise. The
spectrum of the building response obtained from the filtered GPS data exhibits a dominant peak at 0.19Hz. The frequency of
the peak coincides with that obtained from the analysis of the accelerometer data. The proposed analysis of the GPS signal
provides a method for cross-validating the GPS and accelerometer measurements, and shows that “educated” filtering of the
GPS signal can reveal essential features of the building’s response to wind loading.
Most microwave sensors in the commercial market make use of Doppler effect in order to determine motion, velocity and direction of motion. If fixed object range information is required, the FMCW approach would be applicable, however, the hardware and processing (both analog and digital) efforts are extensive, especially for short range applications. Thus, a new short range radar transceiver sensor architecture has been developed which is capable of precise detection of object range and velocity down to a 15 cm distance. The new architecture has a broad range of industrial and commercial measurement applications and is particularly well suited for automotive parking, `stop & go' and air-bag deployment utilization. This technical presentation explores the high resolution radar sensor architecture, the principles of operation, and the component development. The development of the dielectric resonator oscillator, sampling mixer, high speed modulator, and patch antenna are individually reviewed. Operational results are presented which demonstrate the sensors ability to resolve the detection of object range and velocity. The high resolution radar sensor illustrates one of many practical, commercial applications for M/A-COM's semiconductor and subsystems capabilities.
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