• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.593-607
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• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.11
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• pp.881-888
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• 2020

In this paper, research on estimation of states of a target of interest using Line Of Sight(LOS) angle measurement is performed. Target's position, velocity, and acceleration are chosen to be the states of interests. The LOS measurement is known to be highly non-linear, making target dynamic modeling hard to be implemented into a filter. To solve this issue, the Pseudomeasurement equation was applied to the LOS measurement equation. With the help of this equation, 3D variable turn target dynamic model is applied to the filter model. For better performance, Kinematic Constraint is also implemented into the filter model. As for the filter, Bias Compensation Pseudomeasurement Filter (BCPMF) is used which is known for its robustness to initial conditions. Moreover, Two-Stage Kalman Filter (TSKF) form was also implemented to benefit from the parallel computation. As a result, TBCPMF 3DVT-KC is proposed and simulated to assess performance.

• Smart Structures and Systems
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• v.17 no.4
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• pp.647-667
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• 2016

In this paper, dynamic displacement is estimated with high accuracy by blending high-sampling rate acceleration data with low-sampling rate displacement measurement using a two-stage Kalman estimator. In Stage 1, the two-stage Kalman estimator first approximates dynamic displacement. Then, the estimator in Stage 2 estimates a bias with high accuracy and refines the displacement estimate from Stage 1. In the previous Kalman filter based displacement techniques, the estimation accuracy can deteriorate due to (1) the discontinuities produced when the estimate is adjusted by displacement measurement and (2) slow convergence at the beginning of estimation. To resolve these drawbacks, the previous techniques adopt smoothing techniques, which involve additional future measurements in the estimation. However, the smoothing techniques require more computational time and resources and hamper real-time estimation. The proposed technique addresses the drawbacks of the previous techniques without smoothing. The performance of the proposed technique is verified under various dynamic loading, sampling rate and noise level conditions via a series of numerical simulations and experiments. Its performance is also compared with those of the existing Kalman filter based techniques.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.948-953
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• 2008

In high dynamic situations, the GPS carrier tracking loop requires a wide bandwidth to track a carrier signal because the Doppler frequency changes more rapidly with time. However, a wide bandwidth allows noises within the bandwidth of the tracking loop to pass through the loop filter. As these noises are used in the numerical controlled oscillator(NCO), the carrier tracking loop of a GPS receiver shows a degraded performance in high dynamic situations. To solve this problem, an adaptive two-stage Kalman filter, which offers the NCO a less noisy phase error, can be used. This filter is based on a carrier phase dynamic model and can adapt to an incomplete dynamic model and a quickly changed Doppler frequency. The performance of the proposed tracking loop is verified by several simulations.

• Smart Structures and Systems
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• v.15 no.1
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• pp.57-80
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• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.148-155
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• 2014

In this research a signal processing system is designed for detecting the ultrasonic signal envelope using Xilinx's Zynq SoC(system on chip). As a design tool, Vivado IDE(integrated design environment) is used to hierarchically design the whole signal processing system. The proposed system consists of a Zynq-internal ADC, an FIR(finite impulse response) BPF(band pass filter), an absolute value calculator, an FIR LPF(lpw pass filter), and the Kalman filter. Under this configuration, two design schemes, HW design scheme with LPF as a final stage and HW/SW co-design scheme with a Kalman filter as a final stage, are compared in terms of the performance and efficiency. As a result, envelope detecting performances of the two schemes are proved to be almost same, but the HW/SW co-design is verified to be much more efficient than the HW design considering the much smaller time consumption during system design.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.270-276
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• 2002

An integrated robust fault diagnosis and fault accommodation strategy for a class of linear stochastic systems subjected to unknown disturbances is presented under the assumption that only a single fault may occur at a given time. The strategy is based on the fault isolation and estimation using a bank of robust two-stage Kalman filters and introduction of the additive compensation input for cancelling out the fault's effect on the system. Each filter is set up such that the residual is decoupled from unknown disturbances and fault with the influence vector designed in the filter. Simulation results for the simplified longitudinal flight control system with parameter uncertainties, process and sensor noises demonstrate the effectiveness of the present approach.

• Smart Structures and Systems
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• v.26 no.6
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• pp.693-701
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• 2020

The conventional Kalman Filter (KF) provides a promising way for structural state estimation. However, the physical parameters of structural systems or models should be available for the estimation. Moreover, it is not applicable when the loadings applied to the structures are unknown. To circumvent the aforementioned limitations, a two-stage KF with unknown input approach is proposed for the simultaneous identification of structural parameters and unknown loadings. In stage 1, a modified observation equation is employed. The structural state vector is estimated by KF on the basis of structural parameters identified at the previous time-step. Then, the unknown input is identified by Least Squares Estimation (LSE). In stage 2, based on the concept of sensitivity matrix, the structural parameters are updated at the current time-step by using the estimated structural states obtained from stage 1. The effectiveness of the proposed approach is numerically validated via a five-story shearing model under random and earthquake excitations. Shaking table tests on a five-story structure are also employed to demonstrate the performance of the proposed approach. It is demonstrated from numerical and experimental results that the proposed approach can be used for the identification of parameters of structure and the external force applied to it with acceptable accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.167-170
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• 2002

In this paper, we propose a new tracking algorithm that achieves good tracking performance in manuevering targets while capping the computation load to“low”Kalman Filter (KF) is generally known to be poor in tracking manuevering targets. IMM, on the other hand, compensates the weakness inherent in the mundane KF and is considered as a promising alternative for tracking maneuvering targets. However, IMM suffers from substantially increased computational load as the number of models increases. To remedy this problem, we propose a new method focused to reducing the computational load and attaining the desirable tracking performance at least as good that of IMM. It is achieved by essentially adopting the structure of IMM and injecting Optimal Two-Stage Kalman Estimator (OTSKE). The representative simulation shows a reduction in computational load with the proposed OTSKE but further reduction is shown achieved (by about 58%) with the Interacting Acceleration Compenstation(IAC)-OTSKE approach.

• Aerospace Engineering and Technology
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• v.3 no.1
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• pp.179-187
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• 2004

We consider some problems of the Modified SPRT(Sequential Probability Ratio Test) method for fault detection and isolation of inertial redundant sensor systems and propose an Advanced SPRT method which solves the problems of the Modified SPRT method. The problems of the Modified SPRT method to apply to inertial sensor system come from the effect of inertial sensor errors and the correlation of parity vector components. We use a two-stage Kalman filter to remove effects of the inertial sensor errors and propose the modified parity vector and the controlled parity vector which reduces the effect of correlation of parity vector components. The Advanced SPRT method is derived form the modified parity vector and the controlled party vector. Some simulation results are presented to show the usefulness of the Advanced SPRT method to redundant inertial sensor systems.