• International Journal of Control, Automation, and Systems
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• v.1 no.4
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• pp.444-452
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• 2003

An integration system with multi-measurement sets can be realized via combined application of a centralized and federated Kalman filter. It is difficult for the centralized Kalman filter to remove a failed sensor in comparison with the federated Kalman filter. All varieties of Kalman filters monitor innovation sequence (residual) for detection and isolation of a failed sensor. The innovation sequence, which is selected as an indicator of real time estimation error plays an important role in adaptive mechanism design. In this study, the centralized Kalman filter with adaptive measurement fusion is introduced by means of innovation sequence. The objectives of adaptive measurement fusion are automatic isolation and recovery of some sensor failures as well as inherent monitoring capability. The proposed adaptive filter is applied to the GPS/SDINS integration system with an additional sensor. Simulation studies attest that the proposed adaptive scheme is effective for isolation and recovery of immediate sensor failures.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.223-229
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• 2017

An angle error is a factor obstructing to track accurate position in tracking radars. And the noise incurring the angle error can be divided as follows; thermal noise and glint. In general, Extended Kalman filter used in tracking radars is designed with considering thermal noise only. The Extended Klaman filter uses a fixed measurement error covariance when updating an estimate state by using ahead state and measurement. But, a noise power varies according to the range. Therefore we purposes the adaptive Kalman filter which changes the measurement noise covariance according to the range. In this paper, we compare the performance of the Extended Kalman filter and the proposed adaptive Kalman filter by considering KSLV-I (Korean Satellite Launch Vehicles).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.39-42
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• 1999

Adaptive array is using the array of antenna elements spatially and its output is the sum of each antenna elements output signal which is multiplied by the controlled weight coefficients corresponding to each elements. In this paper, for the BPSK and BFSK signals with S/I=2, S/N=10 is applied to the linear array antenna using the LMS & the Kalman filter algorithm. For the 4 elements equidistance linear array antenna system LMS and Kalman filter algorithm was used as the adaptive instruction principles and the application results to the constant amplitude envelope signals such as BPSK or BFSK can be seen that the computer simulation results are very fast in the convergence characteristics of directional patterns and the signal following characteristics.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.41-43
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• 2004

This paper is concerned with attitude estimation using low cost, small-sized accelerometers and gyroscopes. A two step extended Kalman filter is proposed, which adaptively compensates external acceleration. External acceleration is the main source of estimation error. In the proposed filter, direction of external acceleration is estimated. According to the estimated direction, the accelerometer measurement covariance matrix of the two step extended Kalman filter is adjusted. The proposed algorithm is verified through experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.187-191
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• 1988

To track the manenvering target and to derive the Filter using state estimation and information in real time, we derive adaptive Kalman Filter which reinitialize the internal filter mode.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.1
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• pp.114-122
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• 2009

An adaptive Kalman filter is designed as a post-navigation filter to improve the accuracy of GPS receiver's navigation performance in high dynamic environments. Not only the adaptive Kalman filter reduces the large noise error of navigation data which is obtained by least square method, but also the filter is not degraded as normal Kalman filter in high acceleration movements because the system noise is estimated. Also an initialization structure of the filter is desisted in consideration for irregular output condition of navigation data by least squared method such as reacquisition status in GPS receiver. The filter performance is verified by GPS simulator which has the simulation capability of high velocity and acceleration. Finally, a vehicle test including DGPS is executed to conform the real improvement of that filter performance. This filter can be applied to various data measurement systems to improve accuracy in high dynamic conditions besides GPS receiver.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.59-60
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• 2016

Flooded lead acid batteries are still very popular in the industry because of their low cost as compared to their counterparts. State of Charge (SOC) estimation is of great importance for a flooded lead acid battery to ensure its safe working and to prevent it from over-charging or over-discharging. Different types of Kalman Filters are widely used for SOC estimation of batteries. The values of process and measurement noise covariance of a filter are usually calculated by trial and error method and taken as constant throughout the estimation process. While in practical cases, these values can vary as well depending upon the dynamics of the system. Therefore an Adaptive Unscented Kalman Filter (AUKF) is introduced in which the values of the process and measurement noise covariance are updated in each iteration based on the residual system error. A comparison of traditional and Adaptive Unscented Kalman Filter is presented in the paper. The results show that SOC estimation error by the proposed method is further reduced by 3 % as compared to traditional Unscented Kalman Filter.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.12
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• pp.908-917
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• 1987

Most of moving targets are modelled as nonlinear dynamic equations. In recent years, the extended Kalman filter is frequently used for estimating their behaviors. The conditional Gaussian filter is more suitable than extended kalman filter in the filtering problem of nonlinear systems. But extended Kalman filter and conditional Gaussian filter often do not give optimal estimates and fail to track target trajectories because of its properties. Therefore it is desirable to use adaptive techniques to adapt target maneuvers. In this paper, we will discuss adaptive filtering technique using innovation process based on extended Kalman filter in real time, and suggest another maneuver estimation method using MRAS technique.

• Journal of the Korea Institute of Military Science and Technology
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• v.1 no.1
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• pp.201-210
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• 1998

Inertial Navigation System(INS) can provide the vehicle position and velocity information using inertial sensor outputs without the use of external aids. Unfortunately INS navigation error increases with time due to inertial sensor errors, and therefore it is desirable to combine INS with external aids such as GPS, TACAN, OMEGA, and etc.. In this paper we propose an integration algorithm of commercial GPS/GLONASS and INS where an adaptive filter for signal processing of GPS/GLONASS receiver and the 12th order Kalman filter for aided strapdown INS(SDINS) we employed. Simulation results show that the proposed adaptive filter can effectively remove a randomly occurring abrupt jump due to sudden corruption of the received satellite signal and that the Kalman filter performs satisfactorily.

• Journal of the Optical Society of Korea
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• v.20 no.3
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• pp.368-380
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• 2016

There is no doubt that adaptive optics (AO) is the most promising method to compensate wavefront disturbance in free space optics communication (FSO). In order to improve the performance of the AO system described by discrete-time linear system model with time-delay and implicit phase turbulent model, new controllers based on a Kalman filter and its extensions are proposed. Based on the standard Kalman filter, we propose a fading memory filter to deal with the ruleless strong interference; sequential and U-D filters are applied to reduce implementation complexity for the embedded controllers. Theoretical analysis and the numerical simulations show that the proposed fading memory filter can upgrade the performance for AO systems in consideration of the unforeseen strong pulse interference, and the sequential and U-D filters perform well compared with a Kalman filter.