• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.10
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• pp.928-935
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• 2000

In order to improve the performance of a GPS/DR integration system, the error sources of DR sensors should be modeled accurately, This results in the increases in the dimension of the integration filter and, consequently, computational load becomes large. To reduce the computational load, suggested in this paper is a decoupled GPS/DR integration scheme that consists of two cascaded Kalman filters. The GPS velocity output is used in the first filter to calibrate the DR sensor and to fix the velocity as well. The velocity from the first filter is fed to the second filter where the position is corrected using the GPS position output. Experimental results show that the proposed integration scheme has positioning performance comparable to the conventional coupled one, while its computation is reduced to about 2/3.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.780-788
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• 2006

This paper deals with INS/GPS tightly coupled integration algorithms using extend Kalman filter (EKF) and unscented Kalman filter (UKF). In the tightly coupled approach, nonlinear pseudorange measurement models are used for the INS/GPS integration Kalman filter. Usually, an EKF is applied for this task, but it may diverge due to poor functional linearization of the nonlinear measurement. The UKF approximates a distribution about the mean using a set of calculated sigma points and achieves an accurate approximation to at least second-order. We introduce the generalized scaled unscented transformation which modifies the sigma points themselves rather than the nonlinear transformation. The generalized scaled method is used to transform the pseudo range measurement of the tightly coupled approach. To compare the performance of the EKF- and UKF-based tightly coupled approach, real van test and simulation have been carried out with feedforward and feedback indirect Kalman filter forms. The results show that the UKF and EKF have an identical performance in case of the feedback filter form, but the superiority of the UKF is demonstrated in case of the feedforward filer form.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.817-823
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• 2007

GPS and INS can be integrated in 3 ways of loose, tight, and ultra-tight configuration. This paper investigates the performance of GPS/INS ultra-tightly integrated system when unscented Kalman filter(UKF) is adopted as well as extended Kalman filter(EKF). Covariance analysis is performed using UFK and EKF for tightly coupled and ultra-tightly coupled systems. Various trajectories such as straight, circle, S-shape, spiral are considered for the simulations of covariance analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.688-693
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• 2007

The INS/GPS integration system is designed by employing an adaptive filter that can estimate the measurement noise variance using the residual of the filter. To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulation is performed by assuming that GPS information has large position errors. Simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than those with the Extended Kalman Filter.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.121-125
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• 2006

A new GPS receiver correlator for the deeply-coupled GPS/INS integration system is proposed in order to the computation time problem of the Kalman filter. The proposed correlator consists of two early, prompt and late arm pairs. One pair is for detecting data bit transition boundary and another is for the correlator value calculation between input and replica signal. By detecting the data bit transition boundary, the measurement calculation time can be made longer than data bit period. As a result of this, the computational time problem of the integrated Kalman filter can be resolved. The validity of the proposed method is given through computer simulations.

• Journal of Sensor Science and Technology
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• v.25 no.3
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• pp.202-207
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• 2016

Estimation of vertical position is critical in applications of sports science and fall detection and also controls of unmanned aerial vehicles and motor boats. Due to low accuracy of GPS(global positioning system) in the vertical direction, the integration of IMU(inertial measurement unit) with the GPS is not suitable for the vertical position estimation. This paper investigates an IMU-barometer integration for estimation of vertical position (as well as vertical velocity). In particular, a new two-step Kalman/complementary filter is proposed for accurate and efficient estimation using 6-axis IMU and barometer signals. The two-step filter is composed of (i) a Kalman filter that estimates vertical acceleration via tilt orientation of the sensor using the IMU signals and (ii) a complementary filter that estimates vertical position using the barometer signal and the vertical acceleration from the first step. The estimation performance was evaluated against a reference optical motion capture system. In the experimental results, the averaged estimation error of the proposed method was 19.7 cm while that of the raw barometer signal was 43.4 cm.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.140-145
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• 2013

This paper proposes an efficient integration method for GPS (Global Positioning System) and INS (Inertial Navigation System). To obtain accuracy and computational conveniency at the same time with low cost global positioning system receivers and micro mechanical inertial sensors, a new mechanization method and a new filter architecture are proposed. The proposed mechanization method simplifies velocity and attitude computation by eliminating the need to compute complex transport rate related to the locally-level frame which continuously changes due to unpredictable vehicle motions. The proposed filter architecture adopts two heterogeneous filters, i.e. position-domain Hatch filter and velocity-aided Kalman filter. Due to distict characteristics of the two filters and the distribution of computation into the two hetegrogeneous filters, it eliminates the cascaded filter problem of the conventional loosly-coupled integration method and mitigates the computational burden of the conventional tightly-coupled integration method. An experiment result with field-collected measurements verifies the feasibility of the proposed method.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.3
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• pp.107-117
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• 2019

When a vehicle moves with a high rotation rate, it is not easy to measure the angular velocity using an off-the-shelf gyroscope. If the angular velocity is estimated using the extended Kalman filter in the gyro-free inertial navigation system, the effect of the accelerometer error and initial angular velocity error can be reduced. In this paper, in order to improve the navigation performance of the gyro-free inertial navigation system, an angular velocity estimation method is proposed based on an extended Kalman filter with an accelerometer random bias error model. In order to show the validity of the proposed estimation method, angular velocities and navigation outputs of a vehicle with 3 rev/s rotation rate are estimated. The results are compared with estimates by other methods such as the integration and an extended Kalman filter without an accelerometer random bias error model. The proposed method gives better estimation results than other methods.