• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.471-478
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• 2012

A foot motion is estimated using an inertial sensor unit, which is installed on a shoe. The inertial sensor unit consists of 3 axis accelerometer and 3 axis gyroscopes. Attitude and position of a foot are estimated using an inertial navigation algorithm. To increase estimation performance, a smoother is used, where the smoother employs a forward and backward filter structure. An indirect Kalman filter is used as a forward filter and backward filter. A new combining algorithm for the smoother is proposed to combine a forward indirect Kalman filter and a backward indirect Kalman filter. Through experiments, the estimation performance of the proposed smoother is verified.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1120-1125
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• 2000

This paper presents a technique for estimating the attitude of a model helicopter at near hovering using a combination of inertial and non-inertial sensors such as gyroscope and potentiometer. To estimate the attitude of helicopter a simplified indirect Kalman filter based on sensor modeling is derived and the characteristics of sensors are studied, which are used in determining the optimal Kalman gain. To verify the effectiveness of the proposed algorithm simulation results are presented with real flight data. Our approach avoids a complex dynamic modeling of helicopter and allows for an elegant combination of various sensor data with different measurement frequencies. We also describe the method of implementation of the algorithm in the model helicopter.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.827-835
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• 1999

In this paper, a dead reckoning navigation system for differential drive mobile robots is presented. The navigation system consists of two incremental encoders and a gyroscope. We have built a third order polynomial function for compensating the nonlinear scale factor errors of the gyroscope. We utilize an indirect Kalman filter that feeds back estimated errors to the main navigation system. Also, the observability of the filter is analyzed in order to systematically evaluate the filter's performance. Experimental results show that the proposed navigation system provides a reliable position and heading angle by mutually compensating the encoder and the gyroscope errors. The proposed filter also reduces the computational burden and enhances the navigation system's reliability. The observability analysis confirms the characteristics of inevitably unbounded position error growth in dead reckoning navigation systems.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.6
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• pp.373-379
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• 2015

A hybrid navigation system is implemented to apply for a mobile robot. The hybrid navigation system consists of an inertial navigation system and a global positioning system. The inertial navigation system quickly calculates the position and the attitude of the robot by integrating directional accelerations, angular speed, and heading angle from a strap-down inertial measurement unit, but the results are available for a short time since it tends to diverge quickly. Global positioning system delivers position, heading angle, and traveling speed stably, but it has large deviation with slow update. Therefore, a hybrid navigation system uses the result from an inertial navigation system and corrects the result with the help of the global positioning system where an indirect feedback Kalman filter is used. We implement and confirm the performance of the hybrid navigation system through driving a car attaching it.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.25-32
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• 2017

Most of weapon systems use aided navigation system which integrates inertial navigation and aiding sensors to compensate the INS errors increasing with the passage of time. Various aid sensors can be applied such as Global Navigation Satellite System (GNSS), radar, barometer, etc., but there might exist time delay caused by signal processing or transferring aid information. This time delay leads out-of-sequence measurements (OOSM) systems. Previously, optimal and suboptimal measurment update method for OOSM systems, where the time delay length are known, are proposed. However, previous algorithm does not guarantee the positive definite property of covariance matrix. In order to improve numerical stability for aided navigation using delayed-measurement, this paper proposes a new measurement covariance update algorithm be similar to Joseph-form in Kalman filter. Futhermore, we propose how to implement it in indirect feedback Kalman filter structure, which is commonly used in aided navigation systems, for time-delayed measurement systems. Simulation and vehicle test results show effectiveness of a proposed algorithm.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.488-492
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• 1998

Indirect sensing of the rotor position of permanent magnet brushless DC motors contains position error. Such measuring error can be attenuated by adopting Kalman filter. In this paper, the cause of measuring error is analyzed and the design technique of Kalman filter is described. Experimental results show that the proposed sensorless drive exerts superior performances.

• Journal of Power System Engineering
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• v.5 no.3
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• pp.95-103
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• 2001

This study is applied to common induction motor, and vector control is realized by using an indirect type of induction motor which has a simple composition. In this study extended Kalman filter is used from control theoretical viewpoint, and primary resistance and secondary resistance which change according to the temperature of motor are simultaneously estimated. This paper aims to research an indirect vector control in which the secondary resistance obtained from this estimation is consistent with secondary flux. This estimation is made by on-line estimation, but on-line estimation is difficult because extended Kalman filter takes long time in computation time. So off-line estimation was made on the assumption that the variation of temperature in motor is slow temporally.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.262-264
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• 2005

We propose a filter design method to suppress the effect of gyroscope mixed random errors at INS system level. It is based on the result that mixed random errors can be represented by a single equivalent ARMA model. At first step, the time difference of equivalent ARMA process is performed, which consider the characteristic of indirect feedback Kalman filter used in INS filter. Next, a state space conversion of time differenced ARMA model is achieved. If the order of AR is greater than that of MA, the controllable or observable canonical form is used. Otherwise, we introduce the state equation of which the state variable is composed of the ARMA model output and several step ahead predicts of that. At final step, a complete form state equation is presented. The simulation results shows that the proposed method gives less transient error and better convergence compared to the conventional filter which assume the mixed random errors as white noise.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.774-781
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• 2013

The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The proposed approach relies on a VKF, augmented by a altitude from Echo-sounder based switching architecture to yield robust performance, even when DVL (Doppler Velocity Log) exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) Indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate proposed method, we compare the results of the VKF aided navigation system with simulation result from a PINS (Pure Inertial Navigation System) and conventional INS-DVL method. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.