• Journal of Institute of Control, Robotics and Systems
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• v.6 no.10
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• pp.923-927
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• 2000

This paper presents a relationship between DOP for TOA and TDOA is defined using the error covariance matrix of TDOA. It is analytically shown that the error ellipsoid of TOA is as same as that of TDOA in magnitude and in orientation, which means that DOP for TOA is identical to the DOP for TDOA. By computer simulation, the positioning performance of two methods is compared, and we verify our assertion.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.675-686
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• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.513-518
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• 1987

In this paper the error equations of the SDINS aligned by the gyrocompass are derived considering that the alignment errors are correlated to the sensor errors. Also the navigation errors due to the correlated errors are simulated by this error equations. The simulations are performed by the covariance analysis method, assumed all the sensor errors are random constants. The simulation results show that while the INS maintains the alignment attitude the cancellation takes place between the correlated errors, but once the INS changes attitude this cancellation effect is perturbed.

• Journal of Ocean Engineering and Technology
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• v.31 no.4
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• pp.315-323
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• 2017

This paper presents an integrated navigation system that combines ultra-short baseline (USBL), Doppler velocity log (DVL), and heading measurements for a deep-sea remotely operated vehicle, Hemire. A navigation model is introduced based on the kinematic relation of the position and velocity. The system states are predicted using the navigation model and corrected with the USBL, DVL, and heading measurements using the Kalman filter. The performance of the navigation system was confirmed through re-navigation simulations with the measured data at the Southern Mariana Arc submarine volcanoes. Based on the characteristics of the measurements, the design process for the parameters of the system modeling error covariance, measurement error covariance, and initial error covariance are presented. This paper reviews the influence of the outliers and blackout of the USBL and DVL measurements, and proposes an outlier rejection algorithm that is robust to USBL blackout. The effectiveness of the method is demonstrated with re-navigation for the data that includes USBL blackouts.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.83-90
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• 2003

This paper presents an underwater hybrid navigation system for a semi-autonomous underwater vehicle (SAUV). The navigation system consists of an inertial measurement unit (IMU), and a Doppler velocity log (DVL), accompanied by a magnetic compass. The errors of inertial measurement units increase with time, due to the bias errors of gyros and accelerometers. A navigational system model is derived, to include the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 20. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors, and correct the state equation when the measurements are available. Simulation was performed with the 6-d.o,f equations of motion of SAUV, using a lawn-mowing survey mode. The hybrid underwater navigation system shows good tracking performance, by updating the error covariance and correcting the system's states with the measurement errors from a DVL, a magnetic compass, and a depth sensor. The error of the estimated position still slowly drifts in the horizontal plane, about 3.5m for 500 seconds, which could be eliminated with the help of additional USBL information.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1230-1242
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• 2007

This paper describes an IIR/FIR fusion filter for a nonlinear system, and analyzes the stability of the fusion filter. The fusion filter is applied to INS/GPS integrated system, and the performance is verified by simulation and experiment. In the fusion filter, an IIR-type filter (SPKF) and FIR-type filter (MRHKF filter) are processed independently, then the two filters are merged using the mixing probability calculated using the residuals and residual covariance information of the two filters. The merits of the SPKF and the MRHKF filter are embossed and the demerits of the filters are diminished via the filter fusion. Consequently, the proposed fusion filter has robustness against to model uncertainty, temporary disturbing noise, large initial estimation error, etc. The stability of the fusion filter is verified by showing the closeness of the states of the two sub filters in the mixing/redistribution process and the upper bound of the error covariance matrices. This fusion filter is applied into INS/GPS integrated system, and important factors for filter processing are presented. The performance of the INS/GPS integrated system designed using the fusion filter is verified by simulation under various error environments and is confirmed by experiment.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2437-2442
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• 2013

OFDM Wi-Fi AP is susceptible to the co-channel interference. As a countermeasure, the insertion of a smart has been addressed. Despite of the guaranteed efficiency, the complexity of the post-FFT algorithm often keeps itself from being selected as the countermeasure. Instead, simply constructed pre-FFT smart antenna of which the algorithm is based on the received signal covariance matrix is commonly used and the mathematical modeling of it has been deployed. Computer simulations evaluating the improved BER characteristics of the proposed pre-FFT using the covariance matrix of channel estimator output have been carried out. It has been demonstrated that channel matrix output based smart antenna is superior to that using received signal covariance matrix.

• Journal of Advanced Navigation Technology
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• v.21 no.1
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• pp.138-144
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• 2017

A method for improving the integrated navigation performance in the INS/GPS navigation system by the considering that the condition that the geometric arrangement of the satellite is degraded due to limitation of the line of sight of the satellite such as geographic feature and GPS signal jamming is proposed. A variable covariance extended Kalman filter (VCEKF) that correlated to the measured covariance to the DOP of GPS is suggested. The navigation performance of integrated navigation system using EKF and VCEKF is analyzed by Monte-Carlo simulations. The result is verified that VCEKF has better estimation performance than EKF using fixed covariance on condition that DOP value is larger than the smaller value.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.