• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.16-20
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• 2022

Accurate state estimation is important for autonomous driving. However, the estimation error increases in situations that a lot of longitudinal slip occurs. Therefore, this paper presents a vehicle state estimation method using an Extended Kalman Filter. The filter estimates the states of the host vehicle robust to wheel slip. It utilizes the measurements of the four-wheel rotational speeds, longitudinal acceleration, yaw-rate, and steering wheel angle. Nonlinear measurement model is represented by Ackermann Model. The main advantage of this approach is the accurate estimation of yaw rate due to the measurement of the steering wheel angle. The proposed algorithm is verified in scenarios of autonomous emergency braking (AEB), lane change (LC), lane keeping (LK) using an automated vehicle. The results show that the proposed algorithm guarantees accurate estimation in such scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.153-156
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• 1996

A new filtering algorithm for radar tracking is developed based on the fact that correct evaluation of the measurement error covariance can be made possible by doing it with respect to the Cartesian state vector. The new filter may be viewed as a modification of the extended Kalman filter where the variance of the range measurement errors is evaluated in an adaptive manner. The structure of the proposed filter allows sequential measurement processing scheme to be incorporated into the scheme, and this makes the resulting algorithm favorable in both estimation accuracy and computational efficiency.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.68-75
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• 2012

This paper presents a study on the driveline output torque estimation using a discrete Kalman filter. The in-situ output shaft torque is first measured by a non-contacting magneto-elastic torque transducer. The linear state-space system equations are first derived and the discrete Kalman filter is designed based on the Kalman filter theory to recover the driveline output torque contaminated by random noises. In addition to using torque measurement, the estimation of the output torque using two angular velocities: the output and wheel, is also conducted. The experimental results show that the discrete Kalman filter can be effective for not only removing the random noise in output torque but also estimating the output torque without torque measurement.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.35-41
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• 1993

Statistical calibration is a useful technique for achieving compatibility between two different measurement methods, and it usually consists of two steps : (1) estimation of the relationship between the standard and nonstandard measurements, and (2) prediction of future standard measurements using the estimated relationship and observed nonstandard measurements. A predictive multivariate errors-in-variables model is presented for the multivariate calibration problem in which the standard as well as the nonstandard measurements are subject to error. For the estimation of the relationship between the two measurements, the maximum likelihood (ML) estimation method is considered. It is shown that the direct and the inverse predictors for the future unknown standard measurement are the same under ML estimation. Based upon large-sample approximations, the mean square error of the predictor is derived.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.11
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• pp.493-495
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• 2003

Accurate temperature measurement is a key factor to implement the rapid thermal processing(RTP). A temperature estimation method using acoustic wave has been proposed to overcome the inaccuracy and contamination problem of the previous methods. The proposed method, however, may suffer from the offset and low resolution problem since it is implemented in the time domain. This paper presents a temperature estimation method using the phase detection of acoustic wave. Based on the frequency domain approach, the proposed technique increases the resolution of the measured temperature and reduces the effect of noise. We investigate the performance of the proposed method via experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.69.1-69
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• 2001

In this paper, observability properties of a multiantenna GPS measurement system for the estimation of errors in INS are presented. It is shown that time-invariant INS error models are observable with measurements from at least three GPS antennas on the vehicle. There is at least one unobservable mode with two antennas. There are three unobservable modes with one antenna. It is also shown that time-varying INS error models are instantaneously observable with measurements from three GPS antennas. A numerical simulation results are given to verify the effectiveness of the multiantenna measurement system on the INS error estimation. In the simulation, a GPS measurement system is considered in which a trade-off between computational load and accuracy of estimation is achieved.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.451-459
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• 2008

Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple local optimal solutions may exist and local methods can be trapped in a local optimal solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple local optimal solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.845-851
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• 2012

Power system loads have a significant impact on a system. Although it is difficult to precisely describe loads in a mathematical model, accurately modeling them is important for a system analysis. The traditional load modeling method is based on the load components of a bus. Recently, the load modeling method based on measurements from a system has been introduced and developed by researchers. The two major components of a load modeling problem are determining the mathematical model for the target system and estimating the parameters of the determined model. We use the composite load model, which has both static and dynamic load characteristics. The ZIP model and the induction motor model are used for the static and dynamic load models, respectively. In this work, we propose the measurement-based parameter estimation method for the composite load model. The test system and related measurements are obtained using transient security assessment tool(TSAT) simulation program and PSS/E. The parameter estimation is then verified using these measurements. Cases are tested and verified using the sample system and its related measurements.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1198-1206
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• 2011

In this paper, a sensor bias estimation method with pseudo measurement for asynchronous multisensor systems is proposed. The proposed bias estimation method separates the local filter which estimates the target state with biased measurements into two parts, one is bias part, the other is target state part. By using these two parts, the algorithm generates the pseudo bias measurement for estimating bias, and then eliminates bias of local track through bias compensation. Finally, the proposed algorithm is evaluated by comparing with the existing EXX method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.109-117
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• 2005

Range and velocity measurement algorithm is a procedure for estimating the accurate target position by using matched filter outputs equally spaced both in range and doppler frequency domain. Especially, in measurement algorithm for multi-function radar, it is necessary to consider processing time as well as accuracy in order to track multi-targets simultaneously. In this paper, we analyze range and velocity measurement algorithm using discriminator estimation method which is a technique applied to angle measurement of monopulse radar. The applied method required constant processing time for estimation can be used in multiple target tacking. But, it is necessary to consider measurement accuracy because of using minimum channel outputs for estimation. In the simulation, we show that the applied method is superior to the traditional gravity center measurement algorithm with respect to the accuracy performance and also analyze the characteristics of the proposed technique by calculating RMS error level as the processing parameters such as pulse width , channel step, etc. change.