• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.633-642
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• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.335-336
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• 2011

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.718-724
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• 2013

In today's automotive industry, there exist several systems that help drivers reduce the possibility of accidents, such as the ADAS (Advanced Driver Assistance System). The ADAS helps drivers make correct and quick decisions during dangerous situations. This study analyzed the performance of the IMM (Interacting Multiple Model) method based on multiple Kalman filters using the data acquired from a driving simulator. An IMM algorithm is developed to identify the current discrete state of neighboring vehicles using the sensor data and the vehicle dynamics. In particular, the driving modes of the neighboring vehicles are classified by the cruising and maneuvering modes, and the transition between the states is modeled using a Markovian switching coefficient. The performance of the IMM algorithm is analyzed through realistic simulations where a target vehicle executes sudden lane change or acceleration maneuver.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.1
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• pp.18-27
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• 2020

In order to estimate the launch point of a high-speed vehicle, predicting the various characteristics of the vehicle's movement, such as drag and thrust, must be preceded by the estimation. To predict the various parameters regarding the vehicle's characteristics, we build the IMM filter specialized in predicting the parameters of the post-launch phase based on flight dynamics. Then we estimate the launch point of the high-speed vehicle using Inverse Dynamics. In addition, we assume the arbitrary error level of the radar for accuracy of the prediction. We organize multiple-dimensioned IMM structures, and figure out the optimal value of parameters by comparing the various IMM structures. After deriving the optimal value of parameters, we verify the launch point estimation error under certain error level.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.59-66
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• 1993

In this paper we propose an adaptive update rate tracking algorithm for a phased array radar, based on the interacting multiple model(IMM) algorithm. The purpose of the IMM algorithm hers is twofold: 1) to estimate and predict the target states, and 2) to estimate the level of the process noise. Using the estimate of the process noise level adapted to target dynamics, the update interval is determined to maintain a desired prediction accuracy so that the radar system load is minimized. The adaptive update rate tracking algorithm is implemented for a phased array radar and evaluated with Monte Carlo simulations on various trajectories. The evaluation results of the proposed algorithm and a standard Kalman filter without the adaptive update rate control are presented to compare.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.497-502
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• 2002

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, a DNA coding-based interacting multiple model (DNA coding-based W) method is proposed. The proposed method can overcome the mathematical limits of conventional methods by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive IMM algorithm and the GA-based IMM method in computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1669-1674
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• 2004

In this paper, a nonlinear information filter (IF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, a nonlinear IF is used in place of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1476-1483
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• 2007

In a multi-sensor target tracking systems, the local sensors have the role of tracking the target and transferring the measurements to the fusion center. The measurements from the same target can arrive out of sequence called, the out-of-sequence measurements(OOSMs). The OOSM can arise in a form of single-lag or multi-lag throughout the transfer at the fusion center. The recursive retrodiction step was proposed to update the current state estimates with the multi-lag OOSM from the several previous papers. The real world has the possible situations that the maneuvering target informations can arrive at the fusion center with the random clutter in the possible OOSMs. In this paper, we incorporate the IMM-MPDA(Interacting Multiple Model - Most Probable Data Association) into the multi-lag OOSM update. The performance of the IMM-MPDA filter with multi-lag OOSM update is analyzed for the various clutter densities, OOSM lag numbers, and target maneuvering indexes. Simulation results show that IMM-MPDA is sufficient to be used in out of sequence environment and it is necessary to correct the current state estimates with OOSM except a very old OOSM.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.532-548
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• 2021

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy.

• Journal of the Korean Society of Systems Engineering
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• v.5 no.1
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• pp.1-6
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• 2009

Gun fire solution computed in ballistic computing unit (BCU) needs to evaluated before applying in real fire. In this paper, ballistic performance analysis method is studied for reasonable prediction or hit probability with ballistics error presentation on hitting plane. Also Gun fire solution using interacting multiple model (IMM) algorithm is analyzed through proposed method.