• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2085-2087
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• 2003

A new interacting multiple model (IMM) method based on intelligent input estimation (IIE) is proposed for tracking a maneuvering target. In the proposed method, the acceleration level of each sub-filter is determined by IIE using the fuzzy system, which is optimized by the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method in computer simulations.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.251-254
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• 2003

In this paper, we develop an effective method for estimating and predicting interference power strength using the IMM(Interacting Multiple Model) algorithm. Based on the proposed interference prediction algorithm, we adjust transmission power of mobile terminals to maintain a certain level of target signal - to - interference- plus -noise- ratio ( SINR ) at the base station. Results of numerical experiments are presented to show a performance profile of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.224-232
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• 2015

In order to develop an active safety system which avoids or mitigates collisions with preceding vehicles such as autonomous emergency braking (AEB), accurate state estimation of the nearby vehicles is very important. In this paper, an algorithm is proposed using 3 dynamic models to better estimate the state of a vehicle which has various dynamic patterns in both longitudinal and lateral direction. In particular, the proposed algorithm is based on the Interacting Multiple Model (IMM) method which employs three different dynamic models, in cruise mode, lateral maneuver mode and longitudinal maneuver mode. In addition, a Probabilistic Data Association Filter (PDAF) is utilized as a data association algorithm which can improve the reliability of the measurement under a clutter environment. In order to verify the performance of the proposed method, it is simulated in comparison with a Kalman filter method which employs a single dynamic model. Finally, the proposed method is validated using radar data obtained from the field test in the proving ground.

• The Journal of the Acoustical Society of Korea
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• v.36 no.3
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• pp.202-210
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• 2017

For the target tracking in cluttered environment using a bistatic sonar whose transmitter and receiver are separately positioned, it is necessary to use data association algorithm via applying a proper measurement modelling to the bistatic sonar. The measurements obtained from the interval of ownship's maneuver have an increased error due to uncertainty of the position of transmitter and receiver. Using the measurements from this interval results in poor target tracking performance. In this paper, an improved tracking performance for the proposed data association based multiple model algorithm is validated by a monte carlo simulation.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.198-204
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• 2020

This paper designs a electromagnetic-log (EM-Log) aided navigation filter for maritime environment without global navigation satellite system (GNSS). When navigation is performed for a long time, Inertial navigation system (INS)'s error gradually diverges. Therefore, an integrated navigation method is used to solve this problem. EM-Log sensor measures the velocity of the vehicle. However, since the measured velocity from EM-Log contains the speed of the sea current, the aided navigation filter is required to estimate the sea current. This paper proposes a single model filter and interacting multiple (IMM) model filter methods to estimate the sea current and analyzes the influence of the sea current model on the filter. The performance of the designed aided navigation filter is verified using a simulation and the improvement rate of the filter compared to the pure navigation is analyzed. The performance of single model filter is improved when the sea current model is correct. However, when the sea current model is incorrect, the performance decreases. On the other hands, IMM model filter methods show the stable performance compared to the single model.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.42-47
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• 1999

In this paper, a mixture interacting multiple model (MIMM) algorithm is proposed to enhance speech contaminated by additive nonstationary noise. In this approach, a mixture hidden filter model (HFM) is used to model the clean speech and the noise process is modeled by a single hidden filter. The MIMM algorithm, however. needs large computation time because it is a recursive method based on multiple Kalman filters with mixture HFM. Thereby, a computationally efficient implementation of the algorithm is developed by exploiting the structure of the Kalman filtering equation. The simulation results show that the proposed method offers performance gain compared to the previous results in [4,5] with slightly increased complexity.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.363-374
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• 2012

This paper studies the problem of tracking a re-entry vehicle (RV) in order to predict its impact point on the ground. Re-entry target dynamics combined with super-high speed has a complex non-linearity due to ballistic coefficient variations. However, it is difficult to construct a database for the ballistic coefficient of a unknown vehicle for a wide range of variations, thus the reliability of target tracking performance cannot be guaranteed if accurate ballistic coefficient estimation is not achieved. Various techniques for ballistic coefficient estimation have been previously proposed, but limitations exist for the estimation of non-linear parts accurately without obtaining prior information. In this paper we propose the ballistic coefficient ${\beta}$ model-based interacting multiple model-extended Kalman filter (${\beta}$-IMM-EKF) for precise tracking of an RV. To evaluate the performance, other ballistic coefficient model based filters, which are gamma augmented filter, gamma bootstrapped filter were compared and assessed with the proposed ${\beta}$-IMM-EKF for precise tracking of an RV.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.211-211
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• 2000

The multiple targets tracking problem has been one of main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimension filter, input estimation filter, interacting multiple model (IMM) filter, federated variable dimension filter with input estimation, probable data association (PDA) filter etc. have been proposed to address the tracking and sensor fusion issues. In this paper, two existing tracking algorithms, i.e. the IMMPDA filter and the variable dimension filter with input estimation (VDIE), are combined for the purpose of improving the tracking performance of maneuvering targets in clutter. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns i.e. Waver, Pop-Up, and High-Diver motions, are defined and are applied to the modified IMMPDA filter considered as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMMPDA filter than the standard IMM filter are demonstrated through computer simulations.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.459-464
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• 2004

In this paper, an autonomous navigation system for autonomous guided vehicles (AGVs) operated in an automated container terminal is designed. The navigation system is based on the sensors detecting the range and bearing. The navigation algorithm used is an interacting multiple model (IMM) algorithm to detect other AGVs and avoid other obstacles using informations obtained from multiple sensors. As models to detect other AGVs (or obstacles), two kinematic models are derived： Constant velocity model for linear motion and constant speed turn model for curvilinear motion. For constant speed turn model, an unscented Kalman filter (UKF) is used because of drawbacks of the extended Kalman filter (EKF) in nonlinear system. The suggested algorithm reduces the root mean squares error for linear motions, while it can rapidly detect possible turning motions.