• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.733-739
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• 2008

This article proposes algorithms for the automatic initiation of the tracks of maneuvering targets in cluttered environments. These track initiation algorithms consist of IPDA-AI(Integrated Probabilistic Data Association-Amplitude Information) and MPDA(Most Probable Data Association) in an Interacting Multiple Model(IMM) configuration, and they are referred to as the IMM-IPDAF-AI and IMM-MPDA respectively. The IMM portion consists of several filters based on different dynamical models to handle target maneuvers. Each of the filters utilizes an IPDA-AI(or MPDA) algorithm to deal with the problem of track existence in the presence of clutter. Although the primary purpose of this study is to deal with the track initiation problem, the IMM-IPDAF-AI and IMM-MPDA can also be used for the maintenance of existing tracks and the termination of tracks for targets when they disappear. For illustrative purposes, simulation is used to compare the performance of the algorithms proposed to other track formation algorithms.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.487-494
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• 2011

Data Fusion algorithm is essential in Target Detection using radar, and it has more reliability. In this paper, Multi Radar Fusion algorithm using IMM(Interacting Multiple Model) filter is suggested. This well-known IMM filter has better performance than Kalman filter has. In this simulation, Distributed Data Fusion process was applied, and three sub-filters and one main filter were employed. In addition, this simulation was evaluated by virtual radar data which include constant velocity, constant accelerate, turn rate. The result of an evaluation shows better performance in the maneuvering section of aircraft.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.641-647
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• 2009

In this paper, a new filtering scheme for adaptive cruise control (ACC) system is presented. In the proposed scheme, the identification of the mode of the preceding vehicle is considered as a classification problem and it is done by a neural network classifier. The neural network classifier outputs a posterior probability of the mode of the preceding vehicle and the probability is directly used in the IMM framework. Finally, ten scenarios are made and the proposed NIMM is tested on them to show its validity.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.290-291
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• 2007

Barometers can provide height information steady but its accuracy becomes poor as the air data varies due to the vehicles's moving or time's elapsing. In order to keep the accuracy in spite of the air data changes, we propose a filter for the identification of baro-errors. The baro-errors mainly consist of bias and scale factor errors which gradually varies as the air data varies. With GPS height measurements, the scale factor and bias estimator is designed by applying the interacting multiple model (IMM) filtering technique to the baro-error random walk model. The resultant estimates are used to compensate current baro-measurement to supply accurate measurements steadily.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.4 s.19
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• pp.5-12
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• 2004

The tracking algorithm based on the interacting multiple model(IMM) requires a considerable number of sub-models for the various maneuvering targets in order to have a good performance. But it is not feasible to use the nm algorithm in the real system because of the computational burden. Therefore, we need an algorithm which requires less computing resources while maintaining a good performance. In this paper, we propose a fuzzy interacting multiple model algorithm(FIMMA) for the tracking of maneuvering targets, which uses a minimal number of sub-models by considering the maneuvering properties and adjusts the mode transition probabilities by using the mode probability as a fuzzy input. In order to verify the performance of FIMMA, the developed algorithm is applied to the tracking of i borne targets. Simulation results show that the FIMMA is very effective in the tracking of maneuvering targets.

• Journal of Advanced Navigation Technology
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• v.28 no.5
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• pp.640-649
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• 2024

In this study, we introduce an interactive multi-model-probabilistic data association filter (IMM-PDAF), a multi-target tracking algorithm that integrates multiple dynamic models for accurate real-time maritime target tracking. Multi-target tracking in the maritime environment requires high accuracy due to the complex dynamic environment and various movement patterns. The existing CV-PDAF (constant velocity model) and CT-PDAF (circling model) each assume a constant movement pattern, but it is difficult to handle all the complex movements occurring in various maritime environments with these single models. To solve this problem, this study proposes an interactive multi-model-probabilistic data association filter (IMM-PDAF), and the results of this paper applied to maritime RADAR data show that the proposed IMM-PDAF has relatively lower RMSE values than CV-PDAF and CT-PDAF, and has strong positioning performance even in complex dynamic environments. Therefore, this study results highlight the potential of the proposed IMM-PDAF to improve the reliability and efficiency of maritime surveillance systems and provide a multi-target tracking solution for complex tracking environments.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.578-585
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• 2012

CNS/ATM(Communication Navigation Surveillance/Air Traffic Management) was adopted as a standard navigation system of 21st century. Therefore, ICAO(International Civil Aviation Organization) members are developing the technology and infrastructure of CNS/ATM. ADS-B(Automatic Dependent Surveillance-Broadcast) system and Multilateration system are being implemented in the surveillance field of CNS/ATM. Multilateration system is installed in order to complement radar system and to surveil blind areas. Also, Multilateration system using TDOA(Time Difference Of Arrival) is more accurate than radar. In this paper, we applied an IMM(Interacting Multiple Model) filter which is widely used in radar systems to the Multilateration data in order to improve the reliability of the Multilateration data. Comparisons with the original Multilateration data and the Multilateration data with the IMM filter show that the ADS-B data with the IMM filter provides a better performance: 38.37% near the airport, 20.86% around 10 miles of the airport.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.32-35
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• 1996

In this paper, it is shown that the dominant errors of baro-altimeters can be characterized by bias and scale factor errors. Also an optimal filter for estimating both bias and scale factor is derived based on the concept of model transition. The optimal filter is, however, not realizable because the model transition hypotheses increase exponentially. Therefore a realizable suboptimal filter using the interacting multiple model(IMM) technique is proposed. Computer simulation results show that the estimation errors of the proposed filter are smaller than those of the conventional least squares algorithm with a forgetting factor when both the bias and the scale factor are varying.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.3 s.22
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• pp.24-32
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• 2005

This paper describes the tracking filter performance for Naval Gun Ballistic Computation Unit(BCU). BCU needs tracing filter for gun firing. Using data of tracking sensor, BCU calculates the future position of Target and Gun order in the time of flight. In this paper, tracing filter is designed with interacting multiple model(IMM). The tracking algorithm based on the IMM requirers a considerable number of sub-model for the various maneuvering target in order to have a good performance. But, in the case of ship target, the maneuvering is restricted compared with the air target. Considering the maneuvering properties and adjusting the mode transition probabilities and the process noise of sub-model, We designed the IMM3 algorithm for Naval tracking filter with three sub-model.