• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

• 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.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1826-1835
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• 2007

In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.267-273
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• 1998

This paper introduces an adaptive algorithm determining the measurement-track association problem in multi-target tracking(MTT). We model the target and measurement relationships with mean field theory and then define a MAP estimate for the optimal association. Based on this model, we introduce an energy function defined over the measurement space, that incorporates the natural constraints for target tracking. To find the minimizer of the energy function, we derived a new adaptive algorithm by introducing the Lagrange multipliers and local dual theory. Through the experiments, we show that this algorithm is stable and works well in general environments. Also the advantages of the new algorithm over other algorithms are discussed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.186-197
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• 1996

A multiple target tracking (MTT) problem is to track a number of targets in clusttered environment, where measurements may contain uncertainties of measurement origin due to clutter, missed detection, or other targets, as well as measurement noise errors. Hence, an MTT filter should be introduced to resolve this problem. In this paper, a neural network is rpoposed as an MTT filter.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.209-218
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• 2013

Military surveillance systems with electro-optical sensors can be used to track a number of targets efficiently and reliably. In MTT (Multi-Target Tracking), joint events in which different tracks share the same measurements may occur. Measurement-to-track assignment are computationally challenging because of the number of operations increases exponentially with number of tracks and number of measurements. IHPDA (Integrated Highest Probability Data Association) based on a 2D-Assignment technique can find an optimal solution for measurement to track one-to-one assignments for complex environments. In this paper, LM-IHPDA (Linear Multi-Target IHPDA) which does not need to form all feasible joint events of association and thus the computational load is linear in the number of tracks and the number of measurements. Simulation studies illustrate the effectiveness of this approach in an infrared image environment.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1259-1262
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• 1998

This paper introduced a scheme for finding the relationships between the measurements and tracks in multi-target tracking (MTT). We considered the relationships between targets and measurements as MRF and assumed a priori as a Gibbs distribution. An energy function is defined over the measurement space, as accurately as possible so that it may incorporate most of the important natural constraints. To find the minimizer of the energy function, we derived a new equation of closed form.

• 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 the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.94-104
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• 2000

The accuracy and reliability of the target tracking is very critical issue in the design of automotive collision warning radar A significant problem in multi-target tracking (MTT) is the target-to-measurement data association If an incorrect measurement is associated with a target, the target could diverge the track and be prematurely terminated or cause other targets to also diverge the track. Most methods for target-to-measurement data association tend to coalesce neighboring targets Therefore, many algorithms have been developed to solve this data association problem. In this paper, a new multi-target data association method based on order statistics is described The new approaches. called the order statistics probabilistic data association (OSPDA) and the order statistics joint probabilistic data association (OSJPDA), are formulated using the association probabilities of the probabilistic data association (PDA) and the joint probabilistic data association (JPDA) filters, respectively Using the decision logic. an optimal or near optimal target-to-measurement data association is made A computer simulation of the proposed method in a heavy cluttered condition is given, including a comparison With the nearest-neighbor CNN). the PDA, and the JPDA filters, Simulation results show that the performances of the OSPDA filter and the OSJPDA filter are superior to those of the PDA filter and the JPDA filter in terms of tracking accuracy about 18% and 19%, respectively In addition, the proposed method is implemented using a developed digital signal processing (DSP) board which can be interfaced with the engine control unit (ECU) of car engine and with the d?xer through the controller area network (CAN)