• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.1
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• pp.25-32
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• 2011

Measurement noise variance of the radar is one of the main inputs of a state estimator of surveillance data processing system for air traffic control and has influences on the accuracy performance of maneuvering target tracking. A method is presented of estimating measurement noise variances every frame of target tracking using likelihood functions of multiple IMM filter. The results by running of Monte Carlo simulation show that variances are estimated within 5% of errors compared with true values and the tracking accuracy performance is improved.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.55-57
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• 2015

The tracking system plays a key role in accurate estimation and prediction of maneuvering vessel's position and velocity in a bid to enhance safety by taking avoiding action against collision. Therefore, in order to achieve this, many ocean- going vessels are equipped with radar and the ARPA system. However, the accuracy of prediction highly depends on the choice of the gain parameters, ${\alpha}$, ${\beta}$ and ${\gamma}$ employed in the tracking filter. P revious research of this paper was based on theoretically developing an algorithm for a tracking module. This research paper is hence a continuation by the authors to determine the optimal values of the gain parameters used in the tracking module. A tracking algorithm is developed using the ${\alpha}-{\beta}-{\gamma}$ filter to carry out prediction and smoothing of the positions and velocities. Numerical simulations are then performed to evaluate the optimal values of the smoothing parameters that will improve the performance of the tracking module and reduce measurement noise. The twice distance root mean square (2drms) is then calculated to determine error variation.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.456-462
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• 2007

Tracking error performance is investigated for the typical maneuvering pattern of the anti-ship missile for tracking filters based on IMM filter in both clear and cluttered environments. Threatening targets to a navel vessel can be categorized into having three kinds of maneuvering patterns such as Waver, Pop-Up, and High-Diver maneuvers, which are classified according to launching platform or acceleration input to be applied. In this paper, the tracking errors for three kinds of maneuvering targets are represented and are investigated through simulation results. Studying estimation errors for each maneuvering target allows us to have insight into the most threatening maneuvering pattern and to construct the test maneuvering scenario for radar system validation.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1961-1963
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• 2001

In a target tracking problem, the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In this study, an extended robust $H_{\infty}$ was developed which can significantly improve the tracking performance when glint noise is present. Through the computer simulations, the proposed filter showed superior and robust tracking performance compared with other extended Kalman filters.

• 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 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.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.447-452
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• 2013

Vessel traffic system uses multiple sea surveillance radars as a primary sensor to obtain maritime traffic information like as ship's position, speed, course. The systematic errors such as the range bias and the azimuth bias of the two-dimensional radar system can significantly degrade the accuracy of the radar image and target tracking information. Therefore, the systematic errors of the radar system should be corrected precisely in order to provide the accurate target information in the vessel traffic system. In this paper, it is proposed that the method compensates the range bias and the azimuth bias using AtoN information installed at VTS coverage. The radar measurement residual error model is derived from the standard error model of two-dimensional radar measurements and the position information of AtoN, and then the linear Kalman filter is designed for estimation of the systematic errors of the radar system. The proposed method is validated via Monte-Carlo runs. Also, the convergence characteristics of the designed filter and the accuracy of the systematic error estimates according to the number of AtoN information are analyzed.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.649-652
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• 1999

In this paper, the design method of target tracking systems using the extended $H^{\infty}$ filter(EHF) is proposed. Usually, a Cartesian coordinate frame is tell suited to describe the target dynamics. However, the measurements made in radar-centered polar coordinates are expressed as nonlinear equations in Cartesian coordinates. Thus the tacking problem is concerned with the nonlinear estimation. The extended $H^{\infty}$ filter is able to deal with the problems arising in the target tacking systems such as the parameter uncertainty included inevitably in modeling physical systems mathematically, the unavailableness of the stochastic information about exogenous disturbances, and errors due to the linearization of measurement equations. We show the proposed filter is robuster than the extended Kalman filter(EKF) through a simple target tracking example.

• Advances in aircraft and spacecraft science
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• v.7 no.6
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• pp.495-516
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• 2020

Nonlinear state estimation is a desirable and required technique for many situations in engineering (e.g., aircraft/spacecraft tracking, space situational awareness, collision warning, radar tracking, etc.). Due to high standards on performance in these applications, in the last few decades, there was an increasing demand for methods that are able to provide more accurate results. However, because of the mathematical complexity introduced by the nonlinearities of the models, the nonlinear state estimation uses techniques that, in practice, are not so well-established which, leads to sub-optimal results. It is important to take into account that each method will have advantages and limitations when facing specific environments. The main objective of this paper is to provide a comprehensive overview and interpretation of the most well-known methods for nonlinear state estimation with Gaussian priors. In particular, the Kalman filtering methods: EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), CKF (Cubature Kalman Filter) and EnKF (Ensemble Kalman Filter) with an aerospace perspective.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.186-190
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• 2015

Intelligent vehicle plans motion and navigate itself based on the surrounding environment perception. Hence, the precise environment recognition is an essential part of self-driving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environment, the vehicle must percept all the dynamic obstacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. First, exceptional radar measurement model for vehicle, concentrated on the corner, is described by mixture density network (MDN), and applied to particle filter weighting. Also, to conquer the curse of dimensionality of particle filter and estimate the time-varying number of multi-target states, reversible jump markov chain monte carlo (RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstrated through several computer simulations.