• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.425-428
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• 2006

In this paper, we propose an interacting multiple model (IMM) method using intelligent tracking filter with fuzzy gain to reduce tracking errors for maneuvering targets. In the proposed filter, to exactly estimate for each sub-model, we propose the fuzzy gain based on the relation between the filter residual and its variation. To optimize each fuzzy system, we utilize the genetic algorithm (GA). Finally, the tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and input estimation (IE) method through computer simulations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.3
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• pp.1-8
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• 2000

In this paper, we formulate an optimal control problem to obtain the optimal signal strength and detection threshold for tracking with NN filter, First, we predict the tracking performance of NN filter by using the HYCA method. Based on this method, the predicted tracking performance is represented with respect to signal strength and detection threshold. Using this relation, we find the optimal parameters for following three examples： 1) the sequence of optimal detection threshold which minimizes sum of position estimation error； 2) the sequence of optimal detection threshold which minimizes sum of validation gate volume； and 3) the sequence of optimal signal strength and detection threshold which minimizes sum of signal strength.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.145-147
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• 2017

Tracking filter plays a key role in accurate estimation and prediction of maneuvering vessel's dynamics. The third-order ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. Fading memory algorithm performs a better performance in numerous of ${\alpha}-{\beta}-{\gamma}$ filter algorithms. This study aims to optimize the fourth-order fading memory algorithm ${\alpha}-{\beta}-{\gamma}-{\eta}$ filter, which is extended form ${\alpha}-{\beta}-{\gamma}$ filter, to get much more accurate position of high dynamic target on the condition that the own ship is also high dynamic.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.163-170
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• 2003

The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase in memory size and computation quantity over time. In this paper, the inevitable predicament of the optimal Bayesian filter is resolved in a suboptimal fashion through the use of a receding horizon strategy. As a result, the problems of memory and computational requirements are diminished. As a priori information, the horizon initial state is estimated from the validated measurements on the receding horizon. Consequently, the suboptimal algorithm proposed allows for real time implementation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.12
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• pp.1062-1070
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• 2015

This paper presents an application of an optimal smoothing filter for moving target tracking problem based on measured noise source. In order to measure distance and velocity for the moving target, a beamforming method is applied to use the noise source by using microphone array. Also a Kalman filter and an optimal smoothing algorithm are adopted to improve accuracy of trajectory estimation by using a Singer target model. The simulation is conducted with a missile dynamics to verify performance of the optimal smoothing filter, and a model rocket is used for experiment environment to compare the trajectory estimation results between the beamforming, the Kalman filter, and the smoother. The Kalman filter results show better tracking performance than the beamforming technique, and the estimation results of the optimal smoother outperform the Kalman filter in terms of trajectory accuracy in the experiment results.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2011-2013
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• 2001

In this paper the MMAE(Multiple Model Adaptive Estimation) algorithm using the MGEKF(Modified Gain Extended Kalman Filter) of which modes are set to be measurement biases is proposed to enhance the performance of target tracking with bearing only measurements. The state are composed of relative position, relative velocity and taregt acceleration. The mode probability is calculated from the bearing only measurements from the HMS(Hull-Mounted Sonar). The proposed algorithm is tested in a series of computer simulation runs.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.1
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• pp.78-87
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• 1989

In this paper, a spatio-temporal gradient (STG) method with generalized least square estimation (GLSE) is proposed for the detection of an object motion in an image sequence corrupted by white Gaussian noise. The proposed method is applied to an automatic target tracker using a high speed 16-bit microprocessor in order to track one moving target in real time. Experimental results show that the proposed method has much better performance over the conventional one with least square estimation (LSE).

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.469-474
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• 1990

The implementation of modern guidance law derived from optimal control theory requires accurate current states of target, for example, position, velocity and acceleration etc. But there is no sensors that measure the target states directly. So they are estimated from measurable data. For atmospheric missile engagement, direct application of the modern guidance laws may result In deterioration of Intercept performance because of poor observability associated with angles only-measurements by passive seeker and homing geometry. In this paper, a trajectory modulation method called "adaptive Intermittent maneuvers" is added to the modern guidance law, so the observability is enhanced and, consequently, improved the intercept performance. The estimation algorithm called "modified gain pseudo-measurement filter" is used for tracking filter. It is assumed that the passive seeker measure the angles between line of sight and Inertial frame. The Monte-Carlo simulation for realistic air-to-air Intercept scenario are conducted to demonstrate the effectiveness of intermittent maneuvers.ermittent maneuvers.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.235-243
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• 2000

The probabilistic data association filter(PDAF) is known to provide better tracking performance than the standard Kalman filter(KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al[7], in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to prove the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom[7], is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.112-119
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• 2023

In urban environments, signals of Global Positioning System (GPS) can be blocked and reflected by tall buildings, large vehicles, and complex components of road network. Therefore, the performance of the positioning system using the GPS module in urban areas can be degraded due to the loss of GPS signals necessary for the position estimation. To deal with this issue, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope and accelerometer, and Bayesian filters, such as Kalman filter (KF) and particle filter (PF), have been designed to enhance the performance of the GPS-based positioning system. Among Bayesian filters, the PF has been widely used for the target tracking and vehicle navigation, since it can provide superior performance in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. This paper presents a positioning system that uses the double-stacked particle filter (DSPF) as well as the accelerometer, gyroscope, and GPS receiver on the smartphone to provide higher pedestrian positioning accuracy in urban environments. The DSPF employs a nonparametric technique (Parzen-window) to create the multimodal target distribution that approximates the posterior distribution. Experimental results show that the DSPF-based positioning system can provide the significant improvement of the pedestrian position estimation in urban environments.