• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
• /
• pp.43-46
• /
• 2000

In this paper, we present a method for vehicle tracking systems using $\alpha$-$\beta$ filter based on fuzzy logic. The $\alpha$-$\beta$ filter estimates the future target positions using fixed $\alpha$.$\beta$ coefficients. We utilize the fuzzy logic to make $\alpha$ and $\beta$ coefficients very with the position. Comparisons of tracking performance made for three different schemes: the $\alpha$-$\beta$ filter, $\alpha$-$\beta$filter using fuzzy logic, and the kalman filter.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.2340-2344
• /
• 2003

This paper considers a tracking filter algorithm which can track a maneuvering target. Multiresolutional Interacting Multiple Model (MRIMM) algorithm is proposed to reduce computational burden. In this paper multiresolutional state space model equation and multiresolutional measurement equation are derived by using wavelet transform. This paper shows the outline of MRIMM algorithm. Simulation results show that MRIMM algorithm maintains a good tracking performance and reduces computational burden.

• 정보통신설비학회논문지
• /
• 제8권3호
• /
• pp.107-110
• /
• 2009

In this paper, a PN code tracking loop with extended Kalman filter (EKF) is proposed for a direct-sequence spread-spectrum. EKF is used to estimate amplitude and delay in a multipath. fading channel. It is shown that tracking error performance is significantly improved by EKF compared with a conventional tracking loop.

• Journal of Electrical Engineering and information Science
• /
• 제2권4호
• /
• pp.23-27
• /
• 1997

Presented are closed-form expressions of the three-state exponentially correlated acceleration (ECA) target-tracking filter. The steady-state solution is derived based on Vaughan's approach for the case that he measurements of target position and velocity are available at discrete point in time. The solution for ECA tracking filter using only position measurements and the solution for the constant acceleration (CA) tracking filter are obtained as a special case of the presented results.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2016년도 춘계학술대회
• /
• pp.302-304
• /
• 2016

The purpose of this paper is to draw comparison between the performance of ${\alpha}-{\beta}-{\gamma}$ filter and Kalman filter of a tracking module for ARPA system on board high dynamic warship. The comparison is based on the filters' capability to reduce residual error and maintain a stable transient response. The residual error is computed from the difference between the observed the predicted positions for the entire tracking period. The results indicate that the Kalman filter has a higher tracking accuracy compared to the optimal ${\alpha}-{\beta}-{\gamma}$ filter. However, both filters have a similar transient response.

• 한국통신학회논문지
• /
• 제8권3호
• /
• pp.83-90
• /
• 1983

N-Path SC追跡濾波器를 可聽周波數이상에서 硏究하였다. 먼저 전체 SC濾波器의 特性을 決定지우는 Filter Cell에 대한 解析으로 電荷方程式과 差等方程式에 의한 解析을 수행하고 커패시터와 스위치만으로 構成된 4-path와 8-path SC濾波器를 提示하였다. 다음 실제로 SC濾波器블록과 PLL블록의 構成으로 4-path와 8-path SC追跡濾波器를 각각 構成하였으며 最大應答의 移動, 커패시터比와 path數에 따른 Q와 利得(dB)의 變動, 追跡範圍등을 考察하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권2호
• /
• pp.710-726
• /
• 2018

This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

• Transactions on Electrical and Electronic Materials
• /
• 제14권4호
• /
• pp.187-192
• /
• 2013

Because of the importance of small target detection in infrared images, many studies have been carried out in this area. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small moving targets even in cases of target disappearance and appearance in serial infrared images in an environment with many noises. Difference images, which highlight the background images estimated with a background estimation filter from the original images, have a relatively very bright value, which becomes a candidate target area. Multiple target tracking consists of a Kalman filter section (target position prediction) and candidate target classification section (target selection). The system removes error detection from the detection results of candidate targets in still images and associates targets in serial images. The final target detection locations were revised with the mean shift algorithm to have comparatively low tracking location errors and allow for continuous tracking with standard model updating. In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalman filter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errors and ensured stable tracking with no tracking location diffusion.

• 제어로봇시스템학회논문지
• /
• 제12권12호
• /
• pp.1256-1260
• /
• 2006

This paper presents an enhanced ${\alpha}-{\beta}$ tracking filter whose ${\alpha}-{\beta}$ gains are updated by an approximation method at every scan to account for the transition of measurement dependent observation error variance in two-dimensional Cartesian coordinates. The approximate ${\alpha}-{\beta}$ gains are calculated from the amount of the change in the tracking index and the partial derivatives of the ${\alpha}-{\beta}$ gains with respect to a nominal tracking index. It is shown via simulation that the proposed tracker provides improved performance compared to the conventional ${\alpha}-{\beta}$ tracking filter.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제11권2호
• /
• pp.118-123
• /
• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.