• International Journal of Control, Automation, and Systems
• /
• 제6권3호
• /
• pp.413-423
• /
• 2008

The template matching method is used as a simple method to track objects or patterns that we want to search for in the input image data from image sensors. It recognizes a segment with the highest correlation as a target. The concept of this method is similar to that of SNF (Strongest Neighbor Filter) that regards the measurement with the highest signal intensity as target-originated among other measurements. The SNF assumes that the strongest neighbor (SN) measurement in the validation gate originates from the target of interest and the SNF utilizes the SN in the update step of a standard Kalman filter (SKF). The SNF is widely used along with the nearest neighbor filter (NNF), due to computational simplicity in spite of its inconsistency of handling the SN as if it is the true target. Probabilistic Strongest Neighbor Filter for m validated measurements (PSNF-m) accounts for the probability that the SN in the validation gate originates from the target while the SNF assumes at any time that the SN measurement is target-originated. It is known that the PSNF-m is superior to the SNF in performance at a cost of increased computational load. In this paper, we suggest an image tracking algorithm that combines the template matching and the PSNF-m to estimate the states of a tracked target. Computer simulation results are included to demonstrate the performance of the proposed algorithm in comparison with other algorithms.

• 한국항행학회논문지
• /
• 제25권3호
• /
• pp.217-222
• /
• 2021

EOTS(electro-optical tracking system)는 유도무기의 성능 평가를 위해 유도무기를 추적하여 영상을 획득하는데 활용되고 있다. 유도무기에 대한 추적을 잃어버렸을 경우 유도무기가 매우 빠르게 비행하기 때문에 운용자가 이를 다시 포착하는 것은 거의 불가능하다. 레이더나 텔레메트리 데이터를 활용하여 재 포착 하는 방법이 활용되고 있으나 데이터를 실시간으로 수신할 수 있는 통신망의 설치가 수반되어야하기 때문에 장소에 대한 제약이 따른다. 하지만 유도무기 비행시험 수행 시 계산되는 예상 궤적은 실시간으로 수신할 필요 없이 저장해두었다가 사용할 수 있기 때문에 통신망 설비와 관계없이 활용이 가능하다. 본 논문에서는 미리 알고 있는 비행체의 예상 궤적을 활용하여 비행체를 잃어버렸을 시 비행체의 위치를 예상하는 방법을 제안한다. DTW (dynamic time warping)를 통해 예상궤적과 추적궤적을 비교하여 비행체의 각속도를 추정하고 이를 Kalman Filter의 보정단계에서 관측 값으로 활용하여 비행체의 다음 상태를 예측한다. 제안한 방법의 타당성을 실제 비행체 궤적에 적용하여 검증하였다.

• 대한전기학회논문지
• /
• 제35권11호
• /
• pp.519-528
• /
• 1986

By using a least-square input estimator and likelihood ratio technique, a tracking problem is presented. A Kalman tracking filter based on constant-velocity, straight-line model is used to track a target and the filtered estimate is updated using an input estimate when a maneuver is detected. Track residuals at each scan are sensed by a detector to guard against unexpected corrections of the filter. The simulation results show there are significant improvements using the scheme presented.

• 한국산업융합학회 논문집
• /
• 제27권2_1호
• /
• pp.249-256
• /
• 2024

Traditional methods for monitoring targets rely heavily on probabilistic data association (PDA) or Kalman filtering. However, achieving optimal performance in a densely congested tracking environment proves challenging due to factors such as the complexities of measurement, mathematical simplification, and combined target detection for the tracking association problem. This article analyzes a target tracking problem through the lens of fuzzy logic theory, identifies the fuzzy rules that a fuzzy tracker employs, and designs the tracker utilizing fuzzy rules and Kalman filtering.

• 한국음향학회지
• /
• 제32권4호
• /
• pp.354-360
• /
• 2013

본 논문은 고밀도 클러터 환경에서 클러터 제거기능을 이용하여 구간선형기동 수중운동체의 탐지 및 추적에 대한 성능향상을 다루었다. 고밀도 클러터 환경에서 허프변환(Hough transform)을 이용한 클러터 제거 알고리즘을 통해 클러터 특성을 나타내는 측정치를 제거한 후 남은 측정치에 대해 추적 필터인 CMKF-L을 적용하여 추적성능을 확인하였다. 모의 신호와 해상실험데이터를 이용하여 실험을 수행하였으며 고밀도 클러터 환경에서 제안하는 알고리즘을 적용하여 클러터는 상당수 제거되고 표적에 대한 추적은 지속적으로 안정되게 수행됨을 확인하였다.

• 한국음향학회:학술대회논문집
• /
• 한국음향학회 2004년도 추계학술발표대회논문집 제23권 2호
• /
• pp.479-482
• /
• 2004

In this article we consider a single maneuvering target Tracking algorithm in the presence of missing measurements and high clutter environments for multi-sensor target tracking problem. The tracking algorithm is based on the Particle filtering method to predict and update target states. Proposed is the AMM-PF(Auxiliary Multiple Model Particle Filter)[2] method for maneuvering target tracking to improve performance in track estimate and maintenance with a high level of uncertainty. The algorithm we propose is compared to the Extended Kalman Filter(EKF). A simulation study is included.

• ETRI Journal
• /
• 제38권2호
• /
• pp.356-365
• /
• 2016

This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target's state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
• /
• pp.187-191
• /
• 1988

To track the manenvering target and to derive the Filter using state estimation and information in real time, we derive adaptive Kalman Filter which reinitialize the internal filter mode.

• 제어로봇시스템학회논문지
• /
• 제21권9호
• /
• pp.836-842
• /
• 2015

In this paper, the target size and model based target size estimator (TMBE) algorithm is presented for iimaging infrared (IIR) seeker. At the imaging seeker, target size information is important factor for accurate tracking. The model based target size estimator filter (MBEF) algorithm was proposed to estimate target size at imaging infrared seeker. But, the model based target size estimator filter algorithm need to know relative distance from the target. In order to overcome the problem, we propose target size and model based target size estimator filter (TMBEF) algorithm which based on the target size. The performance of proposed algorithm is tested at target intercept scenario. The experiment results show that the proposed algorithm has the accurate target size estimating performance.

• International Journal of Aeronautical and Space Sciences
• /
• 제15권3호
• /
• pp.258-266
• /
• 2014

We present a system for the real-time visual relative navigation of a fixed-wing unmanned aerial vehicle in a GPS-denied environment. An extended Kalman filter is used to construct a vision-aided navigation system by fusing the image processing results with barometer and inertial sensor measurements. Using a mean-shift object tracking algorithm, an onboard vision system provides pixel measurements to the navigation filter. The filter is slightly modified to deal with delayed measurements from the vision system. The image processing algorithm and the navigation filter are verified by flight tests. The results show that the proposed aerial system is able to maintain circling around a target without using GPS data.