A Study of Optimization of α - β - γ - η Filter for Tracking a High Dynamic Target

Bao-Feng Pan,Anne Wanjiru Njonjo,Tae-Gweon Jeong 한국항해항만학회 2017 한국항해항만학회지 Vol.41 No.5

The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel’s position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The α- β - γ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity α - β filter, the α - β - γ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The α - β - γ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter’s performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, α - β - γ - η algorithm as compared to the constant acceleration model, α - β - γ in terms of error reduction and stability of the filter during target maneuver.

A Study on Optimization of Fourth-Order Fading Memory Filter under the Highly Dynamic Motion of Both Own Ship and Target

Bao Feng Pan,Tae Gweon Jeong 한국항해항만학회 2017 한국항해항만학회 학술대회논문집 Vol.2017 No.추계

Tracking filter plays a key role in accurate estimation and prediction of maneuvering vessel’s dynamics. The third-order α-β-Υ 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 α-β-Υ filter algorithms. This study aims to optimize the fourth-order fading memory algorithm α-β-Υ-η filter, which is extended form α-β-Υ filter, to get much more accurate position of high dynamic target on the condition that the own ship is also high dynamic.

Comparison of Performance of α-β-γ-η Filter and Kalman Filter for ARPA System to Track High Dynamic Target

Bao Feng Pan,Tae Gweon Jeong 한국항해항만학회 2017 한국항해항만학회 학술대회논문집 Vol.2017 No.춘계

A Study of Optimization of α-β-γ-η Filter for Tracking a High Dynamic Target

Pan, Bao-Feng,Njonjo, Anne Wanjiru,Jeong, Tae-Gweon Korean Institute of Navigation and Port Research 2017 한국항해항만학회지 Vol.41 No.5

The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel's position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity ${\alpha}-{\beta}$ filter, the ${\alpha}-{\beta}-{\gamma}$ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The ${\alpha}-{\beta}-{\gamma}$ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter's performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, ${\alpha}-{\beta}-{\gamma}-{\eta}$ algorithm as compared to the constant acceleration model, ${\alpha}-{\beta}-{\gamma}$ in terms of error reduction and stability of the filter during target maneuver.

Optimization of the Gain Parameters in a Tracking Module for ARPA system on Board High Dynamic Warships

Bao-Feng Pan,Anne Wanjiru Njonjo,Tae-Gweon Jeong 한국항해항만학회 2016 한국항해항만학회지 Vol.40 No.5

The tracking filter plays a key role in the accurate estimation and prediction of maneuvering a vessel’s position and velocity when attempting to enhance safety by avoiding collision. Therefore, in order to achieve accurate estimation and prediction, many oceangoing vessels are equipped with the Automatic Radar Plotting Aid (ARPA) system. However, the accuracy of prediction depends on the tracking filter’s ability to reduce noise and maintain a stable transient response. The purpose of this paper is to derive the optimal values of the gain parameters used in tracking a High Dynamic Warship. The algorithm employs a filter to provide accurate estimates and updates of the state variables, that is, positions, velocity and acceleration of the high dynamic warship based on previously observed values. In this study, the filtering coefficients and are determined from set values of the damping parameter. Optimization of the damping parameter , is achieved experimentally by plotting the residual error against different values of the damping parameter to determine the least value of the damping parameter that results in the optimum smoothing coefficients leading to a reduction in the noise corruption effect. Further investigation of the performance of the filter indicates that optimal smoothing coefficients depend on the initial and average velocity of the target.

A Basic Study on Development of a Tracking Module for ARPA system for Use on High Dynamic Warships

Anne Wanjiru Njonjo,Bao-Feng Pan,Tae-Gweon Jeong 한국항해항만학회 2016 한국항해항만학회지 Vol.40 No.2

The maritime industry is expanding at an alarming rate hence there is a perpetual need to improve situation awareness in the maritime environment using new and emerging technology. Tracking is one of the numerous ways of enhancing situation awareness by providing information that may be useful to the operator. The tracking module designed herein comprises determining existing states of high dynamic target warship, state prediction and state compensation due to random noise. This is achieved by first analyzing the process of tracking followed by design of a tracking algorithm that uses α-β-γ tracking filter under a random noise. The algorithm involves initializing the state parameters which include position, velocity, acceleration and the course. This is then followed by state prediction at each time interval. A weighted difference of the observed and predicted state values at the nth observation is added to the predicted state to obtain the smoothed (filtered) state. This estimation is subsequently employed to determine the predicted state in the next radar scan. The filtering coefficients ,  and  are determined from a pre- determined value of the damping parameter, . The smoothed, predicted and the observed positions are used to compute the twice distance root mean square (2drms) error as a measure of the ability of the tracking module to manage the noise to acceptable levels.

A Study on Minimum Number of Ship-handling Simulation Required for Evaluating Vessel’s Proximity Measure

Tae-Gweon JEONG,Bao-Feng PAN 한국항해항만학회 2014 한국항해항만학회지 Vol.38 No.6

The Korean government has introduced and enforced maritime traffic safety assessment to secure traffic safety since 2010. The maritime traffic safety assessment is needed by law to design a new port or modify an existing one. According to Korea Maritime Safety Act, in the assessment the propriety of marine traffic system consists of the safety of channel transit and berthing/unberthing maneuver, safety of mooring, and safety of marine traffic flow. The safety of channel transit and berthing/unberthing maneuver can be evaluated only by ship-handling simulation. The ship-handling simulation is carried out by sea pilots working with the port concerned. The vessel’s proximity measure is an important factor to evaluate traffic safety. The proximity measure is composed of vessel’s closest distance to channel boundary and probability of grounding/collision. What is more, the probability of grounding becomes important. According to central limit theorem, a sample has a normal distribution on condition that its size is more than 30. However, more than 30 simulation runs bring about the increase of assessment period and difficulty of employing sea pilots. Therefore this paper is to find out minimum sample size for evaluating vessel’s proximity. First sample sets of size of 3, 5, 7, 9 etc. are selected randomly on the basis of normal distribution. And then KS test for goodness of fit and t-test for confidence interval are applied to each sample set. Finally this paper decides the minimum sample size. As a result this paper suggests the minimum sample size of 5, that is, the simulation of more than five times.

Molecular Cloning and Characterization of a cis-Epoxysuccinate Hydrolase from Bordetella sp. BK-52

( Hai Feng Pan ),( Wen Na Bao ),( Zhi Peng Xie ),( Jian Guo Zhang ),( Yong Quan Li ) 한국미생물 · 생명공학회 2010 Journal of microbiology and biotechnology Vol.20 No.4

A Basic Study on Development of a Tracking Module for ARPA system for Use on High Dynamic Warships

Anne Wanjiru Njonjo,Bao Feng Pan,Tae Gweon Jeong 한국항해항만학회 2016 한국항해항만학회지 Vol.40 No.2

The maritime industry is expanding at an alarming rate hence there is a perpetual need to improve situation awareness in the maritime environment using new and emerging technology. Tracking is one of the numerous ways of enhancing situation awareness by providing information that may be useful to the operator. The tracking module designed herein comprises determining existing states of high dynamic target warship, state prediction and state compensation due to random noise. This is achieved by first analyzing the process of tracking followed by design of a tracking algorithm that uses α-β-γ tracking filter under a random noise. The algorithm involves initializing the state parameters which include position, velocity, acceleration and the course. This is then followed by state prediction at each time interval. A weighted difference of the observed and predicted state values at the nth observation is added to the predicted state to obtain the smoothed (filtered) state. This estimation is subsequently employed to determine the predicted state in the next radar scan. The filtering coefficients α, β, and γ are determined from a pre- determined value of the damping parameter, ξ. The smoothed, predicted and the observed positions are used to compute the twice distance root mean square (2drms) error as a measure of the ability of the tracking module to manage the noise to acceptable levels.

Basic Study of a Comparison of the Performances of the α-β-γ Filter and the Kalman Filter Regarding Their Use in the ARPA-System Tracking Module of High-Dynamic Warships

Njonjo, Anne Wanjiru,Pan, Bao-Feng,Jeong, Tae-Gweon Korean Institute of Navigation and Port Research 2017 한국항해항만학회지 Vol.41 No.5

"Tracking" here refers to the estimation of a moving object with some degree of accuracy where at least one measurement is given. The measurement, which is the sensor-obtained output, contains systemic errors and errors that are due to the surrounding environment. Tracking filters play the key role of the target-state estimation after the updating of the tracking system; therefore, the type of filter that is used for the conduction of the estimations is crucial in the determining of the reliability of the updated value, and this is especially true since the performances of different filters vary when they are subjected to different environmental and initial conditions. The purpose of this paper is the conduction of a comparison between the performances of the ${\alpha}-{\beta}-{\gamma}$ filter and the Kalman filter regarding an ARPA-system tracking module that is used on board high-dynamic warships. The comparison is based on the capability of each filter to reduce noise and maintain a stable response. The residual error is computed from the difference between the true and predicted positions and the true and estimated positions for the given sample. The results indicate that the tracking accuracy of the Kalman filter is higher compared with that of the optimal ${\alpha}-{\beta}-{\gamma}$ filter; however, the response of the optimal ${\alpha}-{\beta}-{\gamma}$ filter is more stable.