• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.24 no.2
• /
• pp.198-206
• /
• 2013

This paper presents the improvement of tracking performance using fuzzy inference system based task prioritizations for multi-function radars. The presented technique calculates elemental priorities using track information of a target and obtain the total priority from fuzzy inference system of each fuzzy set's membership function. In this paper, we proposed the task prioritization algorithms based on fuzzy inference system, and evaluated the tracking performance on multi-function radar scenario using it. As a result, we confirmed that excellent performance could be achieved when using the proposed algorithm.

• Journal of Advanced Navigation Technology
• /
• v.5 no.1
• /
• pp.74-84
• /
• 2001

Recently a new algorithm which combines advantages of the IMM and IE methods has been suggested. The combined-IMM/IE algorithm could improve the performance to some extent. However, the problem of large increase of tracking error near the maneuver detection due to the sudden maneuver input has not been solved. In this paper, we propose two schemes which can resolve this limitations of combined-IMM/IE algorithm. For illustrations of the performance of the proposed methods. Monte-Carlo simulations are carried out and the results are analyzed.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.43 no.6
• /
• pp.507-515
• /
• 2015

This paper proposes a filtering method for effective state vector estimation of highly maneuvering target. It is needed to hit the point called 'sweet spot' to increase the kill probability in missile interception. In paper, a filtering method estimates the length of a moving target tracked by a frequency modulated continuous wave (FMCW) radar. High resolution range profiles (HRRPs) is generated from the radar echo signal and then it's integrated into proposed filtering method. To simulate the radar measurement which is close to real, the study on the properties of scattering point of the missile-like target has been conducted with ISAR image for different angle. Also, it is hard to track the target efficiently with existing Kalman filters which has fixed measurement noise covariance matrix R. Therefore the proposed method continuously updates the covariance matrix R with sensor measurements and tracks the target. Numerical simulations on the proposed method shows reliable results under reasonable assumptions on the missile interception scenario.

• Journal of the Korea Society for Simulation
• /
• v.25 no.3
• /
• pp.53-63
• /
• 2016

We consider simulation study combining static and mobile decoys for survivability of a surface warship against torpedo attack. It is assumed that an enemy torpedo is a passive acoustic homing torpedo and detects a target within its maximum target detection range and search beam angle by computing signal excess via passive sonar equation, and a warship conducts an evasive maneuvering with deploying static and mobile decoys simultaneously to counteract a torpedo attack. Suggesting the four different decoy deployment plans to achieve the best plan, we analyze an effectiveness for a warship's survival probability through Monte Carlo simulation, given a certain experimental environment. Furthermore, changing the speed and the source level of decoys, the maximum torpedo detection range of warship, and the maximum target detection range of torpedo, we observe the corresponding survival probabilities, which can provide the operational capabilities of an underwater defense system.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.26 no.5
• /
• pp.400-408
• /
• 2023

The tracking accuracy of buoy-based LBL(Long Base Line) systems can be significantly influenced by sea environmental conditions. Particularly, the position of buoys that may have drifted due to sea currents. Therefore it is necessary to predict and optimize the drifted-buoy positions in the deploying step. This research introduces a free-drift simulation model using ocean data from the European CMEMS. The simulation model's predictions are validated by comparing them to actual sea buoy drift tracks, showing a substantial match in averaged drift speed and direction. Using this drift model, we optimize the initial buoy layout and compare the tracking performance between the center hexagonal layout and close track layout. Our results verify that the optimized layout achieves lower tracking errors compared to the other two layout.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.11
• /
• pp.881-888
• /
• 2020

In this paper, research on estimation of states of a target of interest using Line Of Sight(LOS) angle measurement is performed. Target's position, velocity, and acceleration are chosen to be the states of interests. The LOS measurement is known to be highly non-linear, making target dynamic modeling hard to be implemented into a filter. To solve this issue, the Pseudomeasurement equation was applied to the LOS measurement equation. With the help of this equation, 3D variable turn target dynamic model is applied to the filter model. For better performance, Kinematic Constraint is also implemented into the filter model. As for the filter, Bias Compensation Pseudomeasurement Filter (BCPMF) is used which is known for its robustness to initial conditions. Moreover, Two-Stage Kalman Filter (TSKF) form was also implemented to benefit from the parallel computation. As a result, TBCPMF 3DVT-KC is proposed and simulated to assess performance.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.2
• /
• pp.225-233
• /
• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.524-527
• /
• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.10
• /
• pp.747-754
• /
• 2003

An enhancement of the probabilistic data association filter is presented for tracking a single maneuvering target in clutter environment. The use of the variable dimensional structure leads the probabilistic data association filter to adjust to real motion of a target. The detection of the maneuver for the model switching is performed by the acceleration estimates taken from a bias estimator of the two stage Kalman filter. The proposed algorithm needs low computational power since it is implemented with a single filtering procedure. A simple Monte Carlo simulation was performed to compare the performance of the proposed algorithm and the IMMPDA filter.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.9
• /
• pp.381-388
• /
• 2002

This paper suggests the activation-only VSIMM estimator, applied mainly to target tracking problems. This algorithm is much simpler and easier to implement than the ordinary VSIMM algorithm. Also the activation-only VSIMM algorithm provides a substantial reduction in computation while having identical performance with the ordinary VSIMM estimator and the FSIMM estimator. More importantly, the drawbacks related to the improper termination and activation inherent to the VSIMM algorithm are eliminated in this algorithm. The performance of this estimator will be shown through a Monte Carlo simulation for maneuvering target tracking in comparison with the FSIMM and the VSIMM.