• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5464-5488
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• 2019

The multi-population genetic algorithm in multi-target paths coverage has become a top choice for many test engineers. Also, information sharing strategy can improve the efficiency of multi-population genetic algorithm to generate multi-target test data; however, there is still space for some improvements in several aspects, which will affect the effectiveness of covering the target path set. Therefore, a multi-target paths coverage strategy is proposed by improving multi-population genetic algorithm based on individual information sharing among populations. It primarily contains three aspects. Firstly, the behavior of the sub-population covering corresponding target path is improved, so that it can continue to try to cover other sub-paths after covering the current target path, so as to take full advantage of population resources; Secondly, the populations initialized are prioritized according to the matching process, so that those sub-populations with better path coverage rate are executed firstly. Thirdly, for difficultly-covered paths, the individual chromosome features which can cover the difficultly-covered paths are extracted by utilizing the data generated, so as to screen those individuals who can cover the difficultly-covered paths. In the experiments, several benchmark programs were employed to verify the accuracy of the method from different aspects and also compare with similar methods. The experimental results show that it takes less time to cover target paths by our approach than the similar ones, and achieves more efficient test case generation process. Finally, a plug-in prototype is given to implement the approach proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.313-322
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• 2014

This paper is to survey and put in perspective the working methods of multi-target tracking in clutter. This paper includes theories and practices for data association and related filter structures and is motivated by increasing interest in the area of target tracking, security, surveillance, and multi-sensor data fusion. It is hoped that it will be useful in view of taking into consideration a full understanding of existing techniques before using them in practice.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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• pp.1226-1230
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• 2001

Multi-target tracking systems need to tracking several targets simultaneously. To track a target among the measurements of several targets, data association is needed. In this paper, a method using the cou-pled confidence region of predicted target position is proposed. The proposed method shows good performance in simulations of multi-target tracking systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.3
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• pp.445-452
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• 2010

Adequate segmentation of target objects from the background plays an important role for the performance of automatic target recognition(ATR) system. This paper presents a new segmentation algorithm using fuzzy thresholding to extract a target. The proposed algorithm consists of two steps. In the first step, the region of interest(ROI) including the target can be automatically selected by the proposed robust method based on the frame difference of each image sensor. In the second step, fuzzy thresholding with a proposed membership function is performed within the only ROI selected in the first step. The proposed membership function is based on the similarity of intensity and the adjacency of target area on each image. Experimental results applied to real CCD/IR images show a good performance and the proposed algorithm is expected to enhance the performance of ATR system using multi-sensors.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.99-99
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• 2000

An effect ive filter algorithm that can manage radar beam pointing efficiently is needed to track multi-target in the air. For effective beam management the filter has lobe good enough to predict future position of target and based on this filter output radar beam is control led to point toward the predicted target position in the air. In this paper, we investigate the ${\alpha}$-${\beta}$ filter known for its brief filter structure with the steady-state Kalman filter gain, the ruv filter, and the coordinate-transformed filter that can decouple the measurement noise variance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1476-1483
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• 2007

In a multi-sensor target tracking systems, the local sensors have the role of tracking the target and transferring the measurements to the fusion center. The measurements from the same target can arrive out of sequence called, the out-of-sequence measurements(OOSMs). The OOSM can arise in a form of single-lag or multi-lag throughout the transfer at the fusion center. The recursive retrodiction step was proposed to update the current state estimates with the multi-lag OOSM from the several previous papers. The real world has the possible situations that the maneuvering target informations can arrive at the fusion center with the random clutter in the possible OOSMs. In this paper, we incorporate the IMM-MPDA(Interacting Multiple Model - Most Probable Data Association) into the multi-lag OOSM update. The performance of the IMM-MPDA filter with multi-lag OOSM update is analyzed for the various clutter densities, OOSM lag numbers, and target maneuvering indexes. Simulation results show that IMM-MPDA is sufficient to be used in out of sequence environment and it is necessary to correct the current state estimates with OOSM except a very old OOSM.

• Journal of Ocean Engineering and Technology
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• v.28 no.5
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• pp.440-451
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• 2014

A multi-static SONAR system consists of the transmitters and receivers separately in space. The active target echoes are received along the transmitter-target-receiver path and depend on the shape and aspect angle of the submerged objects at each receiver. Thus, the target echo algorithm used with a mono-static system, in which the transmitter and receiver are located at the same position, has limits in simulating the target echoes for a multi-static SONAR system. In this paper, a target echo modeling procedure for a 3D submerged object in space is described based on the Kirchhoff approximation, and the SONAR system is extended to a multi-static SONAR system. The scattered field from external structures is calculated on the visible surfaces, which is determined based on the locations of the transmitter and receiver. A series of experiments in an acoustic water tank was conducted to measure the target echoes from scaled targets with a single transmitter and 16 receivers. Finally, the numerical results were compared with experimental results and shown to be useful for simulating the target echoes/target strength in a multi-static SONAR system.

• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.