• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.6
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• pp.727-732
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• 2009

In this paper, the dependence of the Kalman filter-based emitter location algorithm on the initial estimate is investigated. Given all the LOB data, the initial estimate of the emitter location is obtained from the linear LSE algorithm with the former LOB data. Using the initial estimate, the Kalman filter algorithm is applied with the remaining LOB data to update the initial estimate. It is shown that as the number of data used in the calculation of the initial estimate increases, the accuracy of the final estimate is improved and the total computational complexity of obtaining the initial estimate and the final estimate increases. In addition, the dependence of the performance of the Kalman filter algorithm on the predefined constant is illustrated.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2615-2617
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• 2003

The Kalman filter is a recursive Linear Estimator for the linear dynamic systems(LDS) affected by two different noises called process noise and measurement noise both of which are uncorrelated white. The Expectation Maximization(EM) algorithm is employed in this paper as a preprocessor to reinforce the effectiveness of Kalman estimator. Particularly, we focus on the relation between Kalman filter and EM algorithm in the LDS. In this paper, we propose a new algorithm to improve the performance on the parameter estimation via EM algorithm, which improves the overall process of Kalman filtering. Since Kalman filter algorithm not only needs the system parameters but also is very sensitive the initial state conditions, the initial conditions decided through EM turns out to be very effective. In experiments, the computer simulation results ate provided to demonstrate the superiority of the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.67-72
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• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.7-15
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• 2000

This paper suggests an auto-tuning I'll) control algorithm that uses the advantage of PID controller and improves the system performance. The PID gains being designed by th- conventional method are tuned through the plant parameter estimation. The Extended Kalman Filter is used for the estimation. It works as an observer and noise filter. Moreover, as the plant state and the uncertain parameter could be estimated simultaneously, the proposed algorithm is very useful in the tracking control of a system with uncertain parameter. The auto-tuning I'll) controller could maintain the system performance in the case that the plant parameters are uncertain or varying. The proposed control algorithm requires a correct estimation of the plant parameter. The controller stability and the performance is considered through the stability criteria and a servo motor model. The Kalman filter estimates the most sensitive plant parameter, which is determined by the sensitivity analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1624-1630
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• 2019

With the increase in there are smart devices penetration around the world, services related to exercise checks are attracting attention. However, there is existing exercise amount measurement service does not use the altitude information, or because the use of an algorithm that does not corrected the GPS altitude error is not accurate movement distance provided have a problem. Therefore, in this paper, to improve the existing problems, Kalman-filter-based user movement distance measurement algorithm is designed and implementation of improved by using the Kalman-filter based GPS and barometric altimeter sensor fusion algorithm to improve the altitude value the accuracy and of calculate the coordinate plane distance. As a result of comparing the designed and implementation of algorithm with the existing algorithms, it is confirmed that the proposed algorithm improves the accuracy by about 2.17%.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.477-479
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• 1998

This paper presents the results of the field of an improved GPS navigation algorithm. The improved GPS navigation algorithm is a modified Kalman filter which is designed to be ideally suited to car navigation in urban area where lack of GPS visibility is the major problem because of the frequent blockage of the GPS signals by tall buildings and other structures. The method allows the user to estimate its position when the number of visible GPS satellites becomes less than four by using altitude fixing and clock bias estimation techniques. The two estimation techniques are integrated with the Kalman filter in a mutually compensating manner and it is shown that the 3-dimensional position accuracy is well maintained when the number of the visible satellites drops down to two for a reasonable period of time. The post processing results are included to show the improved performance of the modified algorithm over a normal conventional GPS Kalman filter.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.619-628
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• 2013

In this paper, we describe a strongly improved tracking method using combination of CAMshift and Kalman filter algorithm. CAMshift algorithm doesn't consider the object's moving direction and velocity information when it set the search windows for tracking. However if Kalman filter is combined with CAMshift for setting the search window, it can accurately predict the object's location with the object's present location and velocity information. By using this prediction before CAMshift algorithm, we can track fast moving objects successfully. Also in this research, we show better tracking results than conventional approaches which make use of single color information by using both color information of HSV and YCrCb simultaneously. This modified approach obtains more robust color segmentation than others using single color information.

• ETRI Journal
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• v.45 no.3
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• pp.394-403
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• 2023

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1830-1843
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• 2018

In this paper, the generation of a reference current and voltage signal based on a Kalman filter is offered for a 3-phase 4wire UPQC (Unified Power Quality Conditioner). The performance of the UPQC is improved with source voltages that are distorted due to harmonic components. Despite harmonic and frequency variations, the Kalman filter is capable enough to determine the amplitude and the phase angle of load currents and source voltages. The calculation of the first state is sufficient to identify the fundamental components of the current, voltage and angle. Therefore, the Kalman state estimator is fast and simple. A Kalman based control strategy is proposed and implemented for a UPQC in a distribution system. The performance of the proposed control strategy is assessed for all possible source conditions with varying nonlinear and linear loads. The functioning of the proposed control algorithm with a UPQC is scrutinized and validated through simulations employing MATLAB/Simulink software. Using a FPGA SPATRAN 3A DSP board, the proposed algorithm is developed and implemented. A small-scale laboratory prototype is built to verify the simulation results. The stated control scheme for the UPQC reduces the following issues, voltage sags, voltage swells, harmonic distortions (voltage and current), unbalanced supply voltage and unbalanced power factor under dynamic and steady-state operating conditions.