• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.241-247
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• 2016

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 ${\alpha}-{\beta}-{\gamma}$ 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 ${\alpha}$, ${\beta}$ and ${\gamma}$ are determined from set values of the damping parameter, ${\xi}$. Optimization of the damping parameter, ${\xi}$, 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1511-1519
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• 2010

In this paper, we proposed and analyzed a RSSI based cooperative localization algorithm considering wireless propagation characteristics in indoor and outdoor environments for wireless sensor networks, which can estimate the BN position. The conventional RSSI based estimation scheme has low precision ranging due to instability propagation characteristics by time variable. Hence, we implemented ray-launching simulator for analysis of propagation characteristics in 4 case, and experimented proposed localization scheme with 4 RN and 1 to 5 BN. Simulation results show that NLCA has estimation error as 2m-3.5m, however, proposed CLA/ECLA has 1.3m-2.5m/0.5m-1.2m by same environments. Therefore, if we can consider channel characteristics, the proposed algorithm provides higher localization accuracy than RSSI based conventional one.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.69-74
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• 2016

A technique for improving estimation accuracy is introduced in order to locate the impact position of artillery shell during the weapon scoring test. Study on localization of impacts using acoustic measurement has been conducted and the usability of sensor array is verified with experiments. When the blast occurs above the ground in the firing range, the acoustic sensor above the ground can measure the directly propagated sound with the ground-reflected one. In this study, a method for reducing estimation error by using the reflection signal measurements based on the time difference of arrival method. Considering the reflection sound works as same as placing a virtual sensor symmetrically through the ground. This idea enables a virtual three-dimensional array configuration with a two-dimensional plane array above the ground as such. The time difference between the direct and the reflected propagations can be estimated using cepstrum analysis. Performance test has been made in the simulation experiment in the football size area.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.171-176
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• 2019

Drones are highly utilized because they can efficiently acquire long-distance videos. In drone operation, the speed, which is the magnitude of the velocity, can be set, but the moving direction cannot be set, so accurate information about the drone's movement should be estimated. In this paper, we estimate the velocity of the drone moving at a constant speed and direction. In order to estimate the drone's velocity, the displacement of the target frame to minimize the sum of absolute difference (SAD) of the reference frame and the target frame is obtained. The ground truth of the drone's velocity is calculated using the position of a certain matching point over all frames. In the experiments, a video was obtained from the drone moving at a constant speed at a height of 150 meters. The root mean squared error (RMSE) of the estimated velocities in x and y directions and the RMSE of the speed were obtained showing the reliability of the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.154-159
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• 2019

When measuring the radiating noise of an underwater moving source, the range information between the acoustic source and the receiver is an important evaluation factor, and the measurement standards such as a receiver position, a moving source depth and a speed are set. Although there is a method of using the cross correlation as a method of finding the range of the underwater moving source, this method requires a time synchronization process. In this paper, we proposed the method to estimate the range by comparing the Doppler frequency difference of the theoretically calculated multipath signal with the Doppler frequency difference of the multipath signal estimated from the received signal. The proposed method does not require a separate time synchronization process. Simulations were performed to verify the performance, and the ranging error of the proposed method reduced by about 95 % than that of the conventional method.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.10-21
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• 2023

For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1549-1556
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• 2016

By increased moving speed of aircraft, estimating location of itself becomes more important than ever. This requirement is satisfied by appearance of GPS, however it is useless when signal reception from satellite is not good enough by interruption, for example, traffic jamming. Applying link for communication to additional positioning system is capable of providing relative position of aircraft. Estimating location with link for communication is done without additional equipment but with signal processing based on correlation of received signal. ESPRIT is one of the representative algorithm among them. Estimating correlation matrix is possible to have error since it includes average operation needs enough number of samples not impractical. Therefore we propose algorithm that defines, estimates and removes error matrix of correlation. Proposing algorithm shows better performance than previous one when transmitters are close.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.105-111
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• 2017

In this paper, the analysis results are shown about several weights of Weighted K-Nearest Neighbor method, Recently, it is employed for the indoor positioning technologies using WiFi fingerprint which has been actively studied. In spite of the simplest feature, the W-KNN method shows comparable performance to another methods using WiFi fingerprint technology. So W-KNN method has employed in the existing indoor positioning system. It shows positioning error performance according to data preprocessing and weight factor, and the analysis on the weight is very important. In this paper, based on the real measured WiFi fingerprint data, the estimation error is analyzed and the performances are compared, for the case of data processing methods, of the weight of average, variance, and distance, and of the averaging several position of number K. These results could be practically useful to construct the real indoor positioning system.

• The Journal of Korean Physical Therapy
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• v.24 no.2
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• pp.66-72
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• 2012

Purpose: The purpose of this study is to explore the importance of the image distortion correction in the cross sectional area measurement for the iliopsas muscle, tensor fasciae latae muscle, gluteus maximus muscle and the knee extensor muscles, by using (magnetic resonance imaging) MRI. Methods: This study was performed using an open 0.32T MRI system. To estimate the image distortion, T1 images for an AAPM homogeneity/linearity phantom were acquired, and the region in which the maximum geometric distortion was less than or equal to the pixel size (1.6 mm) of the images, it was defined as the distortion correction-free region. The T2 images for a human subject's pelvis and thigh in normal positions were obtained. Then, after the regions of interest in the pelvis and thigh were moved into the distortion correction-free region, T2 images for the pelvis and thigh were scanned with the same imaging parameters used in the previous T2 imaging. The cross-sectional areas were measured in the two T2 images that were obtained in the normal position, and the distortion correction-free region, as well as the area error caused by geometric image distortion was calculated. Results: The geometrical distortion is gradually increased, from the magnet center to the outer region, in axial and coronal plane. The cross-sectional area error of gluteus maximus muscle and the knee extensors was as high as 9.27% and 3.16% in before and after distortion correction, respectively. Conclusion: The cross-sectional area of the muscles that suffered from the geometrical distortion is necessary to correct for the estimation of the intervention.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.395-401
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• 2018

Recently, most of mobile devices are equipped with GNSS (Global Navigation Satellite System). When the GNSS signal is available, it is easy to obtain position information. However, GNSS is not suitable solution for indoor localization, since the signals are normally not reachable inside buildings. A wide varieties of technology have been developed as a solution for indoor localization such as Wi-Fi, beacons, and inertial sensor. With the increased sensor combinations in mobile devices, mobile devices also became feasible to provide a solution, which based on PDR (Pedestrian Dead Reckoning) method. In this study, we utilized the combination of three sensors equipped in mobile devices including accelerometer, digital compass, and gyroscope and applied three representative PDR methods. The proposed methods are done in three stages; step detection, step length estimation, and heading determination and the final indoor localization result was evaluated with terrestrial LiDAR (Light Detection And Ranging) data obtained in the same test site. By using terrestrial LiDAR data as reference ground truth for PDR in two differently designed experiments, the inaccuracy of PDR methods that could not be found by existing evaluation method could be revealed. The firstexperiment included extreme direction change and combined with similar pace size. Second experiment included smooth direction change and irregular step length. In using existing evaluation method which only checks traveled distance, The results of two experiments showed the mean percentage error of traveled distance estimation resulted from three different algorithms ranging from 0.028 % to 2.825% in the first experiment and 0.035% to 2.282% in second experiment, which makes it to be seen accurately estimated. However, by using the evaluation method utilizing terrestrial LiDAR data, the performance of PDR methods emerged to be inaccurate. In the firstexperiment, the RMSEs (Root Mean Square Errors) of x direction and y direction were 0.48 m and 0.41 m with combination of the best available algorithm. However, the RMSEs of x direction and y direction were 1.29 m and 3.13 m in the second experiment. The new evaluation result reveals that the PDR methods were not effective enough to find out exact pedestrian position information opposed to the result from existing evaluation method.