• International Journal of Precision Engineering and Manufacturing
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• v.7 no.2
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• pp.41-45
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• 2006

This paper deals with human spatial cognition using visual and auditory stimulation. More specially, this investigation is to observe the relationship between the head and the eye motor system for the localization of visual target direction in space and to try to describe what is the role of right-side versus left-side pinna. In the experiment of visual stimulation, nineteen red LEDs (Luminescent Diodes, Brightness: $210\;cd/^2$) arrayed in the horizontal plane of the surrounding panel are used. Here the LEDs are located 10 degrees apart from each other. Physiological parameters such as EOG (Electro-Oculography), head movement, and their synergic control are measured by BIOPAC system and 3SPACE FASTRAK. In the experiment of auditory stimulation, one side of the pinna function was distorted intentionally by inserting a short tube in the ear canal. The localization error caused by right and left side pinna distortion was investigated as well. Since a laser pointer showed much less error (0.5%) in localizing target position than FASTRAK (30%) that has been generally used, a laser pointer was used for the pointing task. It was found that harmonic components were not essential for auditory target localization. However, non-harmonic nearby frequency components was found to be more important in localizing the target direction of sound. We have found that the right pinna carries out one of the most important functions in localizing target direction and pure tone with only one frequency component is confusing to be localized. It was also found that the latency time is shorter in self moved tracking (SMT) than eye alone tracking (EAT) and eye hand tracking (EHT). These results can be used in further study on the characterization of human spatial cognition.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.959-966
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• 2015

In this paper, we propose an indoor visible light communication(VLC) localization using Walsh code which can identify overlapped signals transmitted from the different LED sources as each of orthogonal signal at a receiver and using Manchester code which can eliminate the flicker of LED light and maintain a constant brightness. The proposed system can estimate the relative position of the receiver by using Lambertian radiation properties and trilateration method that are applied to the location information of fixed LED sources and the received signals from them. In order to verify the feasibility of the proposed system, we carried out the simulation in an indoor space with $6{\times}6{\times}1.5m^3$ installed LED lamps of 16. The simulation result shows that the proposed method achieves an average positioning error of 0.0536 m and a maximum positioning error of 0.2977 m.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5785-5804
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• 2019

The information of localization is a fundamental requirement in wireless sensor network (WSN). The method of distance vector-hop (DV-Hop), a range-free localization algorithm, can locate the ordinary nodes by utilizing the connectivity and multi-hop transmission. However, the error of the estimated distance between the beacon nodes and ordinary nodes is too large. In order to enhance the positioning precision of DV-Hop, fast triangle flip bat algorithm, which is based on curve strategy and rank transformation (FTBA-TCR) is proposed. The rank is introduced to directly select individuals in the population of each generation, which arranges all individuals according to their merits and a threshold is set to get the better solution. To test the algorithm performance, the CEC2013 test suite is used to check out the algorithm's performance. Meanwhile, there are four other algorithms are compared with the proposed algorithm. The results show that our algorithm is greater than other algorithms. And this algorithm is used to enhance the performance of DV-Hop algorithm. The results show that the proposed algorithm receives the lower average localization error and the best performance by comparing with the other algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.463-468
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• 2010

The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometeric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm(called M-SLAM).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.204-211
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• 2020

In this paper, we propose an optimal deep learning network architecture for main object location detection through weak supervised learning. The proposed network adds convolution blocks for improving the localization accuracy of the main object through weakly-supervised learning. The additional deep learning network consists of five additional blocks that add a composite product layer based on VGG-16. And the proposed network was trained by the method of weakly-supervised learning that does not require real location information for objects. In addition, Grad-CAM to compensate for the weakness of GAP in CAM, which is one of weak supervised learning methods, was used. The proposed network was tested through the CUB-200-2011 data set, we could obtain 50.13% in top-1 localization error. Also, the proposed network shows higher accuracy in detecting the main object than the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.494-500
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• 2020

This paper proposes a new distance estimation technique for indoor localization in ultra wideband (UWB) systems. The proposed technique is based on recurrent neural network (RNN), one of the deep learning methods. The RNN is known to be useful to deal with time series data, and since UWB signals can be seen as a time series data, RNN is employed in this paper. Specifically, the transmitted UWB signal passes through IEEE802.15.4a indoor channel model, and from the received signal, the RNN regressor is trained to estimate the distance from the transmitter to the receiver. To verify the performance of the trained RNN regressor, new received UWB signals are used and the conventional threshold based technique is also compared. For the performance measure, root mean square error (RMSE) is assessed. According to the computer simulation results, the proposed distance estimator is always much better than the conventional technique in all signal-to-noise ratios and distances between the transmitter and the receiver.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.29-36
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• 2012

In Wireless Sensor Network(WSN)s, the detection of precise location of each node is essential for utilizing sensing data acquired from sensor nodes effectively. Among various location methods, the received signal strength(RSS) based localization scheme is mostly preferable in many applications because it can be easily implemented without any additional hardware cost. Since a RSS-based localization scheme is mainly affected by radio channel or obstacles such as building and mountain between two nodes, the localization error can be inevitable. To enhance the accuracy of localization in RSS-based localization scheme, a number of RSS measurements are needed, which results in the energy consumption. In this paper, a RSS based localization using Bayesian Compressive Sensing(BSS) with path-loss exponent estimation is proposed to improve the accuracy of localization in the energy-efficient way. In the propose scheme, we can increase the adaptative, reliability and accuracy of localization by estimating the path-loss exponents between nodes, and further we can enhance the energy efficiency by the compressive sensing. Through the simulation, it is shown that the proposed scheme can enhance the location accuracy of multiple unknown nodes with fewer RSS measurements and is robust against the channel variation.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.358-367
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• 2016

SSL (Sound Source Localization) has been applied to several applications such as man-machine interface, video conference system, smart car and so on. But in the process of sound source localization, angle estimation error is occurred mainly due to the non-linear characteristics of the sine inverse function. So an approach was proposed to decrease the effect of this non-linear characteristics, which divides the microphone's covering space into narrow regions. In this paper, we proposed an optimal space dividing way according to the pattern of microphone array. In addition, sound source's 2-dimensional position is estimated in order to evaluate the performance of this dividing method. In the experiment, GCC-PHAT (Generalized Cross Correlation PHAse Transform) method that is known to be robust with noisy environments is adopted and triangular pattern of 3 microphones and rectangular pattern of 4 microphones are tested with 100 speech data respectively. The experimental results show that triangular pattern can't estimate the correct position due to the lower space area resolution, but performance of rectangular pattern is dramatically improved with correct estimation rate of 67 %.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.820-827
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• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).