• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.7-14
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• 2012

Due to inaccurate safe navigation estimates, maritime accidents have been occurring consistently. In order to solve this, the precise positioning technology using carrier phase information is used, but due to high buildings near inland waterways or inclination, satellite signals might become weak or blocked for some time. Under this weak signal environment for some time, the GPS raw measurements become less accurate so that it is difficult to search and maintain the integer ambiguity of carrier phase. In this paper, a method to generate code and carrier phase measurements under this environment and maintain resilient navigation is proposed. In the weak signal environment, the position of the receiver is estimated using an inertial sensor, and with this information, the distance between the satellite and the receiver is calculated to generate code measurements using IGS product and model. And, the carrier phase measurements are generated based on the statistics for generating fractional phase. In order to verify the performance of the proposed method, the proposed method was compared for a fixed blocked time. It was confirmed that in case of a weak or blocked satellite signals for 1 to 5 minutes, the proposed method showed more improved results than the inertial navigation only, maintaining stable positioning accuracy within 1 m.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.684-690
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• 2015

This paper presents a modified residual-based EKF (Extended Kalman Filter) for performance improvement of indoor positioning using WiFi RSSI (Received Signal Strength Indicator) measurement. Radio signal strength in indoor environments may have irregular attenuation characteristics due to obstacles such as walls, furniture, etc. Therefore, the performance of the RSSI-based positioning with the conventional trilateration method or Kalman filter is insufficient to provide location-based accurate information services. In order to enhance the performance of indoor positioning, in this paper, error analysis of the distance calculated by using the WiFi RSSI measurement is performed based on the radio propagation model. Then, an IARM (Irregularly Attenuated RSSI Measurement) error is defined. Also, it shows that the IARM error is included in the residual of the positioning filter. The IARM error is always positive. So, it is presented that the IARM error can be estimated by taking the absolute value of the residual. Consequently, accurate positioning can be achieved based on the IEM (IARM Error Mitigated) EKF with the residual modified by using the estimated IARM error. The performance of the presented IEM EKF is verified experimentally.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.957-964
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• 2010

In this paper, we propose an indoor positioning algorithm based on 3 points near field angle-of-arrival estimation without side information. The conventional angle-of-arrival based positioning scheme requires the distance between the listener and the center of two points which is obtained by a received signal strength based range estimation. However, a received signal strength is affected by structure of room, placement of furniture, and characteristic of signal, these effects cause a large error to estimation of angle. In this paper, the proposed positioning scheme based on near field angle-of-arrival estimation can be used to estimate the position of listener without a prior distance information, just using time-difference-of-arrival information given from 3 points microphones. The performance of the proposed scheme is shown by cumulative distribution function of root mean squared error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12A
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• pp.982-990
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• 2009

The IEEE 802.15.4 wireless sensor network is composed of the unique sensor devices to monitor and collect physical or environmental conditions. The interests in a positioning technology, which is one of the environment monitoring technologies, are gradually increased according to the development of the sensor technology and IT infrastructure. Generally, it is difficult for the positioning system using RSSI (Received Signal Strength Indication) based implementation to get accurate position because of obstacles, RF wave's delay and multipath. Therefore, in this paper, we investigate the improved positioning technologies for RSSI-based positioning system. This paper also proposes the enhanced scheme to improve the accuracy of positioning system by applying the Gaussian Filter algorithm, which is widely used for enhancing the performance of image processing system. For the implementation of proposed scheme, we firstly make a look-up tables, which represent the distance between target node and master node and corresponding RSSI value of each target node which are recorded as an average value after investigating the characteristics of attenuation of transmitted signal By applying the pre-determined look-up tables and Gaussian Filtering in the proposed scheme, we analyzed the positioning performance and compared with other conventional RSSI-based positioning algorithms.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.157-166
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• 2023

Several experiments were carried out to analyze the impact of the modernized Global Positioning System (GPS) L2C signal on pseudorange-based point positioning. Three dual-frequency positioning algorithms, ionosphere-free linear combination, ionospheric error estimation, and simple integration, were used, and the results were compared with those of Standard Point Positioning (SPP). An analysis was conducted to determine the characteristics of each dual-frequency positioning method, the impact of the magnitude of ionospheric error, and receiver grade. Ionosphere-free and ionospheric error estimation methods can provide improved positioning accuracy relative to SPP because they are able to significantly reduce the ionospheric error. However, this result was possible only when the ionospheric error reduction effect was greater than the disadvantage of these dual-frequency positioning algorithms such as the increment of multipath and noise, impact of uncertainty of unknown parameter estimation. The RMSE of the simple integration algorithm was larger than that of SPP, because of the remaining ionospheric error. Even though the receiver grade was different, similar results were observed.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.381-388
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• 2022

In 2021, development of a regional satellite navigation system called KPS was approved. In this regard, various studies are in progress, but there is no published signal model. So, in relation to the user segment, it is necessary to design a user receiver, but there is no information. Therefore, in this paper, we assume a signal model that can be a candidate signal for KPS based on related studies. This signal uses CNAV-2 structure navigation message, truncated Gold code and BPSK modulation. Based on this signal, a simulator is designed that can be used for receiver design later. The simulator consists of a signal generator and a signal transmitter, and is verified using a software receiver and spectrum analyzer.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.50-55
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• 2023

Due to the unavailability of global positioning system (GPS) indoors, various indoor pedestrian positioning methods have been designed to estimate the position of the user using received signal strength (RSS) measurements from radio beacons, such as wireless fidelity (WiFi) access points and Bluetooth low energy (BLE) beacons. In indoor environments, radio-frequency (RF) signals are unpredictable and change over space and time because of multipath associated with reflection and refraction, shadow fading caused by obstacles, and interference among different devices using the same frequencies. Therefore, the outliers in the positional information obtained from the indoor positioning method based on RSS measurements occur often. For this reason, the performance of the positioning method can be degraded by the characteristics of the RF signal. To resolve this issue, a map-matching (MM) algorithm based on maximum probability (MP) estimation is applied to the indoor positioning method in this study. The MM algorithm locates the aberrant position of the user estimated by the positioning method within the limits of the adjacent pedestrian passages. Empirical experiments show that the positioning method can achieve higher positioning accuracy by leveraging the MM algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.397-402
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• 2019

Recently, with the emergence of autonomous vehicles and the increasing interest in safety, a variety of research has been being actively conducted to precisely estimate the position of a vehicle by fusing sensors. Previously, researches were conducted to determine the location of moving objects using GNSS (Global Navigation Satellite Systems) and/or IMU (Inertial Measurement Unit). However, precise positioning of a moving vehicle has lately been performed by fusing data obtained from various sensors, such as LiDAR (Light Detection and Ranging), on-board vehicle sensors, and cameras. This study is designed to enhance kinematic vehicle positioning performance by using feature-based recognition. Therefore, an analysis of the required precision of the observations obtained from the images has carried out in this study. Velocity and attitude observations, which are assumed to be obtained from images, were generated by simulation. Various magnitudes of errors were added to the generated velocities and attitudes. By applying these observations to the positioning algorithm, the effects of the additional velocity and attitude information on positioning accuracy in GNSS signal blockages were analyzed based on Kalman filter. The results have shown that yaw information with a precision smaller than 0.5 degrees should be used to improve existing positioning algorithms by more than 10%.

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.772-778
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• 2018

We have previously proposed a preliminary cut-off indoor positioning scheme considering the reference point with the same signal similarity. This scheme estimates the position using the relative rank of the peak of received signal strength from the beacons around user. However, this scheme has a weak point with lower accuracy when there are more than one nearest reference points having the same signal similarity. In order to tackle this, we propose a weighted preliminary cut-off indoor positioning scheme. Firstly, if the above problem occurs, the similarity to the peak of signal strength is considered as well as the relative rank. Next, weights are assigned to the nearest reference points using the similarity to the peak of the received signal strength. Finally, the user's position is estimated by applying the weights. As a result, the weighted preliminary cut-off scheme improves the positioning accuracy by about 7.9% compared to the previous scheme.