In this paper, we propose Time-Division-Multiplexing Tertiary Offset Carrier (TDMTOC), a novel GNSS modulation based on Tertiary Offset Carrier (TOC) modulation. The TDMTOC modulation multiplexes two three-level signals (i.e., -1, 0, and 1) while crossing over time, and is a type of TOC modulation designed specifically for signal multiplexing. The proposed modulation generates TDMTOC subcarriers of two different phases by simply combining two Binary Offset Carrier (BOC) subcarriers by addition or subtraction. TDMTOC has better correlation and spectral properties than conventional BPSK, BOC, and MBOC modulation techniques, and has good power and spectral efficiency since it can multiplex signals without power loss similar to time division multiplexing. To prove this, we introduce the multiplexing process of TDMTOC, and compare TDMTOC with Binary Phase Shift Keying (BPSK), BOC, Composite BOC (CBOC), and Time Multiplexed BOC (TMBOC) that are currently serviced in GNSS by simulations of various aspects. Through the simulation results, we prove that TDMTOC has better correlation property than modulations currently used in GNSS, less intersystem interference due to its wide spectrum property, and robustness in multipath and noise channel environments.

The Nuri rocket developed by South Korea was launched at approximately 07:00 UT on June 21, 2022. We use GPS observations obtained from the Korean GNSS network to analyze ionospheric total electron content (TEC) disturbances induced by the 2022 Nuri rocket launch. TEC disturbances are observed south over South Korea 4-5 min after the rocket launch. In addition, the maximum depletion in the vertical TEC shows approximately 8 TEC units (TECU). We also compute a horizontal velocity from initial ionospheric disturbances triggered by the 2022 Nuri rocket launch. Its velocity is about 1.4 km/s. It may be related to the rocket's flight trajectory at the observation time of the ionospheric TEC disturbance.

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

The satellite navigation deception technology disturbs the navigation solution of the receiver by generating a deceptive signal simulating the actual satellite for the satellite navigation receiver mounted on the unmanned aerial vehicle, which is the target of deception. A single spoofing technique that creates a single deceptive position and velocity can be divided into a synchronized spoofing signal that matches the code delay, Doppler frequency, and navigation message with the real satellite and an unsynchronized spoofing signal that does not match. In order to generate a signal synchronized with a satellite signal, a very sophisticated and high precision signal generation technology is required. In addition, the current position and speed of the UAV equipped with the receiver must be accurately detected in real time. Considering the detection accuracy of the current radar technology that detects small UAVs, it is difficult to detect UAVs with an accuracy of less than one chip. In this paper, we assume the asynchrony of a single spoofing signal and propose a region defense technique using multiple spoofing signals.

The public open signals from Global Navigation Satellite System (GNSS) including Global positioning system (GPS) are used widely by many peoples in the world except for the public regulated restriction signals which are encrypted. Nowadays there are growing concerns about GNSS signal spoofing which can deceive the GNSS receivers by abusing these open services. To counter these spoofing threats, many researches have been studied including array antenna techniques which can detect the direction of arrival by means of Multiple Signal Classification (MUSIC) algorithm. Originally the array antenna techniques were developed to countermeasure the jamming signal in electronic warfare by using the nulling or beamforming algorithm toward a certain direction. In this paper, we study the anti-spoofing techniques using array antenna to overcome the jamming and spoofing issues simultaneously. First, we will present the theoretical analysis results of spoofing signal response of Minimum Variance Distortionless Response (MVDR) algorithm in array antenna. Then the eigenvector algorithm of covariance matrix is suggested and verified to work with the existing anti-jamming method. The modeling and simulation are used to verify the effectiveness of the anti-spoofing algorithm. Also, the field test results show that the array antenna system with the proposed algorithms can perform the anti-spoofing function. This anti-spoofing method using array antenna is very effective in the view point of solving both the jamming and spoofing problems using the same array antenna hardware.

In order to increase the practical use and navigational application performance of the Korean Positioning System (KPS), it is required to provide interoperability with other Global Navigation Satellite System (GNSS). This kind of interoperability can be obtained by broadcasting the time offset between KPS and GNSS using a KPS navigation message. With the assumption that KPS Time (KPST) will be generated by the similar method and equipment of UTC(KRIS), the overall behavior of KPST will be close to that of UTC(KRIS). Therefore, the time offset between KPST and GPS Time (GPST) is estimated by using UTC(KRIS) instead of KPST because KPST can not available at the present time. In this paper, we describe the estimation results of the KPS to GPS Time Offset (KGTO) obtained by using a GNSS time transfer receiver which reference inputs are fed from UTC(KRIS). The estimated KGTO performance is compared to the time offset between UTC(KRIS) and UTC(USNO) which is used to generate GPST and considered as the real GPST. The time offset between UTC(KRIS) and UTC(USNO) is obtained by using the Bureau International des Poids et Mesures (BIPM) Circular T report. From the results, it is observed that KGTO can be estimated under 10 ns with the assumption that KPST will be generated by a similar method of UTC(KRIS) generation.

Inertial Navigation System (INS)/Global Navigation Satellite System (GNSS) integrated navigation system can be used for land vehicle navigation. When the GNSS signal is blocked in a dense urban area or tunnel, however, the problem of increasing the error over time is unavoidable because navigation must be performed only with the INS. In this paper, Non-Holonomic Constraints (NHC) information is utilized to solve this problem. The NHC may correct some of the errors of the INS. However, it should be noted that NHC information is not applicable to all areas within the vehicle. In other words, the lever arm effect occurs according to the distance between the Inertial Measurement Unit (IMU) mounting position and the NHC effective point, which causes the NHC condition not to be satisfied at the IMU mounting position. In this paper, an INS/GNSS/NHC integrated navigation filter is designed, and this filter has a function to compensate for the lever arm effect. Therefore, NHC information can be safely used regardless of the vehicle's driving environment. The performance of the proposed technology is verified through Monte-Carlo simulation, and the performance is confirmed through experimental test.

As intelligent autonomous driving vehicle development has become a big topic around the world, accurate reference dynamics estimation has been more important than before. Current systems generally use speed and heading information sensed from a distant point as a vehicle reference dynamic, however, the dynamics between different points are not same especially during rotating motions. In order to estimate properly estimate the reference dynamics from the information such as velocity and heading sensed at a point distant from the reference point such as center of gravity, this study proposes estimating reference dynamics from any location in the vehicle by combining the Bicycle and Ackermann models. A test system was constructed by implementing multiple GNSS/INS equipment on an Robot Operating System (ROS) and an actual car. Angle and speed errors of 10° and 0.2 m/s have been reduced to 0.2° and 0.06 m/s after applying the suggested method.

In order to provide a location-based services regardless of indoor or outdoor space, it is important to provide position information of the terminal regardless of location. Among the wireless/mobile communication resources used for this purpose, Long Term Evolution (LTE) signal is a representative infrastructure that can overcome spatial limitations, but the positioning method based on the location of the base station has a disadvantage in that the accuracy is low. Therefore, a fingerprinting technique, which is a pattern recognition technology, has been widely used. The simplest yet widely applied algorithm among Fingerprint positioning technologies is k-Nearest Neighbors (kNN). However, in the kNN algorithm, it is difficult to find the optimal K value with the lowest positioning error for each location to be estimated, so it is generally fixed to an appropriate K value and used. Since the optimal K value cannot be applied to each estimated location, therefore, there is a problem in that the accuracy of the overall estimated location information is lowered. Considering this problem, this paper proposes a technique for adaptively varying the K value by using a Convolutional Neural Network (CNN) model among Artificial Neural Network (ANN) techniques. First, by using the signal information of the measured values obtained in the service area, an image is created according to the Physical Cell Identity (PCI) and Band combination, and an answer label for supervised learning is created. Then, the structure of the CNN is modeled to classify K values through the image information of the measurements. The performance of the proposed technique is verified based on actual data measured in the testbed. As a result, it can be seen that the proposed technique improves the positioning performance compared to using a fixed K value.