Seung-Hyun Kong

Detecting and classifying the modulation scheme of the intercepted noisy low probability of intercept (LPI) radar signals in real time is a necessary survival technique required in the electronic warfare systems. Therefore, LPI radar waveform recognition technique (LWRT) has gained an increasing attention recently. In this paper, we propose a convolutional neural network (CNN)-based LWRT, where the input and hyperparameters of the CNN, such as the input size, number of filters, filter size, and

In the cold start of a Global Navigation Satellite Systems (GNSS) receiver, fast acquisition of the GNSS signal requires either an extensive usage of hardware resources for massive parallel correlators or a high computational complexity for fast Fourier transform (FFT) and inverse FFT operations. Because GNSS uses direct-sequence spread spectrum (DSSS) signaling with binary phase-shift keying (BPSK) or with BPSK and binary offset carrier, any GNSS signal can have a sparse representation so that

Gaussian scatterer distribution model (GSDM) is one of the most interesting geometrically based scatterer distribution models for spatial and temporal properties of wireless channels in most multipath environments. The GSDM assumes a circular scattering region around a mobile station (MS), and the scatter density decreases with the distance from the MS. In this paper, the time of arrival (TOA), angle of departure (AOD), and joint TOA/AOD probability density functions (pdfs) of down link are deri

In urban environments one of the causes of pseudo-range measurement error in Global Positioning System (GPS) is short-delay multipaths due to the scatterers around the receiver. Therefore knowledge of the temporal distribution of GPS multipaths based on a statistical scatterer distribution in an urban environment is essential to estimating the positioning performance and to developing an efficient multipath mitigation technique for urban GPS applications. The work presented here introduces a sca

Higher sensitivity and faster acquisition can be two conflicting goals for a global navigation satellite system (GNSS) acquisition function, and both of the goals must be considered in the development of GNSS signal processing techniques to meet the demands for location-based services (LBSs) in GNSS-challenged environments. This article introduces the fundamentals of GNSS acquisition functions and various GNSS acquisition techniques for new GNSS signals and investigates recent acquisition techni

In the cooperative vehicular positioning networks (CVPN), non-line-of-sight (NLOS) delay in the vehicle-to-vehicle (V2V) ranging and malicious attacks, such as location spoofing and the manipulation of V2V ranging, can be significant threats to vehicle safety. However, the resulting observation from a vehicle in the event of any of the threats is the same; difference observation between the measured range and the Euclidian distance based on vehicles' shared location coordinates. In this paper, w

Acquisition of an incoming long pseudo-noise (PN) code signal requires a fast hypothesis testing function for a large number of code phase hypotheses. In addition, when a transmitter is moving at a high speed, the hypothesis testing function needs to search for the signal in a 2-Dimensional (2D) search space that includes all possible combinations of code phase hypothesis and Doppler frequency hypothesis. Since a receiver has limited hardware resources (in terms of number of correlators and comp

The accuracy of the observed time difference of arrival (OTDOA) in the long-term evolution (LTE) systems depends on the accuracy of the time of arrival (TOA) measurements, which are often corrupted by various errors caused by non-light-of-sight propagation, multipath interference, noise, and path detection techniques. Furthermore, signal bandwidth, channel condition, distance from the evolved node-B, and scatterer distribution are the affecting parameters on the OTDOA accuracy. Since the user eq

Successful and fast Global Navigation Satellite System (GNSS) positioning in indoor environments can enable many location based services (LBS). However, fast indoor GNSS positioning has been one of the biggest challenges for GNSS receivers due to the huge computational cost. To detect weak GNSS signals in indoor environments, a GNSS receiver should perform numerous correlations with a longer coherent integration interval for a denser Doppler frequency search, which is computationally too expensi

Existing point cloud feature learning networks often learn high-semantic point features representing the global context by incorporating sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation. However, this process may result in a substantial loss of granular information due to the sampling operation and the widely-used max pooling feature aggregation, which neglects information from non-maximum point features. Consequently, the resulting high-semantic point

Successful position fix in harsh environments such as indoors and dense urban canyons is a strongly required capability for an assisted global positioning system (A-GPS) receiver. In recently developed cellular networks, receiving fine time assistance and maintaining high-frequency accuracy using downlink measurements are not possible for A-GPS receivers, since node-Bs are asynchronous and are not equipped with a source for precise time and frequency. In this paper, we propose a correlator-based

There has been an increasing level of demand for faster, safer and greener transportation systems with higher levels of capacity and convenience, though the implementation of transportation systems overall is often restricted by geographical limitations, presenting a challenge to scientists and engineers in the field. However, we have been witnessing the evolution of the transportation systems over the last few decades, and at present we are facing a new era of intelligent transportation systems

Position and time are some of the most vital pieces of information for convenience, security, and safety in our daily lives and have enabled innovative advancements in many science and engineering fields. Since Global Positioning System (GPS) became available to the public, GPS has been applied not only to smartphones and vehicular navigation systems that we use every day, but also to diverse areas such as Internet-of-Things (IOT), synchronization, cellular communications, military, geodesy, agr

BPSK modulated GPS L1 CA signal is the most widely used GNSS signal to date, and the first path detection (FPD) of the conventional GPS L1 CA signals is the most challenging problem to ensure reliable GPS positioning in multipath environments. In this paper, we propose an FPD network (FPDN) based on multi-layer perceptron (MLP)-Mixer to extract the first path from the discrete autocorrelation function (ACF) output accurately with low computational cost. In addition, the proposed FPDN is useful i