Waveform generation is essential for studying signal propagation and channel characteristics, particularly for objects that are conceptualized but still need to be operational. We introduce a comprehensive guide on creating synthetic signals using channel and delay coefficients derived from the Quasi-Deterministic Radio Channel Generator (QuaDRiGa), which is recognized as a 3GPP-3D and 3GPP 38.901 reference implementation. The effectiveness of the proposed synthetic waveform generation method is validated through accurate estimation of code delay and Doppler shift. This validation is achieved using both the parallel code phase search technique and the conventional tracking method applied to satellites. As the method of integrating channel and delay coefficients to create synthetic waveforms is the same for satellite, HAPS, and gNB PRS, validating this method on synthetic satellite signals could potentially be extended to HAPS and gNB PRS as well. This study could significantly contribute to the field of heterogeneous navigation systems.

This paper addresses a game of guarding a territory with several invaders and several defenders. Although there are a plethora of studies for the single-invader single-defender scenario, or single-invader multi-defender case, there are few articles on the multi-invader case. The reason is the assignment problem. Each defender must know the policy for capturing each invader. In addition, each defender must choose an invader to capture during the game. We proposed a hierarchical reinforcement learning (HRL) process to solve the assignment problem in a multi-invader multi-defender game of guarding a territory. In the proposed method, a higher-level policy is able to change the assignment in the middle of the game as the game environment is changing. The proposed method is also examined on the game of active target defence. It is shown that the proposed method can solve the assignment problem without any knowledge of an external optimal policy, such as geometric approaches.

In this paper, a deceptive version of guarding a territory in a grid world is proposed. Like the original version, a defender tries to intercept an invader before it invades the targets. However, the discerning invader can deceive the defender about its real goal so that it can improve its performance. On the other hand, the defender tries to confront the invader by guessing its true goal. A two-level policy is obtained via reinforcement learning (RL). In the lower level, the invader and the defender learn their optimal policies to invade or defend a particular territory. In the higher level, the invader learns which territory it should pretend to invade in order to manipulate the defender's belief function. A multiagent reinforcement learning (MARL) algorithm is implemented for obtaining the optimal policies via the minimax Q-learning algorithm at the lower level. Whereas for the higher-level policy a single-agent Q-learning algorithm is utilized. Results of different reward functions are compared. The results show that the invader can improve its performance by taking advantage of deception.

This paper presents a low-cost real-time lane-determination system that fuses micro-electromechanical systems inertial sensors (accelerometers and gyroscopes), global navigation satellite system (GNSS), and commercially available road network maps. The system can be used for intelligent transportation systems, telematics applications, and autonomous driving. The system does not depend on visual markings or highly precise GNSS technology, such as DGPS or RTK, and it does not need explicit lane-level resolution maps. High-resolution estimation of the vehicle's position, velocity, and orientation is implemented by fusing inertial sensors with GNSS in a loosely coupled mode using extended Kalman filter. A curve-to-curve road-level map-matching is implemented using a hidden Markov model followed by a least-square regression step that estimates the vehicle's lane. The system includes a lane-change detector based on inertial sensors and the filtered vehicle's state. The system has been realized in real time and tested extensively on real-road data. Experiments showed robust map-matching in challenging road intersections and a 97.14% lane-determination success rate.

Context-awareness and Location-Based-Services are of great importance in mobile computing environments. Although fingerprinting provides accurate indoor positioning in Wireless Local Area Networks (WLAN), difficulty of offline site surveys and the dynamic environment changes prevent it from being practically implemented and commercially adopted. This paper introduces a novel client/server-based system that dynamically estimates and continuously calibrates a fine radio map for indoor positioning without extra network hardware or prior knowledge about the area and without time-consuming offline surveys. A modified Bayesian regression algorithm is introduced to estimate a posterior signal strength probability distribution over all locations based on online observations from WLAN access points (AP) assuming Gaussian prior centered over a logarithmic pass loss mean. To continuously adapt to dynamic changes, Bayesian kernels parameters are continuously updated and optimized genetically based on recent APs observations. The radio map is further optimized by a fast features reduction algorithm to select the most informative APs. Additionally, the system provides reliable integrity monitor (accuracy measure). Two different experiments on IEEE 802.11 networks show that the dynamic radio map provides 2-3m accuracy, which is comparable to results of an up-to-date offline radio map. Also results show the consistency of estimated accuracy measure with actual positioning accuracy.