Signal quality monitoring is an essential portion of the Global Positioning System (GPS) enterprise. Through monitoring, accuracy and normality are achieved for each satellite and signal. GPS users rely on this information from monitoring stations to ensure safe and successful operation. Traditional monitoring has been performed with hardware receivers or custom radio frequency front-ends. These receivers have known and measured impacts on the distortions of the received signal. This research aims to elucidate and quantify the amount of distortion introduced by the front-ends of general-purpose commeroutfitted with SDRs are more flexible and cheaper than those without SDRs and can quickly adapt to newly launched or reconfigured navigation satellites. This research quantifies the amount of signal distortions introduced by a handful of SDR front-ends when subjected to consistent navigation signals from a high-quality simulator.
This contribution presents a theoretical and practical analysis of the precision of deep-indoor radio positioning using sub-GHz (ultra-high frequency) band ranging pseudolites, penetrating whole buildings and blocks. Working deep indoors, pseudolite systems face a negative signal-to-noise ratio and a challenging dynamic power range. These challenges lead to small nuances and limitations compared with global navigation satellite systems. Therefore, ranging codes, modulation techniques, and corresponding time-delay/range, clock, carrier, and signal-to-noise estimators are analyzed and discussed to demonstrate the limits of deep-indoor ranging. Then, the ranging precision is evaluated based on the Cram & eacute;r-Rao lower bound of ranging variance: its analytical development is followed by numerical computation and comparison with results from real measurement campaigns and a particular model of losses.
This study introduces a novel deep-learning-assisted Kalman filter (DLKF) for integrating global navigation satellite system (GNSS) and inertial navigation system (INS) measurements, aiming to improve navigation accuracy and robustness in GNSS-denied environments. GNSS/INS integration is critical for autonomous vehicles to maintain continuous navigation when GNSS signals are obstructed. Traditional model-based Kalman filters (MBKFs), while widely employed for GNSS/INS integration, face challenges such as system model deficiencies, incorrect noise assumptions, and complex inertial measurement unit (IMU) error characteristics, especially for micro-electromechanical system IMUs. To address these challenges, the DLKF integrates a deep neural network (DNN) with the Kalman filter, leveraging its ability to learn nonlinear system dynamics and noise statistics to provide adaptive Kalman gain and accurate IMU error estimation. Our approach further addresses practical challenges such as training with asynchronous sensor data and adapting to an error-state MBKF structure. This method also leverages DNN capabilities while retaining the interpretability of model-based filtering. In addition, we design specialized training strategies to enhance model stability and performance, particularly under GNSS outage conditions. The proposed method was evaluated on simulated three-dimensional data sets and real-world scenarios. The results demonstrate that the DLKF achieves improved navigation accuracy and reduced position errors during GNSS outages compared with the MBKF. More importantly, the proposed approach provides a consistent and interpretable framework for integrating DNNs into the well-established MBKF-based GNSS/ INS integration framework. These findings highlight the potential of combining data-driven methods with traditional model-based approaches to address the limitations of GNSS/INS integration and to provide enhanced solutions for autonomous systems in challenging environments.
