Global Navigation Satellite Systems (GNSSs) remain the principal mean of positioning in many applications and systems, but in several types of environment, the performance of standalone receivers is degraded. Although many works show the benefits of the integration between GNSS and Inertial Navigation Systems (INSs), tightly-coupled architectures are mainly implemented in professional devices and are based on high-grade Inertial Measurement Units (IMUs). This paper investigates the performance improvements enabled by the tight integration, using low-cost sensors and a mass-market GNSS receiver. Performance is assessed through a series of tests carried out in real urban scenarios and is compared against commercial modules, operating in standalone mode or featuring loosely-coupled integrations. The paper describes the developed tight-integration algorithms with a terse mathematical model and assesses their efficacy from a practical perspective.

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

Precision Agriculture (PA) refers to applications asking for reliable and highly available precise positions, at centimeter level, in most of operational scenarios. Machinery guidance, automatic steering and controlled traffic farming enable machinery to move along repeatable tracks on the field, minimizing pass-to-pass errors and overlaps. In the recent years, satellite-based navigation has also opened the door to (semi) autonomous machineries for some specific farming scenarios and operations. Farming industry is now looking to use small robots to bring efficiencies and benefits to farms, capable of complex tasks that have not been possible with traditional large-scale agricultural machinery. Even though the state-of-the-art Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) receivers usually match the requirements posed by PA applications in open fields, propagation effects degrade the performance under foliage or with surrounding obstacles. This paper presents an experimental testbed and methodology suitable to assess the real performance of RTK GNSS-based devices in operational environments. Such testbed and methodology were effective to compare different devices, which resulted to be equivalent in open-sky conditions, but with significant differences in other types of environments. The paper also discusses opportunities and current limits of GNSS for emerging PA applications based on small robots and artificial intelligence.

The paper investigates approaches for loosely coupled GPS/INS integration. Error performance is calculated using a reference trajectory. A performance improvement can be obtained by exploiting additional map information (for example, a road boundary). A constrained solution has been developed and its performance compared with an unconstrained one. The case of GPS outages is also investigated showing how a Kalman filter that operates on the last received GPS position and velocity measurements provides a performance benefit. Results are obtained by means of simulation studies and real data.

Global Navigation Satellite Systems (GNSS) spoofing is a pernicious type of intentional interference where a GNSS receiver is fooled into tracking counterfeit signals, with the purpose of inducing a misleading information into the application it is used for. A possible technique able to identify a GNSS spoofing attack is a dual-antenna method based on the analysis of the dispersion of the double differences (D3) of carrier-phase measurements. Such technique has been recently presented as a relatively simple method to detect situations where even only a subset of counterfeit signals is tracked by the receiver. However, the D3 technique has not been analyzed in a rigorous theoretical way so far and the detection threshold was, for instance, set only empirically. Aiming at filling these gaps, this article intends to revise the main concepts of the aforementioned technique in a clear mathematical way. Thus, the detection threshold will be given according to a target probability of missed detection. Moreover, the article provides a thorough analysis of expected performance in terms of probability of missed detection and probability of false alarm, addressing them first as pairwise probability, then as overall probability. The effect of the signal C/N0 ratio on these detection performances is analyzed. Methods to reduce the occurrence of events of false alarm are also discussed. Eventually, an assessment of performance of the ${{\rm{D}}^3}$ algorithm is evaluated through a set of tests that emulate real working conditions.