Wind Field Estimation for Small Unmanned Aerial Vehicles

Global Positioning System (GPS) spoofing is a common mean to cheat an Unmanned Aerial Vehicle (UAV) which is guided by the INS/GPS integrated navigation system. Detecting the spurious GPS signal is the precondition of anti-spoofing for UAVs. A detection system of GPS spoofing is designed based on dynamics identification. The centroid motion model and attitude motion model were established. The measurement information from inertial measurement units (IMUs) and positioning information from GPS both are visualized responses to the dynamic laws of UAVs. These two types of measurement information were separately used to estimate the dynamics parameters of UAVs online using two Extended Kalman Filters (EKFs). The detection of spurious GPS signals was realized effectively based on the monitoring of relative estimation errors. Numerical simulations demonstrated that the dynamics parameters could be estimated online by the combined filters. And the precision and accuracy of filter #1, which was based on IMU information, were both higher than those of filter #2, which was based on GPS information. When the spurious GPS signal was just zero-order continuous with the true signal at the penetration point, a pulse relative error would occur at the penetration point; when the spurious GPS signal was first-order continuous with the true signal at the penetration point, a step relative error would occur. Furthermore, relative systematic errors matched with the period of spurious signals.

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed for low-cost navigation of ground vehicles. The ANFIS structure is proposed for fusing the measurements of the Global Positioning System (GPS) and inertial measurement unit (IMU). Low-cost GPS receivers provide the position and velocity of the vehicle at a lower rate than the IMU sampled vehicle dynamics. Additionally, the GPS measurements might be missed/delayed due to receiver's inability to lock on the signal or due to obstruction from neighboring vehicle's or infrastructures. Therefore, the variable delay between the IMU and GPS signals is selected as an additional input to the ANFIS architecture. The performance of the proposed ANFIS architecture is demonstrated through experimental results. The instrumentation system used in this experiment consists of a microcontroller system, a GPS receiver, and an IMU.

An efficient state estimator is critical for the development of an autonomous plug-in hybrid electric vehicle (PHEV). To achieve effective autonomous regulation of the powertrain, the latency period and estimation error should be minimum. In this work, a novel error state extended kalman filter (ES-EKF)-based state estimator is developed to perform sensor fusion of data from light detection and ranging sensor (LIDAR), the inertial measurement unit sensor (IMU), and the global positioning system (GPS) sensors, and the estimation error is minimized to reduce latency. The estimator will provide information to an intelligent energy management system (IEMS) to regulate the powertrain for effective load sharing in the PHEV. The integration of the sensor fusion data with the vehicle model is simulated in MATLAB environment. The PHEV model is fed with the proposed state estimator output, and the response parameters of the PHEV are monitored.

This paper presents an adaptive learning method for data fusion in autonomous\ndriving vehicles. The localization is based on the integration of Inertial\nMeasurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning\nSystem (GPS) units in an adaptive Kalman filter (KF). The observability\nanalysis reveals that $i$) integration of a single GPS with IMU does not\nconstitute an observable system; $ii$) integration of two GPS units with IMU\nresults in a locally observable system provided that the line connecting two\nGPS antennas is not parallel with the vector of the measured acceleration,\ni.e., the sum of inertial and gravitational accelerations. The latter case\nmakes it possible to compensate for the error in the estimated orientation due\nto gyro drift and its bias without needing additional instruments for absolute\norientation measurements, e.g., a magnetic compass. Moreover, in order to cope\nwith the fact that GPS systems sometimes lose their signal and receive\ninaccurate position data, the self-tuning filter estimates the covariance\nmatrix associated with the GPS measurement noise. This allows the KF to\nincorporate GPS measurements in the data fusion process heavily only when the\ninformation received by GPS becomes reliably available.\n

Misalignment can be an important error source in the integration of the global positioning system (GPS) and inertial navigation systems. This paper presents car test results on the estimation of alignment errors in the integration of a low-grade inertial measurement unit (IMU) with accurate GPS measurement systems. The car test was conducted with a low-cost solid-state IMU and carrier-phase differential GPS measurement systems. Test results showed that changes in the angular velocity improve the estimation of the lever arm between the GPS antenna and IMU. They also showed that changes in acceleration improve the estimation of the relative attitude between the GPS antenna array and IMU. The lever arm was estimated with a 10-cm error. The relative attitude was estimated with a half-degree error. An iterative scheme was used to improve the estimation of the alignment errors during postprocessing. The scheme was shown to be useful when the test car could not have sufficient changes in motion due to limitations in its path. With the given set of test data, the estimation error decreased as the number of iterations increased.

Social preferences in childhood and adolescence. A large-scale experiment to estimate primary and secondary motivations

A trajectory reference system based on the Global Positioning System (GPS) can be used instead of conventional radars during missile flight tests. The high quality of the GPS-based trajectory reference makes it useful for evaluating the performance of the missiles' Inertial Measurement Units (IMUs). Such a system was installed and flight tested on two recently launched ballistic missiles. The GPS hardware configuration used on these flights is described. A Kalman filter approach is used to estimate individual IMU errors based on the GPS range and delta range data. The ability of the GPS-based system to identify IMU errors is compared to that of radar; GPS is found to provide superior estimates of the IMU errors.

The Wakita's area functions based on the linear prediction model, assume the vocal tract to be lossless. Due to this assumption, the actual non-zero losses and additional errors in estimating them affect the area functions significantly. To quantify these effects, the sensitivity of area functions to perturbations in band widths and formants is systematically studied and their relative importance is presented. To overcome the effects of errors in bandwidth estimation, a modification for the area function is proposed. The effectiveness of these modified area functions is demonstrated by a vowel recognition test using both Wakita's area functions and the modified area functions.

Consideration of the inertial dissipation method for routine wind stress estimation suggests that the most significant errors are likely to be changes in height of the airflow before reaching the anemometers, and errors in estimating the true wind, due either to flow distortion-induced errors in the relative wind estimate or errors in estimating the ship's speed relative to the water. The results from four anemometers—Solent sonic and Kaijo Denki sonic anemometers, and R.M. Young propeller-vane and bivane anemometers—mounted on the foremast of a research ship were compared. The mean bias between the four anemometers in the friction velocity estimates was less than 3% (rms scatter 6%–12%). In contrast the bias and scatter for the drag coefficient was 17%–27% due to flow distortion-induced errors in estimating the true wind speed. It is concluded that, with a reasonably well-exposed anemometer, wind stress can be determined to 5% or better by the dissipation method whereas errors in the bulk aerodynamic method are likely to be between 20% and 30%. The data from the two sonic anemometers showed the best correlation; the Solent sonic, a relatively new instrument, was comparable in performance to the Kaijo Denki. Comparisons of the two propeller anemometers typically showed twice the scatter compared to the sonic values. Overcorrection for the propeller response at low wind speeds resulted in spuriously high drag coefficient values for wind speeds below 10 m s−1. In contrast, the sonic anemometer data showed no change in the slope of the drag coefficient to wind speed relationship at low wind speed.

Information communication technology related vehicle services need to support location and the transmission of communication and traffic information between vehicles, or between vehicles and infrastructure. In particular, the technology for the measurement of the accurate location of a vehicle is dependent on location-determination technology like Global Positioning System, and this technology is very important for vehicle driving and location services. If, however, a vehicle is in a Global Positioning System radio-shadow area, neither a Global Positioning System nor a Differential Global Positioning System can accurately measure the corresponding location because of a high error rate caused by the shadowing intervention. Even an Inertial Measurement Unit could provide inaccurate location data due to sensor drift faults around corners and traffic-road speed dumps. Vehicles, therefore, need an absolute location to prevent the provision of inaccurate vehicle-location data that is due to radio-shadow areas and relational Inertial Measurement Unit positions. To achieve this, we assume that vehicle-to-infrastructure communication is possible between a vehicle and roadside unit in Vehicular Ad hoc Networks. We used iBeacon at the roadside unit and revised its Universally Unique Identifier so that it generates absolute Global Positioning System location data; that is, moving vehicles can receive absolute Global Positioning System data from the roadside unit-based iBeacon. We compared the proposed method with current Global Positioning System and Inertial Measurement Unit systems for the following two cases: one with a radio-shadow area and one without. We proved that the proposed method generates location data that are more accurate than those of the other methods.

In response to the issue of low positioning accuracy and insufficient robustness in small UAVs (unmanned aerial vehicle) caused by sensor noise and cumulative motion errors during flight in complex environments, this paper proposes a multisource, multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors, including GPS (global positioning system), an IMU (inertial measurement unit), and visual sensors, to complement the strengths and weaknesses of each hardware component, thereby mitigating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, a high-fidelity UAV model is established in the digital twin based on the real UAV parameters, providing a robust reference for data fusion. By utilizing the extended Kalman filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables the real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multisource, multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

Land vehicles rely mainly on global positioning system (GPS) to provide its position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors (accelerometers and gyroscopes) mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. Neuro ? Fuzzy modules were used to fuse data from GPS and inertial sensors in real-time. This research optimizes the operation of these modules by implementing a temporal window-based cross-validation approach during the update procedure. A field test data was used to examine the performance of the proposed method and the results discuss the merits and the limitations of the proposed technique.

Position determination using satellite navigation system has grown significantly. It provides geospatial with global coverage called GNSS (Global Navigation System Satellite). GNSS satellites consists of GLONASS, GPS (Global positioning system) and Galileo.GPS is the most commonly used system and it is known to its capability to determine 3D position on the surface of the earth. In order to determine the position, a GPS receiver must be able to receive signals from at least four GPS satellites. However, the determination of position in condensed areas such as tunnels, area surrounded by high rise buildings, highly forested and in other closely-knit areas is not achieved because satellite signals cannot reach the receiver in the above-mentioned areas and also others where the signals are reflected before being received by a GPS receiver. In this paper, we present the algorithm to fuse GPS and the inertial measurement unit (IMU) to enable positioning in the above-mentioned Condensed Areas. The standard deviations of the two measurements show that GPS-IMU is better than GPS alone, the standard deviation when satellite outages occurred is - 4.57475 for GPS-IMU measurements and 0.218675 for GPS observations. We presented the results in graphics and it shows that GPS measurements are easily disturbed by external influence such as multipath but GPS-IMU graphic is continuous and robust. The advantages and disadvantages of GPS and INS are complementary and make them work together to enable the accurate measurements in the areas mentioned above. Integration of INS and GNSS can be classified into three types, loosely coupled Kalman filter, tightly coupled Kalman filters and ultratight coupled Kalman filter. In this research we used loosely coupled Kalman filter and tightly coupled Kalman filters to combine GPS and INS in one system.

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

This paper reviews the pivotal role of Global Positioning System (GPS) and Inertial Measurement Unit (IMU) technologies in the navigation and control of Unmanned Aerial Vehicles (UAVs). GPS offers precise global positioning, while IMU provides high-frequency motion and orientation data. However, GPS signal interruptions and IMU drift pose challenges, particularly in dynamic or GPS-denied environments. This review explores the integration of GPS and low cost IMU systems through advanced sensor fusion techniques, such as Kalman filtering and machine learning, to enhance navigation reliability. Future directions, including advancements in hardware, adaptive algorithms, and swarm navigation, are discussed to address operational challenges and unlock the potential of UAVs in diverse applications.